Tarun Das ADB Nepal Upgraded Macronomic Model Part-1

8/9/2019 Tarun Das ADB Nepal Upgraded Macronomic Model Part-1

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Upgraded Nepal Macroeconomic Model- Test, Calibrations and Projections

UPGRADED NEPAL MACROECONOMIC MODEL

P ART -O NE

P ROJECTIONS , S IMULATIONS , MODEL RESULTS AND P OLICY CONCLUSIONS

Acknowledgements

The Modeling Team would like to express their sincere gratitude and thanks to:

Mr. Ravindra Prasad Pande, Executive Director (Research), NepalRastra Bank (NRB) and Chairman, Technical Committee on Modeling, for thestrategic guidance on modeling and economic analysis; Dr. Nephil Matangi Maskay, Director (Research), NRB and FocalOfficer, Technical Committee on Modeling, for overall supervision and day-to-day advice on modeling and research; Mr. Yubraj Acharya, ADB Project Implementation Officer; Mr. ShahidParwez, former Program/ Project Implementation Officer, Mr. Raju Tuladhar,Senior Economics Officer;.Ms. Shreejana Rajbhandari, Finance Officer and

Ms. Neelina Nakarmi, ADB Nepal Resident Mission at Kathmandu for providing all possible support for the project; Dr. Ram Sharan Kharel, Deputy Director (Research) and Secretary,Technical Committee for valuable discussions and comments on modeling; Mr. Suman Neupane, Assistant Director (Research), NRB for logisticssupport and academic help on data base and methodology; and All the Directors and experts of the concerned divisions in theResearch Department, NRB for their enthusiastic cooperation, keen interest,useful discussions and for providing relevant data and documents.

Dr. Tarun Das, Team Leader And International Macroeconomic Modeling Specialist(Formerly, Economic Adviser, Ministry of Finance, India)30 May 2010Kathmandu, Nepal

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CONTENTS

Contents Pages

Acknowledgements 2

Contents 3

1. Introduction and Scope 4-11

2. Analytical Framework and Test and Calibration Techniques 12-13

3. Specifications, Estimation Results and Baseline Scenario 14-26

4. Evaluation of the In-Sample Forecasting Performance 27

5. Policy Simulations 28-32

6. Limitations of the model 33-36

7. Concluding observations 36

8. An operational dynamic Stochastic General equilibrium (DSGE)Model for Nepal

37-44

Selected References 45-49

Annex-1: Regression Equations and Fitted Graphs(Separate Volume)

1-86

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1. Introduction and Scope

1.1 Background of the Project

The present project is a part of a wider project called Nepal: StrengtheningCapacity for Macroeconomic Analysis - TA-7165 (NEP), which is being funded bythe Asian Development Bank (ADB). The TA will enhance the Governmentscapacity for economic policy formulation based on reliable macroeconomic data andsound policy analysis. The TA has two distinct components viz. (a) to further improvethe national statistical system and (b) to develop and use macroeconomic models for policy simulations, forecasting and economic planning. While component-A of the TAis being executed by the Central Bureau of Statistics (CBS), Component-B is beingexecuted by the Nepal Rastra Bank (NRB).

Under component B, the consultants are required to upgrade the NepalMacroeconomic Model (NMEM) and to develop an operational Dynamic StochasticGeneral Equilibrium Model (DSGEM) for Nepal. This report deals with themethodology and the outputs of the upgraded NMEM developed by the consultantsduring the second phase (6 April 2010 to 31 May 2010) of the component B.

In 2005 ADB developed the Nepal Macroeconomic Model as a part of a TA for Strengthening Institutional Capacity for Public Debt Management. Apart from beingused by the Government for public debt sustainability analyses, Nepal Rasta Bank(NRB) has also been using the NMEM for economic forecasting. However, theNMEMs utility has become limited due to some major developments in the economyover the years.

Nepal has a rich tradition of conducting analytical economic research and a longhistory of developing macroeconomic models. Before discussing the modificationsneeded for the upgraded NMEM, it may be useful to provide a brief review andperspectives of the various macroeconomic models developed in Nepal by variousresearchers and economic agencies.

1.2 A brief review of the past economic modeling exercises in Nepal

Since 1980s, individual researchers had attempted to use statistical techniques andeconometric models for economic analysis and forecasting in Nepal.

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1) In 1985, a multi-sector, economy-wide model was first introduced by Khanal,Thapa and Elbers (1987) 5.

2) In 1987, the Water and Energy Commission developed a macro model for forecasting energy demand.

3) In 1989, a macro-economic model in the Leontief input-output frameworkcomprising 39 sectors was also developed to prepare long-term industrialplans and to formulate alternative development strategies for the economy 6.The basic objective of the model was to determine the optimum volume of investment under an acceptable rate of inflation, acceptable fiscal deficit,realistic level of foreign assistance, and a stipulated growth rate for per capitaconsumption.

4) A similar version of the macroeconomic model was also used in theformulation of the Eighth Plan.

5) In 1992 another macro model was developed as a part of the link model for the South Asian Association for Regional Cooperation (SAARC) countries 7 toexamine the impact of regional trade on its member economies.

6) A slightly different model in the framework of the standard flow-of-fundsaccounting, commonly used by the World Bank and the InternationalMonetary Fund, was developed for the National Planning CommissionSecretariat Project. In the model, different building blocks viz. the nationalaccounts; the budget and pubic sector account; the balance of payments(BOP) or the rest of the world account; the monetary sector account; and theprivate sector account; was integrated into a consistent accountingframework.

7) In 1990, a simple macroeconomic accounting framework was employed toexamine the possibility of policy coordination between exchange rate policyand fiscal policy for more effective management of the economy 8.

5 Khanal, D. R., P. J. Thapa, and C. Elbers. 1987. A Regionally Disaggregated PlanningModels for Nepal . Kathmandu: NPC/ESCAP/IDS/ISI.

6 Development Study Consultants. 1990. Policy Planning Models for the NepaleseEconomy with Special Reference to the Industrial Sector. Mimeo. Kathmandu:DSC/MOI/UNIDO.

7 Khanal, D. R. and G. N. Sharma. 1992. A Macroeconometric Model of Nepal in SAARCLink. Nagvi, S.N.H. and S.A. Samad, eds. New Delhi: Oxford and IBH Publishing Co. Pvt.Ltd.

8 Cruikshank, E.D. and R.H. Nord. 1990. The Role of Exchange Rate Policy: The Case of

Nepal . Mimeo. Kathmandu.

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8) For the formulation of the Tenth Plan, the integrated macro and input-outputmodel was used to set the growth rates and the investment levels at bothsectoral and aggregate levels without disrupting macroeconomic stability.

9) Special attention should be drawn to two other macroeconomic models:

Alamgir and Ra (2001) 9 and Sharma (1989). The Alamgir and Ra (2001)model projects GDP growth by major economic sectors on the basis of growthelasticity derived from historical data and adjusted for structural changes. Acommon feature of the two models is that GDP is predetermined or stipulatedbefore estimating individual equations. These models implicitly assume thatthe supply side determines aggregate output, and they predict eachendogenous variable in a way that is consistent with the given output level.

10) As mentioned above, there had been recent attempts under technicalassistance by ADB to develop a medium size Macroeconomic Model (MEM).A part of TA for Strengthening Institutional Capacity for Public DebtManagement, ADB (Ra and Rhee 2005) supported the Government indeveloping a MEM in 2005 known as Nepal Macroeconomic Model (NMEM) on the basis of the standard Keynesian incomeexpenditure approach inwhich gross domestic product (GDP) is determined endogenously.

11) Apart from being used by the Government for debt sustainability analyses,Nepal Rasta Bank (NRB) has also been using the NMEM for economicforecasting.

12)Subsequently to that Khanal and Kane (2005) had utilized a MEM to analyzereduction of domestic poverty, and

13)Bhattarai (2007) had produced an applied dynamic general equilibrium modelfor Nepal.

14)In recent years Nepal Rastra Bank has engaged itself actively for developingits own MEM which would be consistent with the vision and mission of theBank. The model was developed by the Macroeconomic Modeling Unit (MMU)of the NRB (2007), Research Departments Economic Analysis Division as apart of its macroeconomic modeling activities.

From the above review it follows that the state of the macroeconomic modeling inNepal is still at an experimental and learning stage. There are a few examples of macroeconomic modeling for the purpose of policy planning and formulation of economic plan. But these were not done on a sustainable, comprehensive andcontinual basis. There was also lack of adequate capacity building in the respectiveagencies.

Besides, the utility of NMEM has become limited due to major structural changes inthe Nepals economy, and major revisions of key macroeconomic data (such as

9 Alamgir, M., and S. Ra. 2001. Nepal: Debt Sustainability and Country Risk

Analysis . Manila: ADB.

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National Accounts, Price Statistics, Government Finance Statistics and the Balanceof Payments over the years) due to changes in base period and basic concepts.Therefore, the NMEM developed by ADB needs to be reviewed and upgraded. Inaddition, other econometric models such as the Dynamic Stochastic GeneralEquilibrium (DSGE) model need to be developed to examine the relative merits and

demerits of both these models.

Considerable efforts are, therefore, required for the development of new modelingtechniques with a focus on the direct and practical policy applications by the NepalRastra Bank, Ministry of Finance and the National Planning Commission (NPC).

The basic purpose of the present technical assistance of the ADB is to remove thesedeficiencies in macroeconomic modeling and to strengthen technical capability in theNRB, MOF, NPC and CBS for model building and its use for policy planning

1.3 Updating the Nepal Macroeconomic Model developed by ADB

One of the basic tasks of this project funded by the ADB is to upgrade the NepalMacroeconomic Model (NMEM) initially developed by ADB in 2005 as a part of TAfor Strengthening Institutional Capacity for Public Debt Management. Apart frombeing used by the Government for public debt sustainability analyses, Nepal RastaBank (NRB) has also been using the NMEM for economic forecasting. However, theNMEMs utility has become limited due to some major developments in recent years.

The major characteristics of the NMEM are:

(a) The NMEM is a basically Keynesian income-expenditure model in whichthe demand side determines GDP. The supply side is not explicitlyspecified as the aggregate production function is not estimated.

(b) Its basic features are drawn from the Korea Development Institute (KDI)macro model that had been used to design successive 5-year economicdevelopment plans in Korea. Due to the lack of quarterly data in Nepal theNMEM is an annual model while the KDI used a quarterly macro-econometric model.

(c) The NMEM is a mid-sized model with 59 variables (20 behavioral

equations, 17 identities and 22 exogenous variables). The NMEMdistinguishes policy variables from simple exogenous variables. Whilegovernment has some control over the policy variables, exogenousvariables are determined by non-economic forces or foreign sectors.Taxes, regular and development expenditures, foreign borrowing, and theexchange rate were taken as policy variables in the NMEM.

