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Introductory EconometricsECON2206/ECON3209

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Lecturer: Minxian Yang

ie_Slides01 my, School of Economics, UNSW 1


About the Course

Staff Lecturer: Minxian Yang Tutors: Jessica Moses, Jakree Koosakul, Bo Ye

Required textbook Wooldridge, J.M. (2009), Introductory Econometrics:

A Modern Approach, 4th Edition, South-Western Assessment

Two tutorial assignments (weeks 5 & 12): 20% One course project (week 9): 20% Final exam: 60%

Submit assignments and project to your tutor!


About the Course

Econometric software STATA recommended Available in ASB labs, Lab 2 (Q1035) Mon 12-16.

Course resources Course website: announcements, course outline,

lecture slides, tutorial questions/answers, assignments, course project, data, STATA code,

Library (open reserve) Read Course Outline carefully!

Email is not suitable for discussing course material details with the lecturer.

Work commitment and travel plans are not excuses for missing assessment items.


About the Course

Aim & Summary of ECON2206/3290 Aim to help students develop a working knowledge

of econometrics and its applications. Focus on linear regression models, their

estimation, inference and interpretation. Emphasise the applications of econometric methods

in practice. Foster the ability to conduct empirical research in

economics, finance and other social sciences.


1. Introduction (Ch1)

1. Introduction What is econometrics?

It covers the statistical methods useful for estimating economic relationships; testing economic theories; evaluating policies; forecasting economic variables,

based on data (observations on variables).eg. Estimating and testing the effect of

education on wages; minimum wages on unemployment; govt policies on inflation and growth; school spending on student performance; etc.



What is econometrics? Differing from statistics used in physical sciences, it

deals with non-experimental data. Data are typically not from controlled experiments.

Ideal laboratories are not available in social sciences.

eg. The effect of a new drug on patients:possible to assign drug randomly to patients.

Effect of a interest-rate-rise on economies:impossible to assign a rate-rise randomly to economies.


Empirical economic analysis Exploring data to estimate economic relationships

and test economic theories. It involves

embedding the questions of interest in an econometric model, which may be derived from economic theories;

formulating the questions of interest into hypothesis about the parameters of the model;

estimating the parameters and drawing conclusions about the hypotheses from data;

in some cases, making predictions based on the estimated model.

It helps in business or government decision making.



Empirical economic analysiseg. A model for wage:

wage = 0 + 1educ + 2exper + u, wherewage : hourly wage,educ : years of education,exper : years of employment,u: disturbance term that contains excluded

factors (innate ability, job characteristics, ...),0, 1, 2 :

parameters describing how wage is affected by the included factors.



Empirical economic analysiseg. Continue with the model

wage = 0 + 1educ + 2exper + u . a range of hypotheses can be formulated in terms of

the parameters; econometric methods can be used to estimate the

parameters from data; the hypotheses then can be tested based on the

estimated parameters; the estimated model can also be used to make

predictions about wages.



Data structures Empirical analysis requires data. Different data structures may require different

econometric methods. It is important to understand data structures.

There are 4 major data structures Cross-sectional data, time series data, pooled cross sections, panel (or longitudinal) data.



Data structures Cross-sectional data

The observations of variables are collected at the same point in time.

minor timing differences usually ignored The order of observations does not matter (the index

assigned to each observation is immaterial). Random sampling (observations are independent of

each other) is desirable.eg. Randomly drawing 100 families from the population of

Australian households and recording the income and other characteristics of the families.What if wealthy families tend to decline to report?



Data structures Cross-sectional data

eg. WAGE1.RAW (variables in columns) wage (dollars/hour, in 1976 price) educ (years) exper (years) female (dummy) married (dummy) obsno (index)



Data structures Time series data

The observations on variables are collected over time.eg. stock prices, inflation, gdp, ...

The chronological ordering of observations is important. economic variables (eg. inflation) tend to be related to

their recent histories. dependence in observations needs to be accounted for

in econometric models. Observation frequency is important.

Seasonal patterns need to be accounted for.eg. monthly sales series: peak in Dec, trough in Feb.



Data structures Time series data

eg. Minimum wage: data stored in chronological order. year: observation year avgmin: average minimum wage avgcov: average coverage

(% workers coveredby minimum-wage law)

unemp: unemployment rate

gnp: gross national product



Data structures Pooled cross sections

Two or more sets of cross-sectional data at different points in time, with same variables but different units.eg. two cross-sectional family surveys in Australia:

one in 2000: recording income, expenditures, size, ...; one in 2005: same questions; families in 2005 survey may differ from those in 2000.

Pool two surveys to increase sample size. An increase in sample size provides more information.

Pooled can be treated as cross-sectional. Helpful in investigating how economic relationships

change over time.



Data structures Pooled cross sections

An effective way to analyse government policies by looking at economic relationships before and after the introduction of a policy.

eg. house prices in1993 and 1995:before and after a reduction in property taxes in 1994.year is important here.



Data structures Panel (longitudinal) data

The observations follow the same units (individuals, families, firms, ...) over time.

a times series for each cross-sectional unit.eg. data on individuals wage, education, union

membership over 5 years. Panel data has advantages in controlling certain

unobserved factors and studying dynamic behaviour. Panel data are more difficult and expensive to obtain

than pooled cross section. Depending on time, we will briefly consider panel data

method toward the end of this course.



