Forecasting with Spatial Panel Data
AbstractThis paper compares various forecasts using panel data with spatial error correlation. The true data generating process is assumed to be a simple error component regression model with spatial remainder disturbances of the autoregressive or moving average type. The best linear unbiased predictor is compared with other forecasts ignoring spatial correlation, or ignoring heterogeneity due to the individual effects, using Monte Carlo experiments. In addition, we check the performance of these forecasts under misspecification of the spatial error process, various spatial weight matrices, and heterogeneous rather than homogeneous panel data models.
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Bibliographic InfoPaper provided by ERMES, University Paris 2 in its series Working Papers ERMES with number 0710.
Date of creation: 2007
Date of revision:
Other versions of this item:
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
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- Taub, Allan J., 1979. "Prediction in the context of the variance-components model," Journal of Econometrics, Elsevier, vol. 10(1), pages 103-107, April.
- Badi H. Baltagi & Dong Li, 2006.
"Prediction in the Panel Data Model with Spatial Correlation: The Case of Liquor,"
Center for Policy Research Working Papers
84, Center for Policy Research, Maxwell School, Syracuse University.
- Badi Baltagi & Dong Li, 2006. "Prediction in the Panel Data Model with Spatial Correlation: the Case of Liquor," Spatial Economic Analysis, Taylor & Francis Journals, vol. 1(2), pages 175-185.
- Georges Bresson & Badi H. Baltagi & Alain Pirotte, 2007.
"Panel unit root tests and spatial dependence,"
Journal of Applied Econometrics,
John Wiley & Sons, Ltd., vol. 22(2), pages 339-360.
- Baltagi B-H & Bresson G. & Pirotte A., 2005. "Panel Unit Root Tests and Spatial Dependence," Working Papers ERMES 0503, ERMES, University Paris 2.
- Badi H. Baltagi & Georges Bresson & Alain Pirotte, 2006. "Panel Unit Root Tests and Spatial Dependence," Center for Policy Research Working Papers 88, Center for Policy Research, Maxwell School, Syracuse University.
- Baillie, R.T. & Baltagi, B.H., 1994. "Prediction from the Regression Model with one-way Error Components," Papers 9405, Michigan State - Econometrics and Economic Theory.
- Pesaran, M. Hashem & Smith, Ron, 1995.
"Estimating long-run relationships from dynamic heterogeneous panels,"
Journal of Econometrics,
Elsevier, vol. 68(1), pages 79-113, July.
- Pesaran, M.H. & Smith, R., 1992. "Estimating Long-Run Relationships From Dynamic Heterogeneous Panels," Cambridge Working Papers in Economics 9215, Faculty of Economics, University of Cambridge.
- Kapoor, Mudit & Kelejian, Harry H. & Prucha, Ingmar R., 2007. "Panel data models with spatially correlated error components," Journal of Econometrics, Elsevier, vol. 140(1), pages 97-130, September.
- Badi H. Baltagi, 2008.
"Forecasting with panel data,"
Journal of Forecasting,
John Wiley & Sons, Ltd., vol. 27(2), pages 153-173.
- Badi H. Baltagi, 2007. "Forecasting with Panel Data," Center for Policy Research Working Papers 91, Center for Policy Research, Maxwell School, Syracuse University.
- Baltagi, Badi H., 2006. "Forecasting with panel data," Discussion Paper Series 1: Economic Studies 2006,25, Deutsche Bundesbank, Research Centre.
- Baltagi, Badi H. & Bresson, Georges & Pirotte, Alain, 2002. "Comparison of forecast performance for homogeneous, heterogeneous and shrinkage estimators: Some empirical evidence from US electricity and natural-gas consumption," Economics Letters, Elsevier, vol. 76(3), pages 375-382, August.
- Bernard Fingleton, 2008. "A Generalized Method of Moments Estimator for a Spatial Panel Model with an Endogenous Spatial Lag and Spatial Moving Average Errors," Spatial Economic Analysis, Taylor & Francis Journals, vol. 3(1), pages 27-44.