(d) Given the peg of the Nepalese rupee to the Indian Rupee, monetaryvariables were not considered as policy variables.

(e) The NMM attempted to incorporate Nepals strong economic ties with Indiain international transactions although economic relationships with other

countries have become important over the years.

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(f) The impact of the exchange rate on trade in Nepal was quite limited sincethe Nepalese rupee is pegged to the Indian rupee and India is the major trading partner of Nepal.

The Nepal Macro-economic Model (NMEM) was tested and calibrated on the basisof time series data for the sample period from FY1975 to FY2004. Then the modelwas simulated to carry out alternative scenario analysis for formulation of the TenthPlan. The model was also calibrated to measure the economic costs of conflict interms of the effect of declining development expenditures on economic growth (Ra,Sungsup and Bipul Singh 2005).

There were 5 building blocks in the NMEM viz. final demand, prices, credit andmoney, government, and the balance of payments blocks. Final demand is the sumof private consumption, government consumption, private fixed investment,

government fixed investment, increase in stock or inventory, and net exports. InNMEM, development expenditure is a determinant for the private fixed investment,public fixed investment and public consumption.

1.4 Limitations of the Nepal Macroeconomic Model

In brief, the initial NMEM considers only the demand side of GDP, but does notconsider the supply side of the real sectors, which put basic constraints on thegrowth and development of a developing country like Nepal. In addition, monetary,financial and price blocks, government block, and balance of payments block were

not fully developed and did not consider the supply side and the role of expectationsof economic agents. The model also needs to be updated on the basis of new dataavailable for the national accounts, government finance and balance of payments.

The utility of NMEM in the national policy analysis has been limited due to thefollowing reasons:

a) The structure of Nepals economy has changed significantly. Agriculturescontribution to GDP has declined fast whereas the services sectors contributionhas grown rapidly compared to other sectors of non-agriculture sectors. It isinteresting to note that the industrys share of GDP has remained more or lessunchanged. The national accounts also underestimate certain incomes, such asmigrant workers remittances, which have grown sharply since 2004.b) There have been some major revisions of basic concepts and standards of key macroeconomic variables such as national accounts, government finance,balance of payments and prices since the NMEM was developed. c) In view of these changes, it was felt necessary to upgrade the NMEM for analytical work and macroeconomic forecasting.

To take care of these limitations, we have upgraded the model. While doing so, due

consideration has been given to the availability new data base, up-to-date statisticaltest and calibration techniques, computer algorithms, and available hardware and

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software capabilities, and technical manpower in the NRB, MOF and other budgetaryagencies which are likely to use the updated NMEM for budgeting, planning andmonetary and financial programming.

In the second part of this report, we describe in details the analytical structure, modelstructures, data base, test and calibrations techniques of the upgraded modeldeveloped by us in the second phase (April-May 2010) of the project. So these arenot repeated here. We summarize only the essential changes of the earlier NMEM.

1.5 Improvements Made in the Upgraded Model

Table-1.1 summarizes the limitations of the ADB NMEM and the steps taken by us inthe Upgraded NMEM to rectify these limitations. Table-1.2 presents the differences

of basic features between ADB NMEM and Upgraded NMEM in a tabular form. Inbrief, as mentioned earlier, the earlier NMEM ignored the role of supply for the realsectors and money and credits. Upgraded model has specific blocks for the supplysides of both GDP and money.

In addition, the upgraded model has fully developed the government finance andbalance of payments blocks which were very weak in the NMEM. For all the sub-models, the upgraded model is more detailed than the ADB NMEM and uses moreup-to-date data on national accounts, BOP and government finance on the basis of revised base or revised IMF concepts, which were not available at the time of

building NMEM more than five years ago.

Upgraded NMEM is much larger than NMEM. For example, the NMEM consisted of only 59 variables, out of which 37 were endogenous (comprising 20 behavioralequations and 17 identities) and 22 were exogenous. On contrast, the upgradedmodel comprises 151 variables 10, of which 139 variables are treated as endogenousand determined within the sub-models, and only 12 variables are treated asexogenous and determined outside the model. Out of 139 endogenous variables, 92variables will be determined by equations and the residual 47 variables are identitiesto ensure consistency and equilibrium within the sub-models.

10 In this report at other sections, there may be inconsistencies regarding the number of variables either

exogenous or endogenous. Although origanaly basic data were collected for 151 variables, many variables weregenerated in the process of test and calibrations depending on the nature of equations. Therefore, the ultinamenumber of variables exceeds 151.

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Table-1.1

Limitations of the ADB NMEM and Proposed Upgradation

Limitations of the earlier NepalMacroeconomic Model

Steps taken to rectify the limitationsin the upgraded model

a) Model considers only the demandside of GDP, but does not consider the supply side of the real sectors.

Supply side of GDP is estimated by sector wiseGDP at current and constant prices with the help of econometric time trends as well as by fitting Cobb-Douglas type production functions.

b) Money and credit blocks were notfully developed and NMEM did notconsider the supply side of moneyand bank finances.

Supplis of different components of money areprojected by econometric relations.

c) The Government block was theweakest block with only equation for the non-tax revenue and all other variables were defined by simpleaccounting identities.

Government finance block is fully developed withseparate equations for different components of expenditure and revenues.

d) Similarly, balance of payments blockwere not well developed. Itconsidered only merchandiseexports and merchandised importsbut did not endogenize the other components in the current account(such as services and remittances).In the capital account, loans andamortization was endogenized, butthe underlying econometric relationsare less convincing.

BOP block is fully developed by endogenizingdifferent components in the current and capitalaccounts such as services, income, remittances, FDIinflows etc.

e) There had been structural changesin the economy and also changes inthe basic concepts of data for different blocks. So the model needsto be updated and upgraded on the

basis of new data available for thenational accounts and balance of payments.

The model is also being updated on the basis of data for the period 1975-2009 incorporating newdata available for the national accounts,government finance and balance of payments,and to take care of structural change of theeconomy over the years..

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Table-1.2

Differences of Basic Features between ADB NMEM and Upgraded NMEM

Basic characteristics of the earlier Nepal Macroeconomic Model

Basic characteristics of the present upgradedNepal Macroeconomic Model

a) The NMEM is a basicallyKeynesian income-expendituremodel in which the demand sidedetermines GDP.

The upgraded NMEM considers both demand andsupply side of real sector, money and externalsectors and attempts to provide partial equilibriumin the macroeconomic consistency accountingframework.

b) The supply side is not explicitlyspecified as the aggregateproduction function is notestimated.

Supply sides of sectoral GDP, money supply andexternal sectors are specifically considered in theupgraded model.

c) There are 5 building blocks in theNMEM viz. final demand, prices,credit and money, government, andthe balance of payments blocks.Final demand is the sum of privateconsumption, governmentconsumption, private fixedinvestment, government fixedinvestment, increase in stock or inventory, and net exports. InNMEM, development expenditureis a determinant for the privatefixed investment, public fixedinvestment and publicconsumption.

There are also five blocks in the upgraded modelviz. (a) supply and demand for real sectors andNational accounts; (b) supply and demand for money and credits; (c) government finance withdetailed projections for major components of expenditure and revenues; (d) balance of payments with detailed projections of major components in the current account and the capitaland financial accounts; (e) prices, interest ratesad exchange rates.

d) The NMEM is a mid-sized modelwith 37 equations. A medium-sizedmodel has an advantage in policyanalysis as it incorporates moredetail of the structure of theeconomy.

The upgraded NMEM is a large sized model withmore than 150 variables. It will provide moreflexibility for the policy makers to analyze theimpact of many variables on the fiscal, monetary,BOP and overall economic outlook.

e) The NMEM consisted of 59variables (20 behavioral equations,17 identities and 22 exogenous).The NMEM distinguishes policyvariables, on which governmenthas some control, from simpleexogenous variables which aredetermined by non-economicforces or foreign sectors. Taxes,regular and developmentexpenditures, foreign borrowing,and the exchange rate were takenas policy variables in the NMEM.

The upgraded model comprises 167 variables of which 62 variables are endogenous and 38variables are exogenous. There are 67 identitiesto ensure consistencies and equilibrium within the

system.

f) The impact of the exchange rate inthe NMEM is quite limited as theNepalese rupee is pegged to theIndian rupee and India is the major

trading partner of Nepal.

The upgraded NMEM makes an attempt tocapture the influence of the exchange rate on theNepalese trade and commerce.

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2. Analytical Structure and Test and Calibration Techniques

Part-2 of this report describes in details the analytical framework, model structure, database, test and calibration techniques and computer algorithms for the model. So theseare not discussed here to avoid duplication. Basically we use a macroeconomic

accounting model ( see Box-1.1 Figure-1.2) consistent with the general macroeconomicframeworks of the UN System of National Accounts (UN-SNA) and the IMF Manuals andFrameworks on the Government Finance Statistics (GFS), Balance of Payments (BOP)Statistics and Monetary and Financial Statistics (MFS). Basically, it is a Consistent Macroeconomic A ccounting Model and not an optimizing model as it does not have anyoptimizing or objective functions. It ensures only partial equilibrium in the variables given thefitted equations. But, technically there could be another set of equations leading to another set of results in equilibrium. We are not sure which set would be better unless we evaluatethe results according to some objective functions and constraints.

We use the classical or the ordinary least squares method for the test and calibration of theequations. We satisfy all the conditions (except the complete test of stationarity of variables)for the use of classical least squares. However, all the fitted equations are observed to begood fits as judged by the standard goodness of fit statistics such as R-bar-square, t-statistics, F-statistic, Durbin Watson statistic, Mean Absolute Percentage Error, TheilInequality Index, mean bias, variance bias and covariance bias.

The estimated regression results along with the standard goodness of fit statisticsand the graphs with confidence limits are presented in Annex-1 of this report (Part-1).

Components of sub-models are projected by regression equations, while aggregates areestimated by identities or derived equations to ensure consistency and equilibrium amongthe variables.