Data structures Panel (longitudinal) data

eg. crime in 150 US cities: 1986 and 1990.Data are stored bycity and year.

Our topics will be grouped according to data type. Part 1: cross-sectional, where econometric foundations

will be formed. Part 2: time series, where trends, seasonality and

dynamics will be treated. Part 3: pooled cross sections and panel.



Work with STATA Create a folder, say F:\ie Save bwght_1st.do and

bwght.raw in the folder F:\ie Launch STATA

(by clicking STATA icon) Press Ctrl+8 to open Do-file Editor,

Click File/Open to open F:\ie\bwght_1st.do

Press Ctrl+D to execute thecommands in the .do fileand check the Results window

Create, modify and save .do filesas you wish



Open Do-Editor Data Browser


Notes on STATA A do-file (ie, .do file) contains a collection of STATA

commands for specific tasks.Commands are separated by semincolon.

A do-file may be created, modified and saved by using the Do-File Editor.

Once a do-file is run, the results or error messages show up in the Results window (or log file). The output must be carefully checked for errors.If any errors, the do-file must be corrected and re-run.

Blackboard: data in Data Files folder , do-files in STATA Files folder.See A Guide for Using STATA in Tutorial folder



Some useful STATA commands /* and */ : whatever comments between /* and */ are ignored by STATA // : whatever after // are ignored (not for 2 or more lines of comments) #delimit; : commands should be separated by semicolon (;) version 9 : compatible with Stata Version 9 clear : clear things from previous runs (clear all in version 11) capture log close : close possibly open log file cd : define working folder (cd means change directory) log using xxx : save output in the file xxx.smcl infile v1 v2 using vvv.raw : read v1 v2 from data file vvv.raw summarize : produce descriptive statistics graph twoway (scatter y x) : produce scatter plot y versus x histogram y : produce histogram of y regress y x : regressing y on x (OLS) exit : stop execution



Causality and Ceteris Paribus In testing economic theories and evaluating policies,

the goal is often to infer the causal effect of one variable on another.eg. Does education has causal effect on workers productivity?

Most economics propositions are ceteris paribus, which means other factors being equal.eg. The demand for coffee decreases as price goes up, holding

other factors (income, prices of other goods, ...) constant.

Ceteris paribus is critical in policy analysis.eg. Would OHS training reduce accidents, holding other factors

fixed?



Causality and Ceteris Paribus In social sciences, obtaining experimental data is

usually impractical. It is not feasible to literally hold all else equal.

However, when properly applied, econometric methods can simulate a ceteris paribus experiment and help us uncover causal effects.

We look at the examples below from two angles. If we were able to carry out an experiment, ...

think of how the experiment would be structured. As we are unable to do the experiment,

think of why and how observed data differ from the experimental data.



Causality and Ceteris Paribus Example 1. Effects of fertiliser on soybean yield

Yield is affected by fertiliser, land quality, Infeasible experiment:

Choose many plots of land of same quality, ; Apply various amounts of fertiliser to the plots; Measure the yields and find the effect of fertiliser.

Feasible experiment: Choose many plots of land; Apply various amounts of fertiliser to the plots randomly

(the amounts applied are independent of land characteristics); Measure the yields and find the ceteris paribus effect of

fertiliser statistically. What if we only have passively-observed data?



Causality and Ceteris Paribus Example 2. Return to education: the wage increase

caused by an extra year of education? Wage is affected by education, experience, ability, ... Experiment:

Choose many individuals (kids); Assign various levels of education to them randomly; Measure the wages and find the ceteris paribus effect of

education statistically. But we only have passively-observed data.

It would be an easy task if education were independent of experience, innate ability,

But education is likely correlated with experience, etc, It will be doable if we have measures on experience,



Causality and Ceteris Paribus Example 2. Return to education

Wage is affected by education, experience, ability, ... Ceteris paribus effect of education on wage can be

properly estimated if data were collected such that education were

independent of experience, ability, (but these factors are likely correlated)

if we have measures (or data) on experience, ability, (but ability, for instance, is hard to measure)

We have to deal with these issues in empirical analysis.



A summary

Meaning of econometrics Meaning of empirical economic analysis Four major data structures Work with STATA Causal effect and ceteris paribus


Introductory EconometricsECON2206/ECON3209About the CourseAbout the CourseAbout the Course1. Introduction (Ch1)1. Introduction (Ch1)1. Introduction (Ch1)1. Introduction (Ch1)1. Introduction (Ch1)1. Introduction (Ch1)1. Introduction (Ch1)1. Introduction (Ch1)1. Introduction (Ch1)1. Introduction (Ch1)1. Introduction (Ch1)1. Introduction (Ch1)1. Introduction (Ch1)1. Introduction (Ch1)1. Introduction (Ch1)Slide Number 201. Introduction (Ch1)1. Introduction (Ch1)1. Introduction (Ch1)1. Introduction (Ch1)1. Introduction (Ch1)1. Introduction (Ch1)1. Introduction (Ch1)1. Introduction (Ch1)

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