- Richard Schmalensee & Thomas M. Stoker & Ruth A. Judson, 1998. "World Carbon Dioxide Emissions: 1950-2050," The Review of Economics and Statistics, MIT Press, vol. 80(1), pages 15-27, February.
- Herbert Brücker & Boriss Siliverstovs, 2006.
"On the estimation and forecasting of international migration: how relevant is heterogeneity across countries?,"
Springer, vol. 31(3), pages 735-754, September.
- Brücker, Herbert & Siliverstovs, Boriss, 2005. "On the Estimation and Forecasting of International Migration: How Relevant Is Heterogeneity Across Countries?," IZA Discussion Papers 1710, Institute for the Study of Labor (IZA).
- Luc Anselin & Rosina Moreno, 2001.
"Properties of tests for spatial error components,"
ERSA conference papers
ersa01p183, European Regional Science Association.
- Frees, Edward W. & Miller, Thomas W., 2004. "Sales forecasting using longitudinal data models," International Journal of Forecasting, Elsevier, vol. 20(1), pages 99-114.
- Bernard Fingleton, 2008. "A generalized method of moments estimator for a spatial model with moving average errors, with application to real estate prices," Empirical Economics, Springer, vol. 34(1), pages 35-57, February.
- Badi H. Baltagi & James M. Griffin & Weiwen Xiong, 2000. "To Pool Or Not To Pool: Homogeneous Versus Hetergeneous Estimations Applied to Cigarette Demand," The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 117-126, February.
- Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-33, May.
- Badi H. Baltagi & Georges Bresson & Alain Pirotte, 2004. "Tobin q: Forecast performance for hierarchical Bayes, shrinkage, heterogeneous and homogeneous panel data estimators," Empirical Economics, Springer, vol. 29(1), pages 107-113, January.
- Baltagi, Badi H. & Griffin, James M., 1997. "Pooled estimators vs. their heterogeneous counterparts in the context of dynamic demand for gasoline," Journal of Econometrics, Elsevier, vol. 77(2), pages 303-327, April.
- Rapach, David E. & Wohar, Mark E., 2004. "Testing the monetary model of exchange rate determination: a closer look at panels," Journal of International Money and Finance, Elsevier, vol. 23(6), pages 867-895, October.
- Hoogstrate, Andre J & Palm, Franz C & Pfann, Gerard A, 2000. "Pooling in Dynamic Panel-Data Models: An Application to Forecasting GDP Growth Rates," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(3), pages 274-83, July.
- Hughes, Gordon & Chinowsky, Paul & Strzepek, Ken, 2010. "The costs of adaptation to climate change for water infrastructure in OECD countries," Utilities Policy, Elsevier, vol. 18(3), pages 142-153, September.
- Du, Zaichao, 2014. "Testing for serial independence of panel errors," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 248-261.
- Fingleton, Bernard & Palombi, Silvia, 2013. "Spatial panel data estimation, counterfactual predictions, and local economic resilience among British towns in the Victorian era," Regional Science and Urban Economics, Elsevier, vol. 43(4), pages 649-660.
- Badi H. Baltagi & Bernard Fingleton & Alain Pirotte, 2012.
"Estimating and Forecasting With A Dynamic Spatial Panel Data Model,"
Center for Policy Research Working Papers
149, Center for Policy Research, Maxwell School, Syracuse University.
- Badi H. Baltagi & Bernard Fingleton & Alain Pirotte, 2011. "Estimating and Forecasting with a Dynamic Spatial Panel Data Model," SERC Discussion Papers 0095, Spatial Economics Research Centre, LSE.
- You, Jing, 2013. "China's challenge for decarbonized growth: Forecasts from energy demand models," Journal of Policy Modeling, Elsevier, vol. 35(4), pages 652-668.
- Millo, Giovanni, 2014. "Maximum likelihood estimation of spatially and serially correlated panels with random effects," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 914-933.
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