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Box-1.1: Basic Structures of Macroeconomic Sub-models and Inter-linkages

13

REAL SECTORNational Accounts

GDP at current market prices Private consumption General govt. consumption

General govt. investment Private investment Exports of goods and

Non-factor services Imports of goods and

Non-factor services

CENTRAL GOVERNMENT Revenues

Taxes and non-taxes GrantsExpenditures Current Capital

Overall balanceFinancing

Domestic financing (net) Banking system Nonbanking sector

External financing (net)

Balance of PaymentsCURRENT ACCOUNT Exports of goods and

Non-factor services Imports of goods and

Non-factor services Factor services (net) Transfers (net)

Official Private

MONETARY SECTORMonetary Authorities Net Foreign AssetsNet domestic assets: Net credit to central

govt. Credit to banks other items (net)

Reserve money

CAPITAL ACCOUNTDirect investmentMedium/long-term

Capital (net)Short-term capital (net)Overall balanceChange in net foreign

assets

Deposit Money Banks Banks' reserves Net Foreign Assets

Net domestic assets: Net credit to central

govt. Credit to private sector other items (net)

Liabilities to monetaryauthorities

Private sector deposits

Prices, Inflation, Exchange Rate,Deposit and Lending Rates


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3. Specifications, Estimation Results and Baseline Scenario

As in the case of the ADB NMEM, the upgraded NMEM consists of 5 building blocks viz.real sectors, BOP, Government Finance, Monetary and Fiscal Sectors; and Prices,Inflation, interest and exchange rates (see Box-1.1 ). Given the estimated behavioral

equations ( presented in Annex-1 of Part-1) and accounting identities, the model isintegrated in a systems framework, and computer algorithms are generated throughsuccessive iterations and interactions among all variables in all blocks until uniquesolutions are reached for all variables within a given degree of convergence. This iscalled the baseline solution 11 for the projection period 2010-2015. The projected growthrates of selected variables in five blocks and their ratios to GDP at current market pricesare presented in Tables-3.1 to Table-3.5.

For an alternative scenario and simulation exercise, an external shock or policy shock isintroduced to the baseline solution. The shock initially disturbs a specific relation andthen affects the overall economy through the continuous feedbacks and successive

iterations across the five building blocks. All the variables go through a dynamicadjustment process until the economy finds a new equilibrium. The estimated regressionequations for each block and forecasts are described briefly in the following paragraphs.

3.1 Real Sector Block

We consider both supply and demand of the real sectors. On the supply side, real GDPat constant 2000/01 basic prices is estimated and projected for three broad sectors-agriculture, industry and services. In addition to AR(1) model, agricultural and industrialreal GDP are estimated by Cobb-Douglas type production functions with respectiveinvestment and employment as the factors influencing production; real FISM isestimated by autoregressive model and time dummy for the year 1985 (before whichFISM equaled zero) and service GDP is estimated by government expenditure.

Nominal GDP at current prices is projected for nine sub-sectors viz. agriculture, mining.manufacturing, public utilities, construction, trade, transport and communications,financial sector and real estate, and social sectors (comprising health, education, publicadministration, community, personal and religious services). Besides autoregressiveAR(1) model, nominal agricultural, construction, mining, utility and social sector GDP areto government expenditure; financial and real sector, manufacturing, trade and transportGDP are estimated by Cobb-Douglas production functions; FISM by intercept and slopetrend dummies for 1985, and indirect taxes less subsidies by autoregressive model.Sectoral employment and investment generally follow autoregressive AR(1) models,while agriculture, mining. Utility, financial, construction and social sector investrmentdepend on government capital expenditure in addition to AR(1) model.

As both real and nominal GDP are estimated for three broad sectors, the GDP deflatorsare then derived for agriculture, industry and services. The nominal GDP for a sub-sector is deflated by the corresponding GDP deflator to project the real GDP for ninesub-sectors. The projected real growth rates for nine sub-sectors for the years 2010-2015 ( presented in Table-3.1-A and Table-3.1-B) appear to be realistic given thetrends in the immediate past .

Baseline projections Indicate that the real GDP growth rate is likely to range around 4.4

percent in 2010-2015 supported by agricultural growth around 3.2 percent, industrial11 Baseline solution was generated after about 5000 iterations of equations.

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growth around 4.1 percent and services around 5.3 percent compared with the secular growth rate around 4.1 percent during 1975-2009 aided by agricultural growth at 4.1percent, industry at 6.2 percent and services at 5.8 percent.

Final demand of nominal GDP is defined as the sum of private consumption,

government consumption, private fixed investment, government fixed investment,increase in stock or inventory, exports of goods and services less imports of goods andservices, and statistical discrepancies. In addition to autoregressive model AR(1) insome cases, private consumption is specified as a function of the disposable income,government consumption depends on current expenditure by the government,government fixed investment is related to capital expenditure by the government, privatefixed expenditure is related to disposable income and average lending rate.

Exports and imports of goods and services are obtained from the BOP sub-model by theidentities (XGS=Merchandise Exports+ Services Credit and MGS=MerchandiseImports+ Services Debit). Stocks are treated as the residual or the balancing factor to

establish consistency between supply side and demand side of nominal GDP.The projected growth rates of various components of nominal GDP and their corresponding percentages to GDP at current market prices ( presented in Table-3.1-Cand Table-3.1-D) appear to be realistic given the trends in the immediate past .

3.2 Prices, Interest and Exchange Rate Block

The price and interest block consists of the CPI, GDP deflators, savings and depositrates. CPI is estimated by relating it to real GDP, currency in circulation and Indian CPI.Indian CPI is treated as exogenous and estimated by autoregressive model. Currency

also follows an autoregressive model. Annual inflation in terms of CPI equals the rate of change of CPI.

GDP deflators for three broad sectors (agriculture, industry and services) and overallGDP are derived by the relation Deflator=100* Nominal GDP/ Real GDP. Rate of changeof GDP deflator implies inflation in terms of the GDP deflator.

While saving deposit rate depends on AR(1) and nominal GDP, one year fixed depositrate follows an AR(1) model. Average lending rate is derived by taking the average of lending rates for agriculture, industry, commerce and exports. While agriculture lendingrate depends on saving deposit rate, agriculture GDP and AR(1); commercial lending

rate dependson AR(1) and saving deposit rate; exports lending rate depends on AR(1),exports and savings deposit rate; and industrial lending rate depends on one year fixeddeposit rate and industrial GDp.

The projected inflation rates in terms of CPI and four GDP deflators and the projectedlending rates, bank rate, savings and fixed deposit rates, exchange rate of NepaleseRupee in terms of US dollar ( presented in Table-3.2-A and Table-3.2-B) appear to berealistic given the trends in the immediate past . Although projected average CPI inflationrate (9.1%) for the period 2010-2015 is higher than long run average (8.3%), there is adistinct deceleration of all inflation, deposit and lending rates over the projection period92010-2015) as compared with the rates in the immediate past (2008-2009).

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Table-3.1-A Baseline: Growth rates of real GDP (%)

Year GDP AGRNON-AGR IND MANF MIN Utility

2000 6.00 4.89 6.68 8.75 6.86 4.08 21.57

2001 4.78 5.48 4.36 3.21 26.39 10.53 -0.332002 0.16 3.08 -1.60 0.86 -6.38 12.71 13.602003 3.77 3.33 4.05 3.09 -1.14 3.29 19.652004 4.41 4.81 4.16 1.43 1.72 2.86 0.102005 2.85 3.50 2.45 3.01 2.21 4.01 -1.772006 4.11 1.78 5.60 4.47 2.83 10.03 -5.472007 2.74 0.97 3.83 3.94 3.12 3.10 3.872008 5.26 4.71 5.59 1.85 -0.32 5.01 0.432009 3.80 2.17 4.77 1.78 1.32 4.73 -9.072010 4.23 2.57 5.19 4.70 4.95 4.35 -1.782011 4.43 3.27 5.08 4.37 4.15 3.44 3.352012 4.44 3.43 5.00 4.11 3.80 2.98 4.052013 4.41 3.41 4.95 3.93 3.67 2.74 4.182014 4.36 3.34 4.92 3.79 3.65 2.62 4.132015 4.33 3.26 4.90 3.68 3.67 2.53 4.02

Av75-09 4.09 2.80 5.23 6.19 5.99 8.95 10.48Av10-15 4.37 3.21 5.01 4.10 3.98 3.11 2.99

Table-3.1-B Baseline: Growth rates of real GDP (%)

Year

CONS

T

Servic

e

Trade Trans FIN Social FISM

2000 8.59 5.68 3.41 12.87 5.38 3.13 2.74

2001-

24.445.31 39.65 -

18.14-3.29 -8.76 13.36

20027.41 -1.78 -

11.617.18 0.48 7.88 10.66

2003 3.14 3.68 1.24 7.54 0.19 8.40 -6.622004 1.81 6.82 10.66 12.52 0.57 2.90 30.13

2005 4.57 2.53 -7.39 2.06 14.05 7.61 6.232006 7.82 6.45 2.97 9.44 12.99 2.05 11.202007 4.13 4.49 -4.00 4.07 11.56 8.63 12.41

2008 5.29 7.00 10.50 2.91 1.94 12.08 6.48

2009 4.84 5.85 3.38 14.32 0.84 7.84 5.50

20104.93 5.32 25.38 -

13.191.24 0.81 4.83

2011 4.58 5.28 6.79 5.14 4.98 3.47 4.65

2012 4.26 5.24 6.72 5.32 4.83 3.39 4.492013 3.95 5.22 6.67 5.46 4.72 3.32 4.332014 3.67 5.21 6.63 5.57 4.62 3.27 4.19

2015 3.42 5.20 6.61 5.67 4.53 3.22 4.05

Av75-09 6.21 5.78 8.95 5.95 4.95 5.55 37.46

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Av10-15 4.14 5.25 9.80 2.33 4.15 2.91 4.42Notes: AV75-09 stands for average for the sample period 1975-2009and AV10-15 stands for average for the projection period 2010-2015 .

