Forecast Accuracy after Pretesting with an Application to the Stock Market
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- Jan R. Magnus & Dmitry Danilov, 2004. "Forecast accuracy after pretesting with an application to the stock market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(4), pages 251-274.
References listed on IDEAS
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CitationsCitations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
- Magnus, Jan & Peresetsky, Anatoly, 2010. "The price of Moscow apartments," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 17(1), pages 89-105.
- Mouchart, Michel & Rombouts, Jeroen V.K., 2005.
"Clustered panel data models: an efficient approach for nowcasting from poor data,"
International Journal of Forecasting,
Elsevier, vol. 21(3), pages 577-594.
- MOUCHART, Michel & ROMBOUTS, Jeroen, 2003. "Clustered panel data models: an efficient approach for nowcasting from poor data," CORE Discussion Papers 2003090, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Joshua Gallin & Randal Verbrugge, 2007. "Improving the CPI’s Age-Bias Adjustment: Leverage, Disaggregation and Model Averaging," Working Papers 411, U.S. Bureau of Labor Statistics.
- Ouysse, Rachida, 2006. "Consistent variable selection in large panels when factors are observable," Journal of Multivariate Analysis, Elsevier, vol. 97(4), pages 946-984, April.
- Magnus, Jan R. & Wan, Alan T.K. & Zhang, Xinyu, 2011. "Weighted average least squares estimation with nonspherical disturbances and an application to the Hong Kong housing market," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1331-1341, March.
More about this item
Keywordsforecasting; stock markets; return on investment;
- C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
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