Forecasting with spatial panel data
Various forecasts using panel data with spatial error correlation are compared using Monte Carlo experiments. 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. In addition, the root mean squared error performance of these forecasts is examined under misspecification of the spatial error process, various spatial weight matrices, and heterogeneous rather than homogeneous panel data models.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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).
- 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.
- 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.
- Baltagi B-H & Bresson G. & Pirotte A., 2005. "Panel Unit Root Tests and Spatial Dependence," Working Papers ERMES 0503, ERMES, University Paris 2.
- 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.
- 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.
- 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.
- 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.
- Frees, Edward W. & Miller, Thomas W., 2004. "Sales forecasting using longitudinal data models," International Journal of Forecasting, Elsevier, vol. 20(1), pages 99-114.
- Baltagi, Badi H., 2006.
"Forecasting with panel data,"
Discussion Paper Series 1: Economic Studies
2006,25, Deutsche Bundesbank, Research Centre.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Kajal Lahiri, 2005. "Analysis of Panel Data," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(4), pages 1093-1095.
- 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.
- Luc Anselin & Rosina Moreno, 2001.
"Properties of tests for spatial error components,"
ERSA conference papers
ersa01p183, European Regional Science Association.
- 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.
- Taub, Allan J., 1979. "Prediction in the context of the variance-components model," Journal of Econometrics, Elsevier, vol. 10(1), pages 103-107, April.
- 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.
- Harry H. Kelejian & Ingmar R. Prucha, 1995. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," Electronic Working Papers 95-001, University of Maryland, Department of Economics, revised Mar 1997.
- 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.
- 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.
When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:56:y:2012:i:11:p:3381-3397. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Shamier, Wendy)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.