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Table-3.1-C Baseline: Growth rates of GDP demand at current prices (%) Year YCMP C GC PC INV GFCF GFCF-

PUB2000 9.30 8.95 11.25 8.68 31.70 12.34 10.672001 18.10 21.16 5.36 23.02 6.91 15.58 -31.67

2002 4.06 6.62 7.83 6.50 -5.70 6.06 -3.452003 7.14 8.24 10.54 8.00 13.29 9.10 -15.602004 9.04 5.24 8.78 4.87 24.95 11.33 1.602005 9.81 10.05 13.05 9.73 18.41 7.66 15.102006 10.97 14.20 8.28 14.86 12.65 15.31 1.722007 10.66 10.24 17.70 9.45 16.62 9.51 19.042008 13.26 10.73 21.89 9.46 27.02 16.39 22.442009 15.72 21.55 31.34 20.31 9.62 18.08 56.062010 17.21 17.45 12.27 18.16 19.51 18.40 18.032011 13.58 14.20 12.74 14.39 16.34 15.04 12.38

2012 13.20 13.48 13.03 13.54 17.27 14.33 9.932013 12.94 13.05 13.19 13.03 17.46 13.94 8.872014 12.75 12.78 13.28 12.72 17.31 13.73 8.452015 12.61 12.61 13.32 12.52 17.00 13.62 8.31

Av75-09 12.74 12.90 14.51 12.79 15.64 14.59 15.03Av10-15 13.72 13.93 12.97 14.06 17.48 14.84 11.00

Table-3.1.-C Baseline: Growth rates of GDP demand at current prices (%) Year GFCF-

PUBGFCF-

PVT YSTO

CK YXGS YMGS YGNDI YGNI

2000 10.67 13.31 295.41

14.29 27.54 17.91 9.45

2001 -31.67

42.23 -26.66

11.53 13.20 17.98 17.81

2002 -3.45 8.64 -77.46

-18.19

-10.80

3.58 3.52

2003 -15.60

15.05 133.37

-5.17 7.34 7.60 7.13

2004 1.60 13.04 207.70

15.87 12.55 9.32 8.85

2005 15.10 6.47 70.60 -4.00 9.87 11.10 10.462006 1.72 17.64 4.51 2.32 17.88 14.00 11.502007 19.04 8.10 40.67 6.38 12.73 9.55 10.952008 22.44 15.40 54.98 11.37 17.50 17.48 13.202009 56.06 11.50 -7.12 17.78 26.26 19.73 16.032010 18.03 18.49 22.32 12.32 18.00 16.39 16.942011 12.38 15.68 19.50 8.07 15.62 13.58 13.402012 9.93 15.37 24.14 7.01 15.40 13.30 13.052013 8.87 15.08 25.06 6.40 15.18 13.12 12.812014 8.45 14.85 24.33 6.04 14.99 13.00 12.652015 8.31 14.68 23.07 5.81 14.83 12.92 12.53

Av75-09 15.03 15.15 47.48 15.07 16.59 13.45 12.78

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Av10-15 11.00 15.69 23.07 7.61 15.67 13.72 13.56Notes: AV75-09 stands for average for the sample period 1975-2009and AV10-15 stands for average for the projection period 2010-2015 .

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Table-3.1-D Baseline: As % of GDP at current market prices Year YCMP C GC PC INV GFCF GFCF-

PUB2000 100.0

086.11 9.08 77.02 24.68 19.61 7.07

2001 100.00 88.34 8.11 80.23 22.34 19.20 4.092002 100.0

090.51 8.40 82.11 20.25 19.56 3.80

2003 100.00

91.44 8.67 82.77 21.41 19.92 2.99

2004 100.00

88.25 8.64 79.61 24.53 20.34 2.79

2005 100.00

88.44 8.90 79.55 26.45 19.94 2.92

2006 100.00

91.02 8.68 82.33 26.85 20.72 2.68

2007 100.00

90.67 9.24 81.44 28.30 20.51 2.88

2008 100.00

88.64 9.94 78.71 31.74 21.07 3.11

2009 100.00

93.11 11.28 81.83 30.06 21.50 4.20

2010 100.00

93.30 10.81 82.49 30.65 21.72 4.23

2011 100.00

93.81 10.73 83.08 31.40 22.00 4.18

2012 100.00

94.04 10.71 83.33 32.53 22.22 4.06

2013 100.00

94.13 10.73 83.40 33.83 22.42 3.92

2014 100.00

94.16 10.78 83.37 35.20 22.61 3.77

2015 100.00

94.15 10.85 83.30 36.57 22.81 3.62

Av75-09 100.00

88.64 8.73 79.91 21.67 18.81 5.84

Av10-15 100.00

93.93 10.77 83.16 33.36 22.30 3.96

Notes: AV75-09 stands for average for the sample period 1975-2009

and AV10-15 stands for average for the projection period 2010-2015

Table-3.1-D Baseline: As % of GDP at current market prices Year GFCF-

PUBGFCF-

PVT YSTO

CK YXGS YMGS YGNDI YGNI

2000 7.07 12.54 5.07 23.89 34.68 115.36

100.63

2001 4.09 15.10 3.15 22.56 33.24 115.24

100.39

2002 3.80 15.77 0.68 17.74 28.49 114.71

99.87

2003 2.99 16.93 1.48 15.70 28.55 115.21

99.86

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2004 2.79 17.55 4.19 16.68 29.46 115.50

99.69

2005 2.92 17.02 6.51 14.58 29.48 116.85

100.28

2006 2.68 18.04 6.13 13.45 31.32 120.04

100.76

2007 2.88 17.63 7.79 12.93 31.90 118.85

101.03

2008 3.11 17.96 10.66 12.71 33.09 123.27

100.97

2009 4.20 17.30 8.56 12.94 36.11 127.54

101.24

2010 4.23 17.49 8.93 12.40 36.35 126.64

101.00

2011 4.18 17.82 9.40 11.80 37.00 126.64

100.84

2012 4.06 18.16 10.31 11.15 37.72 126.7

5

100.7

12013 3.92 18.50 11.41 10.51 38.47 126.9

6100.6

02014 3.77 18.85 12.58 9.88 39.23 127.2

4100.5

12015 3.62 19.19 13.75 9.28 40.00 127.5

8100.4

3Av75-09 5.84 12.97 2.86 15.15 25.46 108.5

2100.3

6Av10-15 3.96 18.33 11.06 10.84 38.13 126.9

7100.6

8Notes: AV75-09 stands for average for the sample period 1975-2009and AV10-15 stands for average for the projection period 2010-2015

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Table-3.2-A Baseline: Annual Inflation Rates in Terms of CPI and Deflators (%)CPI CPIINDI

ANDFYF

CDFYA

GRDFYIN

DDFYSE

R

2000 3.37 4.01 4.62 4.52 3.50 5.52

2001 10.76 3.85 10.98 1.66 -9.42 30.852002 -4.82 4.15 3.94 3.53 4.94 3.802003 4.79 3.98 3.07 0.69 4.07 4.702004 -2.08 3.63 4.18 2.77 5.52 4.502005 10.97 4.28 6.51 3.50 5.38 8.682006 7.97 5.09 7.02 4.33 3.65 9.022007 6.41 6.50 7.70 6.11 5.75 8.472008 7.69 8.14 6.32 7.23 7.49 5.222009 13.24 10.83 12.40 14.87 9.15 11.252010 10.69 8.64 11.34 12.85 6.00 11.09

2011 8.91 8.58 8.77 10.22 6.69 8.092012 8.84 8.53 8.35 8.99 6.92 8.022013 8.80 8.49 8.10 8.40 6.92 7.922014 8.76 8.44 7.94 8.14 6.82 7.822015 8.72 8.41 7.83 8.06 6.68 7.71

Av75-09 8.33 7.42 8.22 7.27 8.37 9.70Av10-15 9.12 8.51 8.72 9.44 6.67 8.44

Table-3.2-B Baseline: Trends of Bank rate, deposit rate, lending rateAnd exchange rates (%)

BankRate DR1YR DRSAV LRAVG EXRATE$ LRAGR LRCOM2000 7.5 6.88 5.25 12.56 69.07 13.3 12.82001 7.5 6.13 4.75 11.44 73.83 13.5 11.52002 6.5 5.25 4.38 11.13 76.88 13.0 11.32003 5.5 5.00 4.25 10.94 77.79 12.5 11.82004 5.5 4.25 3.50 10.56 73.79 11.8 11.82005 5.5 3.63 3.38 10.34 71.06 11.5 11.02006 6.25 3.63 3.50 10.31 72.32 11.3 11.02007 6.25 3.63 3.50 9.63 70.49 11.3 8.32008 6.5 4.25 4.25 9.13 65.02 10.0 8.32009 6.5 5.95 4.75 10.31 76.88 10.8 11.02010 6.2 5.89 4.60 11.37 77.53 12.5 11.22011 6.1 5.84 4.49 11.63 76.49 13.3 11.22012 5.9 5.79 4.39 11.71 75.18 13.8 11.22013 5.7 5.74 4.28 11.72 73.72 14.0 11.12014 5.5 5.69 4.17 11.70 72.18 14.1 10.92015 5.4 5.65 4.06 11.66 70.60 14.3 10.8

Av75-09 11.22 9.65 7.10 13.76 42.36 13.3 12.8Av10-15 5.80 5.77 4.33 11.63 74.28 13.5 11.5

Notes: AV75-09 stands for average for the sample period 1975-2009and AV10-15 stands for average for the projection period 2010-2015

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3.3 BOP Sub-model

Like the real sector sub-model, the BOP sub model in the upgraded NMEM is detailedand takes care of many items in the current and capital and financial accounts. Exportsdepend on economic size represented by GDP, exchange rate and AR(1); imports on

GDP and AR(1). While grants, remittance, other transfers, FDI, credits and debits of income follow AR(1) models, credits and debits of services are related to service GDPand AR(1). Amortization depends on disbursement of foreign loans and AR(1), and BOPforeign loans and government loans in the government finance appear to be highlyrelated to each other. Other flows in financial account depend on economic sizerepresented by GDP and deposit rates in addition to follow an AR(1) model.

The projected ratios of different items of BOP sub-model to GDP at current marketprices are presented in Tables-3.3-A and Table-3.3-B. As compared with the pasttrends, the projected ratios appear to be realistic.

Goods balance is likely to deteriorate due to acceleration of import growth anddeceleration of exports growth. However, services and income balances are likely toimprove and the net transfer is also likely to improve due to buoyant growth of remittances and normal growth of grants and other transfers. But the improvements inservices, incomes and transfers are not sufficient to compensate for widening of goodsdeficit. Consequently the current account balance is likely to deteriorate from a projectedsurplus of 2.69 percent in 2010 to a deficit of 3.14 percent in 2015.

The capital and financial account is also likely to deteriorate from a marginal deficit of 0.2percent of GDP in 2010 to significant deficit of 1.5 percent in 2015 due to significantgrowth of amortization and deterioration in other financial transactions and noimprovement in FDI and portfolio investment inflows.

The combined impact of deterioration in both current and capital account will lead todeterioration in the overall BOP balance from a surplus of 2.49% of GDP in 2010 to adeficit of 4.66% of GDP in 2015. So there will be significant pressures on foreignexchange reserves unless appropriate policies are taken by the government and themonetary authority to enhance exports, remittance and tourism income in currentaccount and non-debt creating financial flows in the capital and financial account.

3.4 Money Sub-model

Money Demand is estimated by net foreign assets (NFA) and net domestic assets (NDA)comprising credits (sub-divided into government, public enterprises and private sredits)and other items. In addition to lagged values, NFA depends on BOP balance,government credits on fiscal balance, public credits on lending rates, private credits oncommercial lending rates and economic activities represented by GDP while other itemsare taken as residual or balancing item to ensure consistency with money supply.

Within money supply, currency follows an autoregressive model, while both demand andfixed deposits depend on overall income represented by GDP and lagged values.

