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Testing for Nonstationarity Using Maximum Entropy Resampling: A Misspecification Testing Perspective

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  • Andreas Koutris
  • Maria Heracleous
  • Aris Spanos

Abstract

One of the most important assumptions in empirical modeling is the constancy of the statistical model parameters which usually reflects the stationarity of the underlying stochastic process. In the 1980s and 1990s, the issue of nonstationarity in economic time series has been discussed in the context of unit roots vs. mean trends in AR(p) models. This perspective was subsequently extended to include structural breaks. In this article we take a much broader perspective by allowing for more general forms of nonstationarity. The focus of the article is primarily on misspecification testing. The proposed test relies on Maximum Entropy (ME) resampling techniques to enhance the information in the data in an attempt to capture heterogeneity “locally” using rolling window estimators. The t-heterogeneity of the primary moments of the process is generically captured using orthogonal Bernstein polynomials. The effectiveness of the testing procedure is assessed using Monte Carlo simulations.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Econometric Reviews.

Volume (Year): 27 (2008)
Issue (Month): 4-6 ()
Pages: 363-384

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Handle: RePEc:taf:emetrv:v:27:y:2008:i:4-6:p:363-384

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Related research

Keywords: Berstein polynomials; t -Heterogeneity; Maximum Entropy bootstrap; Nonstationarity; Parameter t -invariance; Rolling window estimates;

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Cited by:
  1. Mehmet Balcilar & Rangan Gupta & Stephen M. Miller, 2012. "Housing and the Great Depression," Working papers 2012-47, University of Connecticut, Department of Economics.
  2. A. Yasemin Yalta, 2011. "New Evidence on FDI-Led Growth: The Case of China," Working Papers 1107, TOBB University of Economics and Technology, Department of Economics.
  3. repec:hal:cesptp:halshs-00611932 is not listed on IDEAS
  4. Goodness C. Aye & Mehmet Balcilar & Adel Bosch & Rangan Gupta, 2013. "Housing and the Business Cycle in South Africa," Working Papers 201323, University of Pretoria, Department of Economics.
  5. A. Talha Yalta, 2013. "Small Sample Bootstrap Inference of Level Relationships in the Presence of Autocorrelated Errors: A Large Scale Simulation Study and an Application in Energy Demand," Working Papers 1301, TOBB University of Economics and Technology, Department of Economics.
  6. Wendy Nyakabawo & Stephen M. Miller & Mehmet Balcilar & Sonali Das & Rangan Gupta, 2013. "Temporal Causality between House Prices and Output in the U.S.: A Bootstrap Rolling-Window Approach," Working papers 2013-14, University of Connecticut, Department of Economics.
  7. repec:hal:cesptp:halshs-00721327 is not listed on IDEAS
  8. Yalta, A. Yasemin, 2013. "Revisiting the FDI-led growth Hypothesis: The case of China," Economic Modelling, Elsevier, vol. 31(C), pages 335-343.
  9. Spanos, Aris, 2010. "Statistical adequacy and the trustworthiness of empirical evidence: Statistical vs. substantive information," Economic Modelling, Elsevier, vol. 27(6), pages 1436-1452, November.
  10. Balcilar, Mehmet & Ozdemir, Zeynel Abidin & Arslanturk, Yalcin, 2010. "Economic growth and energy consumption causal nexus viewed through a bootstrap rolling window," Energy Economics, Elsevier, vol. 32(6), pages 1398-1410, November.
  11. Dominique Guegan & Philippe de Peretti, 2011. "Tests of Structural Changes in Conditional Distributions with Unknown Changepoints," Documents de travail du Centre d'Economie de la Sorbonne 11042, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.

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