By Clements M.P., Hendry D.F.
A better half to financial Forecasting offers an available and accomplished account of modern advancements in fiscal forecasting. all of the chapters has been especially written by way of a professional within the box, bringing jointly in one quantity a variety of contrasting techniques and perspectives. Forecasting is a pragmatic enterprise, such a lot of of the chapters are geared toward practitioners and nonspecialists.This ebook surveys a box that has multiplied quickly in recent times. There are not any different updated remedies that survey forecasting in one quantity. at this time, the reader has little alternative yet to search out magazine articles and books which regularly extol the virtues of 1 method between many, or clarify only one of the various difficulties that beset fiscal forecasting. The significant other offers a complete account of the major ways and modeling techniques which are commonly hired. an in depth editorial evaluation locations the contributions in context, and exhibits their interconnections and commonalities.
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Additional resources for A Companion to Economic Forecasting
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.