The projected growth rates of different items of money supply and money demand for the period 2010-2015 and the corresponding ratios to GDP at current market prices are

presented in Tables 3.4-A to 3.4-D appear to be realistic.

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Table-3.3-A Baseline: BOP items as percentage of nominal GDP (%)

YearGB EXP Imp Incom

eServic

e Transf

erGrant

s

2000 -13.98 15.65 29.62 0.63 3.19 14.73 3.44

2001 -12.79 15.81 28.59 0.39 2.11 14.86 2.732002 -11.61 12.62 24.23 -0.13 0.86 14.84 2.752003 -14.28 10.31 24.59 -0.14 1.43 15.35 2.812004 -14.47 10.29 24.76 -0.31 1.69 15.82 3.642005 -14.55 10.17 24.72 0.28 -0.35 16.58 3.582006 -16.83 9.40 26.23 0.76 -1.04 19.29 2.882007 -17.82 8.50 26.31 1.03 -1.16 17.82 2.522008 -19.03 7.56 26.59 0.97 -1.35 22.30 2.562009 -22.07 7.37 29.43 1.24 -1.10 26.30 2.822010 -22.87 7.17 30.04 1.00 -1.08 25.64 2.82

2011 -24.07 6.81 30.88 0.84 -1.14 25.80 2.892012 -25.29 6.39 31.68 0.71 -1.28 26.05 2.962013 -26.51 5.96 32.46 0.60 -1.45 26.37 3.042014 -27.70 5.53 33.23 0.51 -1.65 26.74 3.112015 -28.87 5.11 33.98 0.43 -1.85 27.15 3.18

Av75-09 -13.67 7.76 21.43 0.36 3.36 8.16 2.54Av10-15 -25.89 6.16 32.05 0.68 -1.41 26.29 3.00

Table-3.3-B Baseline: BOP items as percentage of nominal GDP (%) Year Remitance C/A

Bal

CapB

al

FLOA

N

Amort BOPB

AL2000 9.85 4.57 -0.13 3.36 1.09 4.442001 10.69 4.56 -2.40 2.65 1.07 2.162002 10.35 3.95 -4.58 1.75 1.10 -0.632003 11.01 2.36 -1.55 1.06 1.15 0.812004 10.92 2.72 1.02 1.72 1.07 3.742005 11.12 1.96 -1.00 1.23 1.01 0.962006 14.94 2.17 2.26 1.18 1.07 4.432007 13.84 -0.12 1.60 1.34 1.04 1.482008 17.41 2.89 1.49 1.38 0.96 4.38

2009 22.11 4.37 2.56 0.77 1.07 6.932010 21.41 2.69 -0.20 1.30 1.56 2.492011 21.39 1.43 -0.48 1.42 1.93 0.962012 21.45 0.19 -0.72 1.48 2.24 -0.532013 21.55 -1.00 -0.97 1.52 2.51 -1.972014 21.70 -2.11 -1.23 1.52 2.78 -3.352015 21.87 -3.14 -1.52 1.51 3.05 -4.66

Av75-09 5.01 -1.79 6.13 3.15 0.73 4.34Av10-15 21.56 -0.32 -0.85 1.46 2.35 -1.18

Notes: AV75-09 stands for average for the sample period 1975-2009

and AV10-15 stands for average for the projection period 2010-2015

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Table-3.4-A Growth rates of Money Supply and Money Demand (%) GrowthRate (%)

M2D NFA NDA Credit CR.Gov CR.Pub

2000 21.81 23.74 20.37 17.18 9.52 13.13

2001 15.22 9.11 19.88 18.89 28.63 15.472002 4.45 0.71 7.04 10.36 21.11 21.202003 9.79 3.38 13.97 10.19 5.45 1.602004 12.77 19.03 9.06 9.91 -0.81 10.892005 8.34 -0.98 14.36 13.57 10.43 18.882006 15.64 29.42 7.93 7.67 9.75 -11.112007 13.84 -5.40 26.75 18.96 9.92 -60.262008 25.25 29.98 22.88 21.10 10.94 7.162009 27.73 28.95 27.09 26.11 18.38 -11.602010 13.85 13.19 14.21 15.51 22.47 4.83

2011 14.89 -2.07 23.91 21.67 27.31 3.732012 15.49 -8.78 25.69 21.49 26.76 2.932013 15.83 15.50 15.93 21.03 25.04 2.412014 16.00 25.92 12.99 20.57 23.39 2.052015 16.09 31.72 10.79 20.20 22.14 1.77

Av75-09 18.47 19.25 19.91 18.92 20.63 9.71Av10-15 15.36 12.58 17.25 20.08 24.52 2.95

Table-3.4-B Growth rates of Money Supply and Money Demand (%)

GrowthRate (%) others M1 Currrency DD FD CR.Pvt2000 11.24 19.42 20.46 17.16 23.00 20.542001 16.91 15.74 14.60 18.29 14.97 15.822002 17.25 9.32 15.25 -3.52 2.05 5.172003 3.05 8.55 2.20 24.99 10.44 13.232004 11.69 12.20 11.13 14.45 13.06 14.282005 11.96 6.64 8.80 2.18 9.22 14.202006 7.11 14.15 13.29 16.04 16.38 8.782007 2.60 10.93 7.22 18.85 15.28 28.492008 16.48 21.64 19.89 25.00 26.95 24.512009 23.45 27.29 25.54 30.52 27.93 29.002010 19.18 11.26 13.87 6.62 15.01 13.952011 15.63 12.32 13.78 9.53 16.01 20.422012 9.31 12.90 13.69 11.33 16.58 20.222013 37.99 13.21 13.60 12.43 16.89 20.032014 41.76 13.37 13.52 13.08 17.05 19.862015 41.17 13.44 13.43 13.46 17.10 19.72

Av75-09 18.42 15.95 15.73 16.76 20.95 21.00Av10-15 27.51 12.75 13.65 11.07 16.44 19.03

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Table-3.4-C Money Supply & Demand as percentage of GDP at CMP

Year M2D NFA NDA Credit CR.Gov CR.Pub

2000 49.78 21.52 28.26 42.26 10.23 2.76

2001 48.57 19.89 28.69 42.55 11.14 2.70

2002 48.75 19.24 29.51 45.12 12.97 3.14

2003 49.96 18.57 31.39 46.41 12.76 2.98

2004 51.66 20.27 31.39 46.78 11.61 3.03

2005 50.97 18.28 32.69 48.38 11.67 3.28

2006 53.12 21.32 31.80 46.94 11.55 2.63

2007 54.65 18.23 36.42 50.46 11.47 0.94

2008 60.43 20.92 39.51 53.95 11.23 0.89

2009 66.70 23.31 43.39 58.80 11.49 0.68

2010 64.79 22.51 42.28 57.94 12.01 0.61

2011 65.53 19.40 46.13 62.07 13.46 0.56

2012 66.86 15.64 51.22 66.61 15.07 0.51

2013 68.57 15.99 52.57 71.39 16.69 0.46

2014 70.54 17.86 52.69 76.34 18.26 0.42

2015 72.72 20.89 51.83 81.48 19.81 0.38

Av75-09 35.46 13.50 21.95 31.88 10.36 3.29

Av10-15 68.17 18.71 49.45 69.31 15.88 0.49

Table-3.4-D Money Supply & Demand as percentage of GDP at CMP

Year others M1 Currrency DD FD CR.Pvt

2000 -14.00 16.31 11.27 5.04 33.47 29.282001 -13.86 15.99 10.94 5.05 32.59 28.712002 -15.62 16.79 12.11 4.68 31.96 29.022003 -15.02 17.02 11.56 5.46 32.94 30.672004 -15.39 17.51 11.78 5.73 34.16 32.142005 -15.69 17.00 11.67 5.33 33.97 33.432006 -15.14 17.49 11.91 5.57 35.63 32.772007 -14.04 17.53 11.54 5.99 37.11 38.05

2008 -14.44 18.83 12.22 6.61 41.60 41.832009 -15.40 20.71 13.26 7.45 45.99 46.62

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2010 -15.66 19.66 12.88 6.78 45.13 45.332011 -15.94 19.44 12.90 6.54 46.09 48.062012 -15.40 19.39 12.96 6.43 47.47 51.042013 -18.81 19.44 13.03 6.40 49.13 54.242014 -23.65 19.54 13.12 6.42 51.00 57.662015 -29.65 19.69 13.22 6.47 53.03 61.30

Av75-09 -9.93 13.99 9.46 4.53 21.47 18.22Av10-15 -19.85 19.52 13.02 6.51 48.64 52.94


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3.5 Government Finance Block

As compared with the earlier NMEM, the government finance block in the upgraded NMEMis very detailed with various equations and identities for different components of governmentrevenues and expenditure.

Government current and capital expenditure are policy variables and follow anautoregressive model. Amortization depends on government revenues and AR(1).

As regards receipts, direct tax depends on non-agriculture income, indirect tax depends onoverall economic size represented by GDP, non-tax depends on non-agriculture GDP andgrants follow an autoregressive model.

While domestic loans follow an autoregressive AR(1) model, foreign loans are influenced bygovernment capital expenditure.

Several other equations for excise duties, customs duties, property taxes, wealth and

income taxes, other goods and service taxes are also estimated in the model and can beprojected for future.

The projected growth rates of different items of government finance block for the period2010-2015 and the corresponding ratios to GDP at current market prices are presentedin Tables 3.5-A to 3.5-D appear to be realistic.

As percentages to GDP, under the baseline scenario, revenues show a declining trend,while expenditures show an increasing trend. Consequently, there is deterioration in fiscaldeficit from 5.6 percent of GDP in 2010 to 8.6 percent of GDP in 2015. This implies that thefiscal deficit will be unsustainable overb the years unless government takes appropriatemeasures to enhance tax buoyancy and to curtail unproductive and wasteful expenditure.

3.6 Block Linkages

As mentioned above, once the coefficients in regression equations are estimated, asimulation can be conducted to solve for each endogenous variable. Specifying theexogenous variables, interactions among variables across blocks are repeated until thesystem finds the equilibrium for the economy. Therefore, it is important to understandhow the variables or blocks are interlinked in the system.

The government block is directly linked to the final demand block of the nationalaccounts. The government expenditures influence both consumption and investments in

the national accounts block. A rise in government expenditures leads to higher investment in agriculture, mining, construction, utilities, financial and social sectors whichin turn induce production in all the sectors. Higher production may lead to higher taxcollections which in turn may influence higher capital expenditure by the government.These feedbacks interact with each other and ultimately the system settles for equilibrium.

The money block is linked to final demand, government, and BOP. The aggregate M2 isthe sum of private and government domestic credits and net foreign assets. NominalGDP influences private credits on the side of money demand, and the demand and fixeddeposits on the side of money supply. The net foreign assets is determined by the

overall balance from the BOP block.

