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By Hans Wilhelm

An image e-book designed to motivate examining in five-year-olds and upwards, this can be on hand as a part of photo e-book set D (0-7500-1221-8), or separately topic to a minimal order price. There are 5 units of those photo books, graded in response to studying skill and curiosity variety.

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London: Harvester Wheatsheaf. A Companion to Economic Forecasting Edited by Michael P. Clements, David F. Hendry Copyright © 2002, 2004 by Blackwell Publishing Ltd C H A P T E R T W O Predictable Uncertainty in Economic Forecasting Neil R. 1. INTRODUCTION Forecasts of economic variables play a prominent role in business decisionmaking, government policy analysis, and economic research. Forecasts often are model-based, with forecasts from an estimated model being constructed as the model’s fitted values over a sample not used in estimation.

H. 19) and the MSFE is As in the simpler static model with only an intercept, the (asymptotic) MSFE does not depend upon the forecast horizon h. 16) is the DGP. 17), as is common in many econometrics software packages. In general, the predicted MSFE differs from the actual MSFE, as when the assumed model is not the DGP; see Clements and Hendry (2001). To illustrate the properties of the predicted MSFE empirically, consider modeling real net national income in the United Kingdom over 1970–1993 and forecasting it through 2010.

5. 7. 1c), both the asymptotic and finite sample MSFEs remain unchanged. 6) imply. 1d), both the asymptotic and finite sample MSFEs have altered. 33σ 2. 15). Thus, the static nature of the static model over the estimation (in-sample) period carries through to the (predictable) properties of its forecasts and forecast errors. Time dependence characterizes the nature of the dynamic model, both in sample and over the forecast period. These results show that characteristics of forecasts are model dependent.

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