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Learning About Models and Their Fit to Data

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  • Adrian Pagan

Abstract

The paper asks what is the most informative way of assessing the fit of a model to data. often an answer comes from the context. In particular, from a consideration of how the model is to be used. Such information often leads one to seek transformations of the data that deliver the requisite information. Even in those instances in which we are sure of the best way of looking at fit, e.g. by the mean of the sample scores of an alternative model, it is often useful to augment the information provided by these tests through a decomposition of them. In time series such decolpositions have often involved recursive analyses. In this paper we propose that he moments underlying tests be re-written as an integrated conditional moment, where the conditioning variable is chosen to elicit useful information. The idea is potentially useful in assessing non-linear models. To implement the approach non-parametric methods generally need to be applied to simulated data in order to perform the decomposition. A range of applications of the idea, drawn from published articles, is used to illustrate the advantages of the method. [C10]

Suggested Citation

  • Adrian Pagan, 2002. "Learning About Models and Their Fit to Data," International Economic Journal, Taylor & Francis Journals, vol. 16(2), pages 1-18.
  • Handle: RePEc:taf:intecj:v:16:y:2002:i:2:p:1-18
    DOI: 10.1080/10168730200000009
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    References listed on IDEAS

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    1. Ait-Sahalia, Yacine, 1996. "Testing Continuous-Time Models of the Spot Interest Rate," Review of Financial Studies, Society for Financial Studies, vol. 9(2), pages 385-426.
    2. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 2002. "Bayesian Analysis of Stochastic Volatility Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 69-87, January.
    3. Pagan, Adrian R. & Schwert, G. William, 1990. "Alternative models for conditional stock volatility," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 267-290.
    4. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 38(2), pages 112-134.
    5. Henry, Olan T & Olekalns, Nilss & Summers, Peter M, 2001. "Exchange Rate Instability: A Threshold Autoregressive Approach," The Economic Record, The Economic Society of Australia, vol. 77(237), pages 160-166, June.
    6. Bodman, Philip M, 1998. "Asymmetry and Duration Dependence in Australian GDP and Unemployment," The Economic Record, The Economic Society of Australia, vol. 74(227), pages 399-411, December.
    7. Horowitz, Joel L., 1993. "Semiparametric estimation of a work-trip mode choice model," Journal of Econometrics, Elsevier, vol. 58(1-2), pages 49-70, July.
    8. Pagan, Adrian, 1996. "The econometrics of financial markets," Journal of Empirical Finance, Elsevier, vol. 3(1), pages 15-102, May.
    9. repec:bla:restud:v:65:y:1998:i:3:p:361-93 is not listed on IDEAS
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    Cited by:

    1. Daniel Buncic, 2012. "Understanding forecast failure of ESTAR models of real exchange rates," Empirical Economics, Springer, vol. 43(1), pages 399-426, August.
    2. Edwards, Sebastian & Biscarri, Javier Gomez & Perez de Gracia, Fernando, 2003. "Stock market cycles, financial liberalization and volatility," Journal of International Money and Finance, Elsevier, vol. 22(7), pages 925-955, December.
    3. Mehmet Balcilar & Rangan Gupta & Stephen M. Miller, 2012. "The Out-of-Sample Forecasting Performance of Non-Linear Models of Regional Housing Prices in the US," Working Papers 201226, University of Pretoria, Department of Economics.
    4. Daniel Buncic, 2008. "A Note on Long Horizon Forecasts of Nonlinear Models of Real Exchange Rates: Comments on Rapach and Wohar (2006)," Discussion Papers 2008-02, School of Economics, The University of New South Wales.
    5. Hyginus Leon & Serineh Najarian, 2005. "Asymmetric adjustment and nonlinear dynamics in real exchange rates," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 10(1), pages 15-39.
    6. Buncic, Daniel & Moretto, Carlo, 2015. "Forecasting copper prices with dynamic averaging and selection models," The North American Journal of Economics and Finance, Elsevier, vol. 33(C), pages 1-38.
    7. Rapach, David E. & Wohar, Mark E., 2006. "The out-of-sample forecasting performance of nonlinear models of real exchange rate behavior," International Journal of Forecasting, Elsevier, vol. 22(2), pages 341-361.
    8. Gene L. Leon & Serineh Najarian, 2003. "Time-Varying Thresholds; An Application to Purchasing Power Parity," IMF Working Papers 03/181, International Monetary Fund.

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