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Table 3.5-A: Growth Rates of Items in Government Finance Block (%)Year Receipt Rev Tax Non-

TaxGrants Exp

2000 16.87 15.15 15.30 14.63 31.71 11.23

2001 14.49 13.99 17.23 2.95 18.24 20.462002 2.67 3.17 1.20 10.83 -1.00 0.302003 18.27 11.47 8.28 22.74 69.59 4.912004 8.95 10.85 13.12 3.78 -0.49 6.472005 14.81 12.50 12.31 13.14 27.54 14.672006 1.89 3.08 6.15 -7.28 -3.92 8.122007 20.21 21.35 23.85 11.67 14.27 20.482008 23.60 22.70 19.72 35.46 28.61 20.742009 38.17 32.14 37.39 12.23 70.12 32.402010 5.87 11.02 10.09 15.35 -15.34 16.60

2011 8.56 10.85 10.23 13.61 -3.78 15.132012 10.79 11.97 11.61 13.53 3.49 14.642013 11.89 12.56 12.36 13.39 7.41 14.542014 12.44 12.86 12.77 13.24 9.46 14.582015 12.70 13.00 12.98 13.09 10.50 14.67

Av75-09 15.85 15.93 15.92 17.22 19.06 15.93Av10-15 10.38 12.04 11.67 13.70 1.96 15.03

Table 3.5-A: Growth Rates of Items in Government Finance Block (%) Year

Cap.Exp Cur.Exp Amort Fis.Bal DomLo

anF.Loan

2000 11.36 10.92 12.28 -1.80 16.77 4.532001 16.63 26.64 9.17 36.91 27.27 10.002002 -33.18 31.82 13.08 -5.16 14.29 -17.572003 -9.76 6.60 48.56 -28.35 11.00 -14.472004 3.31 6.65 12.92 -3.71 -36.85 -1.412005 18.38 11.04 25.37 14.01 59.39 37.532006 8.29 8.64 5.41 37.31 32.40 10.132007 34.19 15.08 17.44 21.44 51.19 39.392008 34.70 18.53 -2.18 10.89 14.55 5.482009 36.99 33.55 11.00 10.27 21.97 20.112010 7.56 16.48 53.86 68.18 3.10 11.102011 7.73 16.44 29.32 34.98 6.67 12.402012 7.88 16.39 22.53 24.01 9.07 12.642013 8.02 16.34 20.15 20.28 10.66 12.482014 8.13 16.30 19.31 18.91 11.72 12.222015 8.24 16.25 19.02 18.44 12.41 11.99

Av75-09 14.63 17.76 26.57 19.49 24.27 18.38Av10-15 7.93 16.37 27.36 30.80 8.94 12.14


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Table-3.5-C Ratios to GDP at current market prices (%)

Year Receipt Rev Tax Non- Tax

Grants Exp

2000 13.00 11.47 8.87 2.61 1.53 17.73

2001 12.60 11.07 8.80 2.27 1.53 18.08

2002 12.43 10.98 8.56 2.42 1.46 17.43

2003 13.73 11.42 8.65 2.77 2.30 17.07

2004 13.71 11.61 8.97 2.64 2.10 16.66

2005 14.34 11.90 9.18 2.72 2.44 17.40

2006 13.16 11.05 8.78 2.27 2.11 16.95

2007 14.30 12.12 9.83 2.29 2.18 18.46

2008 15.61 13.13 10.39 2.74 2.48 19.68

2009 18.64 14.99 12.33 2.66 3.64 22.51

2010 16.83 14.20 11.58 2.62 2.63 22.40

2011 16.09 13.86 11.24 2.62 2.23 22.70

2012 15.75 13.71 11.08 2.62 2.04 22.99

2013 15.60 13.66 11.03 2.63 1.94 23.32

2014 15.56 13.67 11.03 2.65 1.88 23.69

2015 15.57 13.72 11.06 2.66 1.85 24.13

Av75-09 11.99 9.82 7.78 2.04 2.17 17.24

Av10-15 15.90 13.80 11.17 2.63 2.09 23.21

Table-3.5-D Ratios to GDP at current market prices (%)

Cap.Exp Cur.Exp Amort Fis.Bal DomLoan

F.Loan

2000 8.50 7.83 1.39 -4.73 1.47 4.632001 8.40 8.40 1.29 -5.48 1.59 4.312002 5.39 10.64 1.40 -4.99 1.74 3.422003 4.54 10.58 1.94 -3.34 1.80 2.732004 4.30 10.35 2.01 -2.95 1.04 2.472005 4.64 10.47 2.30 -3.06 1.52 3.092006 4.53 10.25 2.18 -3.79 1.81 3.072007 5.49 10.66 2.31 -4.16 2.47 3.86

2008 6.53 11.15 2.00 -4.07 2.50 3.602009 7.73 12.87 1.92 -3.88 2.64 3.73

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2010 7.09 12.79 2.52 -5.57 2.32 3.542011 6.73 13.11 2.87 -6.61 2.18 3.502012 6.41 13.48 3.10 -7.25 2.10 3.482013 6.13 13.89 3.30 -7.72 2.06 3.472014 5.88 14.32 3.49 -8.14 2.04 3.452015 5.65 14.79 3.69 -8.56 2.03 3.43

Av75-09 9.31 6.95 0.98 -5.24 1.74 4.66Av10-15 6.31 13.73 3.16 -7.31 2.12 3.48


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4. Evaluation of the In-sample Forecasting Performance

The model is evaluated for within-sample estimation by standard goodness of fitstatistics. These include R-bar-square, Durbin Watson (DW) Statistic, F-value, MeanAbsolute Squarred Error (MASE), Theil inequality index, bias proportion and variance

proportion. For a good fit, R-bar-square should be nearer to unity, DW nearer to 2, F-value should be high, MASE should be less than 5 percent, and Theil inequality index,bias proportion and variance proportion should be nearer to zero. Annex-1 to this part of the Report (Part-1) presents all these statistics for all estimated equations. It may beobserved that all the fits satisfy all these conditions and are very good fits. The followingtable presents these statistics for selected variables.

Table-4: Evaluation Summary of Estimated Regression Equations

Dependentvariable

Adj. R-square DW F-value

RMSPE

Theilcoefficien

t

Biasproportio

n

Varianceproporti

on

YCSTAGR 0.988 2.120 793.9 2.45 0.013 0.001 0.003

YCSTIND 0.9972.07

5 3898.9 3.60 0.019 0.044 0.137

YCSTSER 0.9971.80

9 7508.6 2.26 0.010 0.000 0.102

YGC 0.9951.78

5 3560.9 8.54 0.021 0.009 0.001

YGFCFPUB 0.9952.07

6 1759.9 8.00 0.036 0.000 0.000

YGFCFPVT 0.9951.76

5 2382.5 7.588 0.012 0.0224 0.0155

BOPEXP 0.9901.72

4 1045.8 12.25 0.055 0.021 0.016

BOPIMP 0.9981.92

6 7043.9 7.70 0.022 0.032 0.137

CPI 0.9981.62

8 6459.7 2.40 0.010 0.001 0.012

EXRATE$ 0.9951.22

1 - 4.77 0.017 0.030 0.097

GFAMORT 0.9912.09

2 1791.9 13.53 0.051 0.026 0.068

GFCAPEXP 0.985

1.37

9 1072.7 19.10 0.086 0.000 0.038

GFCREXP 0.9972.21

611897.

5 6.23 0.020 0.021 0.008

GFDIRTAX 0.9921.34

6 2025.6 17.29 0.097 0.126 0.650

GFINDTAX 0.9971.78

8 5003.2 6.94 0.031 0.017 0.157GFNONTA

X 0.9952.15

8 7193.0 7.34 0.019 0.000 0.000

LRAGR 0.7441.38

4 32.9 6.16 0.044 0.000 0.107

LRCOM 0.9281.68

9 213.8 5.58 0.032 0.000 0.066

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LREXP 0.8391.73

5 58.3 6.10 0.044 0.000 0.073

LRIND 0.8661.90

3 48.6 5.26 0.035 0.000 0.046

MCRGOV 0.998

2.16

2

4171.6

49

14.26

9 0.103 0.082 0.707MCRPUB 0.997

1.906 354.6 12.95 0.037 0.083 0.222

MCRPVT 0.9981.35

1 5244.2 8.36 0.018 0.096 0.359MCURREN

CY 0.9991.68

626633.

4 7.70 0.022 0.032 0.137

MDD 0.9962.17

1 4395.3 7.36 0.044 0.052 0.314

MFD 0.9991.28

924460.

5 4.86 0.023 0.033 0.301

MNFA 0.9931.81

9 2299.6 16.59 0.036 0.034 0.256

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5. Policy Simulations

We have made simulations for five alternative scenarios as indicated below:

(a) 10% cut in government current expenditure;

(b) 5% cut in government current expenditure and 10% increase in governmentcapital expenditure;(c) 5% cut in government current expenditure and shifting the savings in expenditure

to the capital expenditure so that total government expenditure remainsunchanged (expenditure neutral policy);

(d) 15% increase in currency by the monetary authority;(e) 15% autonomous increase in remittances (which is assumed to be exogenous in

the model).

The influence of these simulations on selected variables in different blocks along withthe base line scenario is summarized in Tables 5.1 and 5.2. An analysis of the tables

leads to the following conclusions:(a) As compared with the baseline projections, during the projection period, 10

percent cut in government current expenditure (simulation-1) leads todeceleration of real GDP growth rare, deceleration of money growth, decelerationof inflation in terms of GDP deflator, but acceleration of CPI inflation rate,appreciation of exchange rate of Nepalese rupee in terms of US$, improvementin fiscal balance and improvement in the current account balance on the BOP.

(b) As compared with the baseline projections, during the projection period, 5percent cut in government current expenditure and 10% rise in capitalexpenditure (simulation-2) leads to deceleration of real GDP growth rare,deceleration of money growth, deceleration of inflation in terms of GDP deflator,but acceleration of CPI inflation rate, appreciation of exchange rate of Nepaleserupee in terms of US$, improvement in fiscal balance and improvement in thecurrent account balance on the BOP.

(c) As compared with the baseline projections, during the projection period, 5percent cut in government current expenditure and expenditure-neutral increasein capital expenditure (simulation-3) leads to acceleration of real GDP growthrare, deceleration of money growth, no change of inflation in terms of GDPdeflator, but deceleration of CPI inflation rate, no change of exchange rate of Nepalese rupee in terms of US$, deterioration in fiscal balance and improvementin the current account balance on the BOP.

