How Much In-Sample Data to Use in Forecasting? Evidence from a Simple Stock Returns Model
David G. McMillan
University of Stirling
May 14, 2009
Using a simple and well-established model for predictive power this letter assess how much in-sample data is required to obtain good out-of-sample forecasts. Specifically using the present value predictive model for monthly stock returns we conduct a backward recursive exercise where the out-of-sample period and the end of the in-sample period are held constant but the start of the in-sample period is rolled backwards. Using RMSE measure for eight international markets results show that in-sample periods of between ten and fifteen years produce reasonable forecasts across markets and forecast horizons.
Number of Pages in PDF File: 10
Keywords: Dividend Yield, Returns Predictability, Forecasting, Backward Recursion
JEL Classification: C22, G12working papers series
Date posted: May 14, 2009
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