Prediction in a spatial nested error components panel data model

This paper derives the Best Linear Unbiased Predictor (BLUP) for a spatial nested error components panel data model. This predictor is useful for panel data applications that exhibit spatial dependence and a nested (hierarchical) structure. The predictor allows for unbalancedness in the number of observations in the nested groups. One application includes forecasting average housing prices located in a county nested in a state. When deriving the BLUP, we take into account the spatial correlation across counties, as well as the unbalancedness due to observing different numbers of counties nested in each state. Ignoring the nested spatial structure leads to inefficiency and inferior forecasts. Using Monte Carlo simulations, we show that our feasible predictor is better in root mean square error performance than the usual fixed and random effects panel predictors which ignore the spatial nested structure of the data.

[1]  J. Paul Elhorst.  Spatial Panel Data Models , 2010 .

[2]  Harry H. Kelejian,et al.  A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model , 1999 .

[3]  Marc Nerlove,et al.  Further evidence on the estimation of dynamic economic relations from a time series of cross-sections , 1971 .

[4]  Badi H. Baltagi,et al.  Forecasting with Spatial Panel Data , 2012, Comput. Stat. Data Anal..

[5]  J. LeSage.  Introduction to spatial econometrics , 2009 .

[6]  J. Paul Elhorst,et al.  Specification and Estimation of Spatial Panel Data Models , 2003 .

[7]  B. Baltagi,et al.  The unbalanced nested error component regression model , 2001 .

[8]  Peter Nijkamp,et al.  Forecasting Regional Labor Market Developments under Spatial Autocorrelation , 2007 .

[9]  Boriss Siliverstovs,et al.  A Dynamic Panel Data Approach to the Forecasting of the GDP of German Länder , 2008 .

[10]  L. Anselin.  Spatial Econometrics: Methods and Models , 1988 .

[11]  Seuck Heun Song,et al.  BLUP in the panel regression model with spatially and serially correlated error components , 2002 .

[12]  A. Pirotte,et al.  Prediction in an Unbalanced Nested Error Components Panel Data Model , 2013 .

[13]  E. Girardin,et al.  How helpful are spatial effects in forecasting the growth of Chinese provinces , 2011 .

[14]  Badi H. Baltagi,et al.  Forecasting with Panel Data , 2007, SSRN Electronic Journal.

[15]  Badi H. Baltagi,et al.  Incomplete panels: A comparative study of alternative estimators for the unbalanced one-way error component regression model , 1994 .

[16]  A. Goldberger.  Best Linear Unbiased Prediction in the Generalized Linear Regression Model , 1962 .

[17]  S S Arora,et al.  The Exact Finite Sample Properties of the Estimators of Coefficients in the Error Components Regression Models , 1972 .

[18]  Werner Antweiler,et al.  Nested random effects estimation in unbalanced panel data , 2001 .

[19]  Peter Nijkamp,et al.  Forecasting regional labor market developments under spatial heterogeneity and spatial correlation , 2006 .

[20]  Badi H. Baltagi,et al.  Prediction in the Panel Data Model with Spatial Correlation: the Case of Liquor , 2006 .

[21]  Badi H. Baltagi,et al.  Panel Data Inference Under Spatial Dependence , 2010 .

[22]  L. Anselin,et al.  Spatial Panel Econometrics , 2008 .

[23]  Allan J. Taub.  Prediction in the context of the variance-components model , 1979 .

[24]  Badi H. Baltagi,et al.  Prediction in the Panel Data Model with Spatial Correlation , 2004 .