(d) As compared with the baseline projections, during the projection period, 15percent rise in currency (simulation-4) leads to acceleration of money supply andCPI inflation rate, but no appreciable impact in other variables. It is surprising toobserve that money supply does not matter for growth and fiscal balance.

(e) As compared with the baseline projections, during the projection period, 15percent autonomous rise in remittances (simulation-5) leads to improvement incurrent account balance and overall BOP balance but no appreciable impact onother variables.

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.Table 5.1: Average Growth Rates under Baseline and Alternative Scenario (in percentage)

Variables Baseline Baseline Sim-1 Sim-2 Sim-3 Sim-4 Sim-5

AVGR1975-09

AVGR2010-15

AVGR2010-15

AVGR2010-15

AVGR2010-15

AVGR2010-15

AVGR2010-15

BOPGoodsBalance

18.8 18.9 15.3 16.1 20.1 18.9 18.9

Exports 16.1 7.0 3.2 3.9 7.5 7.0 7.0Imports 16.5 16.5 12.8 13.6 17.5 16.5 16.5Net Income -3.0 -4.7 -4.7 -4.7 -4.7 -4.7 -4.7Net Services 25.0 24.0 24.0 24.1 25.0 24.0 24.0Net Transfer 22.4 14.3 14.4 14.4 14.4 14.4 15.5Grants 17.6 16.0 16.0 16.0 16.0 16.0 16.0Remittance 27.2 13.5 13.6 13.6 13.6 13.6 15.0C/A Balance 123.8 -108.9 -1.4 -12.9 37.1 -77.0 -96.2

Cap-FinBalance

56.3 44.7 44.7 45.5 60.2 44.7 44.7

Foreign loan 18.0 30.1 30.1 31.5 46.6 30.1 30.1Amortization 52.3 36.4 36.4 37.4 47.4 36.4 36.4BOP Balance 45.4 31.7 -28.9 -

534.1-27.7 39.2 -51.2

Govt.FinanceReceipt 15.9 10.4 8.6 9.0 11.3 10.4 10.4Revenue 15.9 12.0 10.4 10.8 13.0 12.0 12.0

Tax 15.9 11.7 9.6 10.1 12.6 11.7 11.7Non-Tax 17.2 13.7 13.7 13.8 15.0 13.7 13.7Grants 19.1 2.0 -0.5 0.0 2.7 2.0 2.0Expenditure 15.9 15.0 2.5 5.1 15.5 15.0 15.0Cap. Exp. 14.6 7.9 7.9 10.0 30.0 7.9 7.9Rev. Exp. 17.8 16.4 -10.0 -5.0 0.8 16.4 16.4Amortization 26.6 27.4 24.8 25.4 28.9 27.4 27.4FiscalBalance

19.5 30.8 -0.6 -65.1 30.5 30.8 30.8

Domesticloan

24.3 8.9 8.9 8.9 8.9 8.9 8.9

Foreign loan 18.4 12.1 12.1 12.7 18.5 12.1 12.1MoneyM2D 18.5 15.4 13.1 13.6 15.9 15.6 15.4NFA 19.2 12.6 6.0 -1.0 16.3 12.2 7.7NDA 19.9 17.3 16.5 19.4 16.5 17.8 19.0Credit 18.9 20.1 11.3 11.6 21.0 20.1 20.1Credit toGovt.

20.6 24.5 -3.6 -15.0 24.3 24.5 24.5

Credit toPSEs

9.7 3.0 3.3 3.2 3.0 3.0 3.0

Credit to Pvt. 21.0 19.0 14.2 15.2 20.3 19.0 19.0

Other items 18.4 27.5 -18.1 -34.3 31.5 26.6 23.8M1 16.0 12.8 11.9 12.1 13.0 13.7 12.8

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Currency 15.7 13.6 13.6 13.6 13.6 15.0 13.6DemandDep.

16.8 11.1 8.6 9.1 11.7 11.1 11.1

FixedDeposits

21.0 16.4 13.6 14.2 17.2 16.4 16.4

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Variables Baseline Baseline Sim-1 Sim-2 Sim-3 Sim-4 Sim-5

AVGR1975-09

AVGR2010-15

AVGR2010-15

AVGR2010-15

AVGR2010-15

AVGR2010-15

AVGR2010-15

Real SectorGDP atcur.mp.

12.7 13.7 10.7 11.3 14.6 13.7 13.7

GDP atconst,bp

4.1 4.4 3.3 3.6 5.2 4.4 4.4

Real AGRGDP

2.8 3.2 3.2 3.4 5.2 3.2 3.2

Industry 6.2 4.1 3.9 4.0 4.8 4.1 4.1-

Manufacturing

6.0 4.0 3.8 3.6 1.1 4.0 4.0

- Mining 9.0 3.1 2.9 2.7 0.2 3.1 3.1

- Pub. Utility 10.5 3.0 4.5 4.3 1.7 4.7 4.7-

Construction6.2 4.1 3.9 4.4 9.0 4.1 4.1

services 5.8 5.2 3.3 3.7 5.3 5.2 5.2- Trade 8.9 9.8 7.8 8.2 9.4 9.8 9.8- Trans-

Comm.6.0 2.3 0.4 0.8 2.0 2.3 2.3

- Fin & realest.

5.0 4.2 2.2 2.8 5.4 4.2 4.2

- Socialsectors

5.5 2.9 1.0 1.4 2.6 2.9 2.9

Non-agr. GDP 5.2 5.0 3.4 3.8 5.2 5.0 5.0DemandSideGovt.Con. 14.5 13.0 -8.9 -4.7 0.0 13.0 13.0Pvt.consumption

12.8 14.1 11.8 12.3 14.7 14.1 14.3

Investment 15.6 17.5 16.0 16.7 22.2 17.4 16.9GFCF 14.6 14.8 11.8 12.8 19.6 14.8 14.8Pub. GFCF 15.0 11.0 11.0 12.8 30.0 11.0 11.0Pvt. GFCF 15.2 15.7 12.0 12.8 16.6 15.7 15.7Stocks 47.5 23.1 24.2 24.3 28.3 23.0 21.3GNDI 13.4 13.7 11.4 11.9 14.4 13.7 14.0GNI 12.8 13.6 10.5 11.2 14.4 13.6 13.6Exports of GS 15.1 7.6 5.6 5.9 8.0 7.6 7.6Imports of GS 16.6 15.7 12.6 13.3 16.6 15.7 15.7Inflation(%)BANKRATE 11.2 5.8 6.0 6.0 5.8 5.8 5.8CPI Inflation 8.3 9.1 9.6 9.4 8.8 9.4 9.1Indian CPI Inf 7.4 8.5 8.5 8.5 8.5 8.5 8.5GDP-DFInflation

8.2 8.7 6.6 7.0 8.7 8.7 8.7

AGR-DFInflation

7.3 9.4 3.6 4.0 7.6 9.4 9.4

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IND-DFInflation

8.4 6.7 6.9 7.1 9.8 6.7 6.7

SER-DFInflation

9.7 8.4 10.5 10.0 8.8 8.4 8.4

Dep.rate

(1yr)

9.6 5.8 5.8 5.8 5.8 5.8 5.8

Sav.Dep Rate 7.1 4.3 4.5 4.5 4.3 4.3 4.3EX.RATE (NR/$)

42.4 74.3 68.5 69.6 74.2 74.3 74.3

Ave.lendingrare

13.8 11.6 11.6 11.7 11.8 11.6 11.6

Notes: Sim-1 : 10% cut in government current expenditure;Sim-2: 5% cut in govt. current exp. and 10% increase in govt. capital exp;Sim-3: 5% cut in govt. current exp. And expenditure-neutral increase in Cap. Exp;Sim-4: 15% increase in currency by the monetary authority; andSim-5: 15% increase in remittances.

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Table 5.2: Ratios as percentages of GDP at current market prices

Variable Baseline

Baseline

Sim-1 Sim-2 Sim-3 Sim-4 Sim-5

AVRY

1075-09

AVRY

2010-15

AVRY

2010-15

AVRY

2010-15

AVRY

2010-15

AVRY

2010-15

AVRY

2010-15BOPGoodsBalance

-13.7 -25.9 -25.6 -25.7 -26.1 -25.9 -25.9

Exports 7.8 6.2 5.9 5.9 6.1 6.2 6.2Imports 21.4 32.0 31.5 31.6 32.2 32.0 32.0Net Income 0.4 0.7 0.7 0.7 0.7 0.7 0.7Net Services 3.4 -1.4 -1.6 -1.5 -1.4 -1.4 -1.4Net Transfer 8.2 26.3 29.4 28.7 25.7 26.4 27.3Grants 2.5 3.0 3.3 3.3 2.9 3.0 3.0

Remittance 5.0 21.6 24.1 23.5 21.1 21.6 22.6C/A Balance -1.8 -0.3 3.0 2.2 -1.2 -0.3 0.7Cap-FinBalance

6.1 -0.9 -1.0 -1.0 -0.9 -0.9 -0.9

Foreign loan 3.2 1.5 1.6 1.6 2.3 1.5 1.5Amortization 0.7 2.3 2.6 2.6 3.2 2.3 2.3BOP Balance 4.3 -1.2 2.0 1.2 -2.0 -1.1 -0.2Govt.FinanceReceipt 12.0 15.9 16.7 16.5 15.9 15.9 15.9Revenue 9.8 13.8 14.6 14.4 13.8 13.8 13.8

Tax 7.8 11.2 11.7 11.6 11.2 11.2 11.2Non-Tax 2.0 2.6 2.9 2.9 2.7 2.6 2.6Grants 2.2 2.1 2.1 2.1 2.1 2.1 2.1Expenditure 17.2 23.2 16.9 18.2 23.1 23.2 23.2Capital Exp. 9.3 6.3 7.0 7.3 13.7 6.3 6.3Current Exp. 6.9 13.7 6.6 7.6 6.2 13.7 13.7Amortization 1.0 3.2 3.3 3.3 3.2 3.2 3.2FiscalBalance

-5.2 -7.3 -0.2 -1.6 -7.1 -7.3 -7.3

Domestic

loan

1.7 2.1 2.4 2.3 2.1 2.1 2.1

Foreign loan 4.7 3.5 3.9 3.8 4.2 3.5 3.5MoneyM2D 35.5 68.2 71.2 70.5 67.4 68.7 68.2NFA 13.5 18.7 23.5 18.7 19.4 18.5 17.4NDA 22.0 49.5 47.7 51.8 48.0 50.2 50.8Credit 31.9 69.3 55.7 57.4 69.6 69.3 69.3Credit toGovt.

