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Estimating Time Series Models Using the Relevant Cost Function

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Author Info
Weiss, Andrew A
Abstract

In many forecasting problems, the forecast cost function is used only in evaluating the forecasts; a second cost function is used in estimating the parameters in the model. In this paper, I explore some of the ways in which the forecast cost function can be used in estimating the parameters and, more generally, in producing the forecasts. I define the optimal forecast and note that it may depend on the entire conditional distribution of the data, which is typically unknown. I then consider three of the steps involved in forming the forecast: approximating the optimal forecast, selecting the model, and estimating any unknown parameters. The forecast cost function forms the basis of the approximation, selection, and estimation. The methods are illustrated using time series models applied to 15 US macroeconomic series and in a small Monte Carlo experiment. Copyright 1996 by John Wiley & Sons, Ltd.

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Article provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.

Volume (Year): 11 (1996)
Issue (Month): 5 (Sept.-Oct.)
Pages: 539-60
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Handle: RePEc:jae:japmet:v:11:y:1996:i:5:p:539-60

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  1. Valentina Corradi & Norman Swanson, 2004. "Bootstrap Procedures for Recursive Estimation Schemes With Applications to Forecast Model Selection," Departmental Working Papers 200418, Rutgers University, Department of Economics. [Downloadable!]
  2. Spyros Skouras, 2001. "Decisionmetrics: A Decision-Based Approach to Econometric Modeling," Working Papers 01-11-064, Santa Fe Institute.
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  3. Guillaume Chevillon, 2005. "Direct multi-step estimation and forecasting," Documents de Travail de l'OFCE 2005-10, Observatoire Francais des Conjonctures Economiques (OFCE). [Downloadable!]
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  4. Kala Krishna & Ataman Ozyildirim & Norman R. Swanson, 1998. "Trade, Investment, and Growth: Nexus, Analysis, and Prognosis," NBER Working Papers 6861, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  5. Gianni Amisano & Raffaella Giacomini, 2005. "Comparing Density Forecsts via Weighted Likelihood Ratio Tests," Working Papers ubs0504, University of Brescia, Department of Economics. [Downloadable!]
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  6. Raffaella Giacomini & Ivana Komunjer, 2002. "Evaluation and Combination of Conditional Quantile Forecasts," University of California at San Diego, Economics Working Paper Series 2002-11, Department of Economics, UC San Diego. [Downloadable!]
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  7. Valentina Corradi & Norman Swanson, 2003. "The Block Bootstrap for Parameter Estimation Error In Recursive Estimation Schemes, With Applications to Predictive Evaluation," Departmental Working Papers 200313, Rutgers University, Department of Economics. [Downloadable!]
  8. Norman Swanson & Valentina Corradi, 2006. "Nonparametric Bootstrap Procedures for Predictive Inference Based on Recursive Estimation Schemes," Departmental Working Papers 200618, Rutgers University, Department of Economics. [Downloadable!]
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  9. Peter F. Christoffersen & Francis X. Diebold, 1997. "Optimal prediction under asymmetric loss," Working Papers 97-11, Federal Reserve Bank of Philadelphia. [Downloadable!]
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  10. Granger, C.W.J. & Pesaran, M. H., 1999. "Economic and Statistical Measures of Forecast Accuracy," Cambridge Working Papers in Economics 9910, Faculty of Economics, University of Cambridge. [Downloadable!]
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