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Published online by Cambridge University Press:  08 February 2005

Hashem Pesaran
Cambridge University and University of Southern California
Allan Timmermann
University of California, San Diego


This paper considers the problems facing decision makers using econometric models in real time. It identifies the key stages involved and highlights the role of automated systems in reducing the effect of data snooping. It sets out many choices that researchers face in construction of automated systems and discusses some of the possible ways advanced in the literature for dealing with them. The role of feedbacks from the decision maker's actions to the data generating process is also discussed and highlighted through an example.

Research Article
© 2005 Cambridge University Press

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Aiolfi, M. & C. Favero (2002) Model Uncertainty, Thick Modelling and the Predictability of Stock Returns. IGIER Working paper 221.
Brown, R.L., J. Durbin, & J.M. Evans (1975) Techniques for testing the constancy of regression relationships over time. Journal of the Royal Statistical Society, Series B 37, 149192.Google Scholar
Coe, P.J., M.H. Pesaran, & S.P. Vahey (2003) Scope for Cost Minimization in Public Debt Management: The Case of the UK. Cambridge University,
Croushore, D. & T. Stark (2001) A real-time data set for macroeconomists. Journal of Econometrics 105, 111130.Google Scholar
Dawid, A.P. (1984) Present position and potential developments: Some personal views—Statistical theory, the prequential approach. Journal of the Royal Statistical Society, Series A 147, 278292.Google Scholar
Draper, D. (1995) Assessment and propagation of model uncertainty. Journal of the Royal Statistical Society, Series B 58, 4597.Google Scholar
Egginton, D.M., A. Pick, & S.P. Vahey (2002) Keep it real! A real time UK macro data set. Economic Letters 77, 1520.Google Scholar
Fernandez, C., E. Ley, & M.F.J. Steel (2001a) Benchmark priors for Bayesian model averaging. Journal of Econometrics 100, 381427.Google Scholar
Fernandez, C., E. Ley, & M.F.J. Steel (2001b) Model uncertainty in cross-country growth regressions. Journal of Applied Econometrics 16, 563576.Google Scholar
Garratt, A., K. Lee, M.H. Pesaran, & Y. Shin (2003) Forecast uncertainties in macroeconometric modelling: An application to the UK economy. Journal of the American Statistical Association 98, 829838.Google Scholar
Granger, C.W.J. & D.F. Hendry (2005) A dialogue concerning a new instrument for econometric modeling. Econometric Theory (this issue).Google Scholar
Granger, C.W.J. & Y. Jeon (2004) Thick modeling. Economic Modeling 21, 323343.Google Scholar
Granger, C.W.J. & M.H. Pesaran (2000a) A decision theoretic approach to forecast evaluation. In W.S. Chan, W.K. Li, & H. Tong (eds.), Statistics and Finance: An Interface, pp. 261278. Imperial College Press.
Granger, C.W.J. & M.H. Pesaran (2000b) Economic and statistical measures of forecast accuracy. Journal of Forecasting 19, 537560.Google Scholar
Hendry, D.F. & H.M. Krolzig (2003) New developments in automatic general-to-specific modeling. In B.P. Stigum (ed.), Econometrics and the Philosophy of Economics. Princeton University Press.
Hoeting, J.A., D. Madigan, A.E. Raftery, & C.T. Volinsky (1999) Bayesian model averaging: A tutorial. Statistical Science 14, 382417.Google Scholar
Kass, R. & A.E. Raftery (1995) Bayes factors. Journal of the American Statistical Association 90, 773795.Google Scholar
Koop, G. (2003) Bayesian Econometrics. Wiley.
Leamer, E.E. (1978) Specification Searches: Ad Hoc Inference with Nonexperimental Data. Wiley.
Litterman, R.B. (1986) Forecasting with Bayesian vector autoregressions: Five years of experience. Journal of Business & Economic Statistics 4, 2538.Google Scholar
Pesaran, M.H. & S. Skouras (2002) Decision-based methods for forecast evaluation. In M.P. Clements and D.F. Hendry (eds.), A Companion to Economic Forecasting, pp. 241267. Basil Blackwell.
Pesaran, M.H. & A. Timmermann (1995) The robustness and economic significance of predictability of stock returns. Journal of Finance 50, 12011228.Google Scholar
Pesaran, M.H. & A. Timmermann (2000) A recursive modelling approach to predicting U.K. stock returns. Economic Journal 110, 159191.Google Scholar
Pesaran, M.H. & A. Timmermann (2002) Market timing and return prediction under model instability. Journal of Empirical Finance 9, 495510.Google Scholar
Phillips, P.C.B. (1995) Automated forecasts of Asia-Pacific economic activity. Asia-Pacific Economic Review 1, 92102.Google Scholar
Phillips, P.C.B. (1996) Econometric model determination. Econometrica 64, 763812.Google Scholar
Phillips, P.C.B. (2003) Laws and limits of econometrics. Economic Journal 113, C26C52.Google Scholar
Phillips, P.C.B. & W. Ploberger (1994) Posterior odds testing for a unit root with data-based model selection. Econometric Theory 10, 774808.Google Scholar
Schiff, A.F. & P.C.B. Phillips (2000) Forecasting New Zealand's real GDP. New Zealand Economic Papers 34, 159182.Google Scholar
Stock, J.H. & M. Watson (2002) Macroeconomic forecasting using diffusion indexes. Journal of Business & Economic Statistics 20, 147162.Google Scholar
Sullivan, R., A. Timmermann, & H. White (2001) Dangers of data-driven inference: The case of calendar effects in stock returns. Journal of Econometrics 105, 249286.Google Scholar
Wald, A. (1947) Sequential Analysis. Wiley.