10.4 15.9 5.3 6.3 15.6 15.9 15.9

Credit to PSEs 3.3 0.5 0.5 0.5 0.5 0.5 0.5Credit to Pvt. 18.2 52.9 49.8 50.5 53.6 52.9 52.9Other items -9.9 -19.9 -8.0 -5.6 -21.6 -19.2 -18.5M1 14.0 19.5 21.2 20.8 19.1 20.0 19.5

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Currency 9.5 13.0 14.5 14.2 12.7 13.5 13.0Demand Dep. 4.5 6.5 6.7 6.7 6.5 6.5 6.5FixedDeposits

21.5 48.6 50.0 49.7 48.3 48.6 48.6

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Variable Baseline

Baseline

Sim-1 Sim-2 Sim-3 Sim-4 Sim-5

AVRY

1075-09

AVRY

2010-15

AVRY

2010-15

AVRY

2010-15

AVRY

2010-15

AVRY

2010-15

AVRY

2010-15Real Sector

YCMP 100.0 100.0 100.0 100.0 100.0 100.0 100.0Consumption 88.6 93.9 91.5 91.6 88.2 94.0 94.6Govt.consumpt.

8.7 10.8 5.9 6.7 5.5 10.8 10.8

Pvt.consumption

79.9 83.2 85.5 85.0 82.7 83.2 83.8

Dom.Investment

21.7 33.4 35.7 35.6 39.3 33.3 32.7

GFCF 18.8 22.3 22.3 22.5 25.9 22.3 22.3Pub. GFCF 5.8 4.0 4.4 4.5 7.6 4.0 4.0Pvt. GFCF 13.0 18.3 17.9 18.0 18.4 18.3 18.3Stocks 2.9 11.1 13.4 13.0 13.4 11.0 10.4Exports of GS 15.1 10.8 11.1 11.0 10.7 10.8 10.8imports of GS 25.5 38.1 38.2 38.2 38.2 38.1 38.1GNDI 108.5 127.0 130.1 129.4 126.4 127.0 128.0GNI 100.4 100.7 100.7 100.7 100.7 100.7 100.7Gr.Dom.Savings

11.4 6.1 8.5 8.4 11.8 6.0 5.4

Notes: Sim-1 : 10% cut in government current expenditure;

Sim-2: 5% cut in govt. current exp. and 10% increase in govt. capital exp;Sim-3: 5% cut in govt. current exp. And expenditure-neutral increase in Cap. Exp;Sim-4: 15% increase in currency by the monetary authority;

Sim-5: 15% increase in remittances.

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6. Limitations of the Model

While agreeing that at the conceptual level, the upgraded NMEM developed by theconsultants addresses the major deficiencies in the classic NMEM, the NRB has indicatedthe following econometric limitations for the estimation of the upgraded NMEM:

Testing for stationarity : The modeling team has not been found working in a standardeconometric methodological framework. Macroeconomic variables are time series dataimplying a need to test for stationarity of the data since its presence make the regressionmodel spurious. The discussion so far, focused on the seemingly contradictory results of KPSS and ADF, standard tests of unit roots. However after long and tedious discussion thiswas found to be a non-issue at the standard practice 5% level of significance as shown inthe first, second and third attachment, since the result generally point to the sameconclusion - it seems that the problem was in interpreting the results.

Implication of stationarity : As prior mentioned, the presence of stationarity and in theabsence of correction, opens the result to the accusation of being spurious. Absence of this

exercise also seriously puts into question credibility and competence. The standardmethodology on finding non-stationary variables is to first test whether the series share anylong term trends - e.g. are they cointegrated? Depending on the conclusion, thisnecessitates corrective time series techniques such as either first differences, error correcting mechanism etc.

Correction of non-stationary variables : Brief review of the literature finds that non-stationary variables are a common problem. Please have a look at the macroeconometricmodel of Lithuania; Philippines and Thailand (please note that the Philippines macro modelwas supported by ADB). The experience of central banks after they find that with the seriesbeing non-stationary, is to first test for cointegration and then use an error correctionframework. The different equations are then tested in a simultaneous framework (differentmethodologies are found in the literature) and evaluated/judged by different criteria (in-sample forecasting).

Although we do not have any particular disagreements with these observations,we would like to make the following clarifications:

1. We were given only seven weeks to upgrade the NMEM. Seven weeks are notsufficient to conduct such detailed tests of stationarity and use of Cointegrationand error correction method for a large scale model like ours. We are not awarehow much time was given to build the models in Philippines and Thailand.

2. We do not agree with the view that non-use of these detailed tests of stationaritywill lead to spurious results, because we are guided by economic theories to fitthe equations. Besides we have satisfied all statistical goodness of fit statisticsand have used the desired autoregressive models. In all equations, theautoregressive coefficients are less than unity and the Durbin Watson statistic far exceeds the R-square. These statistics clearly indicate that the relations cannotbe regarded as spurious.

3. In this respect, we would like to refer to one equation on CPI fitted by twomethods- one without conducting detailed tests of stationarity (as have beendone in this report), and another alternative equation on CPI fitted on the basis of

detailed tests of stationarity by both ADF and KPSS tests. While in the first case,the logarithms of the variables were used, in the second case the first differencesof logarithms were used to make the variables stationary. The results are given in

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Tables 6.1 to 6.3 . It may be observed from Tables 6.2 and 6.3 that all standardin-sample goodness of fit statistics such as the Mean Absolute Percentage Error,Theil Inequality Index, Bias proportion and Variance Proportions appear to bebetter for the first equation than those for the second equation. The forecasts of inflation by both the models (Table-6.1) are also almost similar. So it is not clear

how the forecasts of inflation by the OLS can be regarded as spurious.Table-6.1 Estimation of CPI

(MGF stands for estimates by modeling group, NRBF for estimates byNRB,

ESQ-1=error square for First equation and ESQ-2=error square forSecond Equation

CPI MGF NRBF ESQMG ESQNRB1975 15.5 15.8 NA 0.12 NA1976 15.4 14.8 NA 0.38 NA1977 15.8 16.5 15.7 0.46 0.021978 17.6 17.1 17.0 0.27 0.321979 18.2 18.5 18.1 0.11 0.021980 19.9 20.4 19.4 0.23 0.221981 22.6 22.9 21.6 0.09 0.971982 25.0 25.0 24.2 0.00 0.631983 28.5 27.7 26.7 0.59 3.201984 30.3 30.5 29.3 0.04 0.931985 31.5 32.4 32.0 0.86 0.221986 36.5 36.8 34.8 0.09 2.911987 41.4 40.6 37.8 0.68 13.071988 45.9 43.4 41.8 6.26 16.981989 49.7 49.0 46.1 0.55 12.811990 54.5 54.5 50.8 0.00 13.981991 59.8 62.5 55.7 7.44 16.671992 72.4 70.1 63.1 5.39 86.171993 78.8 76.1 71.8 7.27 48.661994 85.9 85.3 77.8 0.42 65.471995 92.5 93.7 85.4 1.43 50.951996 100 101.4 93.1 1.90 47.301997 108.1 107.4 100.4 0.44 58.881998 117.1 120.1 109.9 9.07 51.351999 130.4 126.5 123.9 14.84 42.232000 134.8 135.4 133.7 0.40 1.172001 138.1 142.4 141.4 18.22 10.742002 142.1 150.3 145.0 66.82 8.392003 148.9 152.2 151.2 10.79 5.332004 154.8 158.1 158.8 10.78 16.102005 161.8 163.8 164.5 4.16 7.132006 174.7 173.2 174.3 2.40 0.132007 185.9 181.5 184.8 19.18 1.242008 200.2 199.9 203.2 0.10 8.732009 226.7 227.8 226.7 1.22 0.00

AverageSQ.Error

5.51 17.97

INF INF2010 244.4 248.7 7.8 9.22011 265.8 267.2 8.7 7.42012 288.0 287.1 8.4 7.42013 311.5 308.5 8.1 7.42014 336.4 331.5 8.0 7.42015 363.1 356.2 7.9 7.4

Average (2 years ahead) 8.3 8.3

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Table-6.2 Equation fitted by the First Equation

Dependent Variable: LOG(CPI)Method: Least Squares

Date: 05/20/10 Time: 20:01Sample: 1975 2009Included observations: 35

Variable Coefficient Std. Error t-Statistic Prob.

C -1.541192 0.295182 -5.221150 0.0000LOG(MCURRENCY) 0.334571 0.071475 4.680966 0.0001

LOG(CPIINDIAN) 0.773666 0.104068 7.434225 0.0000TIME -0.024878 0.005733 -4.339155 0.0001

R-squared 0.999044 Mean dependent var 4.124173Adjusted R-squared 0.998951 S.D. dependent var 0.872695S.E. of regression 0.028259 Akaike info criterion -4.187595Sum squared resid 0.024756 Schwarz criterion -4.009841Log likelihood 77.28291 Hannan-Quinn criter. -4.126234F-statistic 10798.22 Durbin-Watson stat 2.266595Prob(F-statistic) 0.000000

0

50

100

150

200

250

300

350

400

1975 1980 1985 1990 1995 2000 2005 2010 2015

CPIF 2 S.E.

Forecast: CPIFActual: CPIForecast sample: 1975 2015Included observations: 41Root Mean Squared Error 2.169581Mean Absolute Error 1.392686

Mean Abs. Percent Error 1.823470Theil Inequality Coefficient 0.007152

Bias Proportion 0.019169Variance Proportion 0.003878Covariance Proportion 0.976954

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Table-6.2 Equation fitted by the Second Equation

Dependent Variable: D(LOG(CPI))Method: Least SquaresDate: 05/22/10 Time: 10:45Sample (adjusted): 1977 2009

Included observations: 33 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

C -0.010136 0.019982 -0.507258 0.6157D(LOG(CPIINDIAN(-1))) 0.590707 0.127265 4.641556 0.0001

D(LOG(M2D)) 0.300647 0.104931 2.865175 0.0075

R-squared 0.507109 Mean dependent var 0.081493Adjusted R-squared 0.474250 S.D. dependent var 0.038856S.E. of regression 0.028174 Akaike info criterion -4.214355Sum squared resid 0.023813 Schwarz criterion -4.078309Log likelihood 72.53685 Hannan-Quinn criter. -4.168579

F-statistic 15.43269 Durbin-Watson stat 2.181694Prob(F-statistic) 0.000025

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1980 1985 1990 1995 2000 2005 2010 2015

CPIF 2 S.E.

Forecast: CPIFActual: CPIForecast sample: 1975 2015Adjusted sample: 1977 2015Included observations: 39Root Mean Squared Error 4.217465Mean Absolute Error 3.303396Mean Abs. Percent Error 3.925303Theil Inequality Coefficient 0.013636

Bias Proportion 0.237953Variance Proportion 0.011154Covariance Proportion 0.750894

7. Concluding

Tarun Das ADB Nepal Upgraded Macronomic Model Part-1

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