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Laws and Limits of Econometrics

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Abstract

We start by discussing some general weaknesses and limitations of the econometric approach. A template from sociology is used to formulate six laws that characterize mainstream activities of econometrics and the scientific limits of those activities, we discuss some proximity theorems that quantify by means of explicit bounds how close we can get to the generating mechanism of the data and the optimal forecasts of next period observations using a finite number of observations. The magnitude of the bound depends on the characteristics of the model and the trajectory of the observed data. The results show that trends are more elusive to model than stationary processes in the sense that the proximity bounds are larger. By contrast, the bounds are of smaller order for models that are unidentified or nearly unidentified, so that lack or near lack of identification may not be as fatal to the use of a model in practice as some recent results on inference suggest, we look at one possible future of econometrics that involves the use of advanced econometric methods interactively by way of a web browser. With these methods users may access a suite of econometric methods and data sets online. They may also upload data to remote servers and by simple web browser selections initiate the implementation of advanced econometric software algorithms, returning the results online and by file and graphics downloads.

Suggested Citation

  • Peter C.B. Phillips, 2003. "Laws and Limits of Econometrics," Cowles Foundation Discussion Papers 1397, Cowles Foundation for Research in Economics, Yale University.
  • Handle: RePEc:cwl:cwldpp:1397
    Note: CFP 1081.
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    References listed on IDEAS

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    3. Dean Corbae & Sam Ouliaris & Peter C. B. Phillips, 2002. "Band Spectral Regression with Trending Data," Econometrica, Econometric Society, vol. 70(3), pages 1067-1109, May.
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    30. Fisher, Irving, 1907. "The Rate of Interest," History of Economic Thought Books, McMaster University Archive for the History of Economic Thought, number fisher1907.
    31. James H. Stock & Mark W. Watson, 1998. "Diffusion Indexes," NBER Working Papers 6702, National Bureau of Economic Research, Inc.
    32. Chao, John C. & Phillips, Peter C. B., 1999. "Model selection in partially nonstationary vector autoregressive processes with reduced rank structure," Journal of Econometrics, Elsevier, vol. 91(2), pages 227-271, August.
    33. Hardle, Wolfgang & Linton, Oliver, 1986. "Applied nonparametric methods," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 38, pages 2295-2339, Elsevier.
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    45. Forchini, Giovanni & Hillier, Grant, 2003. "Conditional Inference For Possibly Unidentified Structural Equations," Econometric Theory, Cambridge University Press, vol. 19(05), pages 707-743, October.
    46. Xiao, Zhijie & Phillips, Peter C. B., 2002. "Higher order approximations for Wald statistics in time series regressions with integrated processes," Journal of Econometrics, Elsevier, vol. 108(1), pages 157-198, May.
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    Cited by:

