Summary Statistics and Forecasting Performance
AbstractThe simultaneous equation econometric model has recently come under increasing, attack as a policy analysis and forecasting tool However, traditional methods of choosing among competing models rely heavily on the use of summary Statistics It IS shown, by example, that choosing a model With relatively better summary statistics does not guarantee getting the best unconditional out-of-sample forecasts Other methods of forecasting time series that allow relative evaluation of the out-of-sample forecasting ability of econometric Simultaneous equation models are also examined
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Bibliographic InfoArticle provided by United States Department of Agriculture, Economic Research Service in its journal Agricultural Economics Research.
Volume (Year): (1983)
Issue (Month): 3 ()
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Summary statistics; econometric models; autoregression; ARIMA; identification; Productivity Analysis; Research Methods/ Statistical Methods;
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- Hatanaka, Michio, 1975. "On the Global Identification of the Dynamic Simultaneous Equations Model with Stationary Disturbances," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 16(3), pages 545-54, October.
- Kydland, Finn E & Prescott, Edward C, 1977. "Rules Rather Than Discretion: The Inconsistency of Optimal Plans," Journal of Political Economy, University of Chicago Press, vol. 85(3), pages 473-91, June.
- Granger, C. W. J. & Newbold, P., 1974. "Spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 2(2), pages 111-120, July.
- Durbin, J, 1970. "Testing for Serial Correlation in Least-Squares Regression When Some of the Regressors are Lagged Dependent Variables," Econometrica, Econometric Society, vol. 38(3), pages 410-21, May.
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