Sign Restrictions, Structural Vector Autoregressions, and Useful
Spatial Vector Autoregressions - Purdue University Vector Autoregressions ... `The basic model...
Transcript of Spatial Vector Autoregressions - Purdue University Vector Autoregressions ... `The basic model...
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Spatial Vector Autoregressions
Todd H. Kuethe - Advanced Topics in SHaPEFebruary 8, 2008
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Spatial Vector AutoregressionsSpatial Vector Autoregressions
Michael Beenstock and Daniel Felsenstein (2007)Spatial Economic Analysis 2 (2): 167-196
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OutlineClassical (aspatial) vector autoregression (VAR)Structural analysisSpatial vector autoregression (SpVAR)Future researchReferencesReferences
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Classical Vector AutoregressionClassical Vector Autoregression
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Vector AutoregressionVAR credited to Sims (1980)The technique is considered one of the most flexible and easy to use models for the analysis of multivariate time seriesDetermines how each endogenous variable responds over time to a shock in its own value and in every other variableAllows the data to guide the researcher
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Vector AutoregressionVAR(p) Equation
Assumes all current innovations are unanticipated but become part of the information set in the next periodVAR( ) i SUR i h l d d d i i i i blVAR(p) is SUR with lagged and deterministic variables as common regressorsSince these variables are the same across all equations, the system can be estimated with OLS without loss of efficiencyRequirements: stationary series (in levels or differences) and free of cointegration
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Selecting the lag lengthThe optimal lag length (p) is usually determined using one of the following popular
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Bayesian VARA common critique of VAR is that parameter estimates are not very precise so forecasts appear more precise than they really areIncorporates a prior belief of the parameter distribution –coefficients on long lags are more likely to be close to
h ffi i h lzero than coefficients on shorter lagsThe Litterman Prior (1986) suggest the prior mean of the first lagged value of the dependent variable should be set to one while all other coefficients should be set close to zero with varying degrees of uncertainty
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CritiquesThere is little structural interpretation of parameter estimates – except for Structural VARsThe estimates are of little value for hypothesis testing“It’s just statistics”Many economists believe we can not acquire enough data y q gto properly describe the behavior of a lot of economic data
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Critiques
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Structural AnalysisStructural Analysis
Interpreting VAR Results
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Structural AnalysisVAR coefficients capture the anticipated impact of a variable. However, there are often a lot of coefficients to interpretIt is more common to examine the model’s residuals which represent unforeseen contemporaneous eventsCommon techniques:
Granger CausalityImpulse Response FunctionsForecast Error Variance Decomposition
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Granger causalityIf y1 is a helpful predictor of y2 then y1 is said to Granger cause y2. Granger causality can be tested with a Wald or F test on all of the coefficients for lagged values of y1 in the y2equation
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Impulse response functionsAny VAR can be modeled as a triangular moving average process
From this equation we can observe changes in Yt given a change in the residual Plotting these values maps out thechange in the residual. Plotting these values maps out the “cycle” created in all variables given a “shock” in one variable
It is common to draw bootstrapped confidence intervals around IRF
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Impulse response functions
Tippecanoe County housing price cycle given a one standard deviation (positive) shock in the effective mortgage rate (Kuethe, 2007).
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Variance decompositionThe forecast error variance decomposition shows the portion of variance in the prediction for each variable in the system – its own shocks and shocks to all other variables
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Variance decomposition
Months PriceMortgage
Rate Inflation EmploymentMoney Supply
1 71.753 0.000 0.774 27.473 0.000
6 55.897 2.358 0.812 37.734 3.199
12 49.286 4.619 1.568 41.421 3.107
18 43.991 5.004 1.965 45.718 3.323
Housing price forecast error variance decomposition – Tippecanoe County, IN (Kuethe, 2007).
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Spatial Vector AutoregressionSpatial Vector Autoregression
Adding Regional Spillovers
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Spatial vector autoregression (SpVAR)The basic model without contemporaneous spatial lags
The model introduces a spatially lagged lag regressorsThe model introduces a spatially lagged lag regressorsIf λ=0, the model reduces to the classical formIf λ=1, the data is spatial nonstationaryIf the spatial lags are contemporaneous YAt and YBt are no longer determined sequentially [Cov(YBt, εAt)≠0] and [Cov(YBt, εAt)≠0], so λA and λB not identified
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Spatial vector autoregressionBeenstock and Felsenstein also present a model that incorporates spatial autocorrelation in the error termThis specification impacts structural analysis with IRFs
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EstimationAssume shocks are correlated across regions by variable, but shocks between variables are uncorrelated
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Bias correctionThere is evidence that the estimated coefficients will be biased downwards when T is finite The bias can be overcome by GMM estimation or corrected for using the following adjustment (which is what Beenstock and Felsenstein do)
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Future ResearchFuture Research
Where YOU can play a part
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Spatial vector error correction (SVECM)Beenstock and Felsenstein’s working paper“Since economic panel data are typically nonstationary either because their means and/or their variances vary over time, the need to develop spatial panel cointegration methods requires no justification.”
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Spatial vector error correctionThe spatially lagged lag variables create additional opportunities for cointegrationLocal cointegration – cointegration within spatial units but not between themSpatial cointegration – cointegration between spatial units but not within themGlobal cointegration – cointegration both locally and spatially
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Other areas for future researchThese tools are ripe for application to a number of economic questions concerning forecasts of variables that may be effected by locational spillovers
Housing/land valuesMacroeconomic measuresE lEmploymentCommodity prices
There may be a need for a more appropriate model using the Bayesian approach – setting prior beliefs on the spatial interaction parameters
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Thank YouThank You
Questions, comments, concerns?
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ReferencesBeenstock, M. and D. Felsenstein. 2007. Spatial Vector
Autoregressions. Spatial Economic Analysis 2 (2): 167-196.
Beenstock, M. and D. Felsenstein. 2008. Spatial Vector Error Correction. Working paper.
Hamilton, J. 1994. Time Series Analysis. Princeton University Press.
Zivot, E. and J. Wang. 2003. Modeling Financial Time Series With S-Plus. Springer.