    1. Simón Sosvilla-Rivero & Javier Alonso Meseguer, 2005. "Estimación de una función de producción MRW para la economía española, 1910-1995," Investigaciones Economicas, Fundación SEPI, vol. 29(3), pages 609-624, September.
    2. Peter C. B. Phillips & Zhentao Shi, 2021. "Boosting: Why You Can Use The Hp Filter," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 62(2), pages 521-570, May.
    3. Phillips, Peter C.B., 2005. "Automated Discovery In Econometrics," Econometric Theory, Cambridge University Press, vol. 21(1), pages 3-20, February.
    4. John Rust, 2014. "The Limits of Inference with Theory: A Review of Wolpin (2013)," Journal of Economic Literature, American Economic Association, vol. 52(3), pages 820-850, September.
    5. Castle Jennifer L. & Doornik Jurgen A & Hendry David F., 2011. "Evaluating Automatic Model Selection," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-33, February.
    6. Derek Bond & Michael J. Harrison & Edward J. O'Brien, 2005. "Testing for Long Memory and Nonlinear Time Series: A Demand for Money Study," Trinity Economics Papers tep20021, Trinity College Dublin, Department of Economics.
    7. Razzak, Weshah, 2023. "Measuring the Deviations from Perfect Competition: International Evidence," MPRA Paper 119605, University Library of Munich, Germany.
    8. Pesaran, Hashem & Timmermann, Allan, 2005. "Real-Time Econometrics," Econometric Theory, Cambridge University Press, vol. 21(1), pages 212-231, February.
    9. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2012. "Model selection when there are multiple breaks," Journal of Econometrics, Elsevier, vol. 169(2), pages 239-246.
    10. Dietmar Bauer & Alex Maynard, 2010. "Persistence-robust Granger causality testing," Working Papers 1011, University of Guelph, Department of Economics and Finance.
    11. David F. Hendry & Hans‐Martin Krolzig, 2004. "We Ran One Regression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(5), pages 799-810, December.
    12. Georgina Catacora-Vargas & Rosa Binimelis & Anne I. Myhr & Brian Wynne, 2018. "Socio-economic research on genetically modified crops: a study of the literature," Agriculture and Human Values, Springer;The Agriculture, Food, & Human Values Society (AFHVS), vol. 35(2), pages 489-513, June.
    13. Victoria Zinde‐Walsh, 2011. "Presidential Address: Mathematics in economics and econometrics," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 44(4), pages 1052-1068, November.
    14. Peter C. B. Phillips & Sainan Jin, 2021. "Business Cycles, Trend Elimination, And The Hp Filter," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 62(2), pages 469-520, May.
    15. Shahidur Rahman, 2005. "An Alternative Estimation to Spurious Regression Model," Economic Growth Centre Working Paper Series 0507, Nanyang Technological University, School of Social Sciences, Economic Growth Centre.
    16. Phillips, Peter C.B., 2005. "Challenges of trending time series econometrics," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 68(5), pages 401-416.
    17. Duncan, Roberto & Toledo, Patricia, 2019. "Inequality in body mass indices across countries: Evidence from convergence tests," Economics & Human Biology, Elsevier, vol. 33(C), pages 40-57.
    18. Allen, P. Geoffrey & Morzuch, Bernard J., 2006. "Twenty-five years of progress, problems, and conflicting evidence in econometric forecasting. What about the next 25 years?," International Journal of Forecasting, Elsevier, vol. 22(3), pages 475-492.
    19. Bauer, Dietmar & Maynard, Alex, 2012. "Persistence-robust surplus-lag Granger causality testing," Journal of Econometrics, Elsevier, vol. 169(2), pages 293-300.
    20. Ernesto LEON CASTRO & Ezequiel AVILÉS OCHOA & Anna Maria GIL LAFUENTE, 2016. "Exchange Rate Usd/Mxn Forecast Through Econometric Models, Time Series And Howma Operators," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(4), pages 135-150.
    21. Phillips, Peter C.B., 2003. "Vision And Influence In Econometrics: John Denis Sargan," Econometric Theory, Cambridge University Press, vol. 19(3), pages 495-511, June.
    22. W A Razzak, 2007. "A Perspective on Unit Root and Cointegration in Applied Macroeconomics," International Journal of Applied Econometrics and Quantitative Studies, Euro-American Association of Economic Development, vol. 4(1), pages 77-102.
    23. Razzak, Weshah, 2024. "Measuring the Deviations from Perfect Competition: International Evidence (second version)," MPRA Paper 120200, University Library of Munich, Germany, revised 17 Feb 2024.
    24. Derek Bond & Michael J. Harrison & Edward J. O'Brien, 2007. "Demand for Money: A Study in Testing Time Series for Long Memory and Nonlinearity," The Economic and Social Review, Economic and Social Studies, vol. 38(1), pages 1-24.
    25. Simon Peters & Ken Clark & Pascal Ekin & Anja Le Blanc & Stephen Pickles, 2007. "Grid Enabling Empirical Economics: A Microdata Application," Computational Economics, Springer;Society for Computational Economics, vol. 30(4), pages 349-370, November.

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    More about this item

    Keywords

    Activities and limitations of econometrics; automated modeling; nearly unidentified models; nonstationarity; online econometrics; policy analysis; prediction; quantitative bounds; trends; unit roots; weak instruments;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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