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Moment-based estimation of smooth transition regression models with endogenous variables

Listed author(s):
  • Waldyr Dutra Areosa

    (Department of Economics PUC-Rio e Banco Central do Brasil)

  • Michael McAleer

    (Erasmus School of Economics e Tinbergen Institute e Center for International Research on the Japanese Economy (CIRJE))

  • Marcelo Cunha Medeiros

    ()

    (Department of Economics PUC-Rio)

Nonlinear regression models have been widely used in practice for a variety of time series and cross-section datasets. For purposes of analyzing univariate and multivariate time series data, in particular, Smooth Transition Regression (STR) models have been shown to be very useful for representing and capturing asymmetric behavior. Most STR models have been applied to univariate processes, and have made a variety of assumptions, including stationary or cointegrated processes, uncorrelated, homoskedastic or conditionally heteroskedastic errors, and weakly exogenous regressors. Under the assumption of exogeneity, the standard method of estimation is nonlinear least squares. The primary purpose of this paper is to relax the assumption of weakly exogenous regressors and to discuss moment based methods for estimating STR models. The paper analyzes the properties of the STR model with endogenous variables by providing a diagnostic test of linearity of the underlying process under endogeneity, developing an estimation procedure and a misspecification test for the STR model, presenting the results of Monte Carlo simulations to show the usefulness of the model and estimation method, and providing an empirical application for inflation rate targeting in Brazil. We show that STR models with endogenous variables can be specified and estimated by a straightforward application of existing results in the literature.

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Paper provided by Department of Economics PUC-Rio (Brazil) in its series Textos para discussão with number 571.

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Length: 30p
Date of creation: Mar 2010
Handle: RePEc:rio:texdis:571
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  2. Dick van Dijk & Timo Terasvirta & Philip Hans Franses, 2002. "Smooth Transition Autoregressive Models — A Survey Of Recent Developments," Econometric Reviews, Taylor & Francis Journals, vol. 21(1), pages 1-47.
  3. Mayte Suarez -Farinas & Carlos E. Pedreira & Marcelo C. Medeiros, 2004. "Local Global Neural Networks: A New Approach for Nonlinear Time Series Modeling," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 1092-1107, December.
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  10. Logue, Dennis E & Willett, Thomas D, 1976. "A Note on the Relation between the Rate and Variability of Inflation," Economica, London School of Economics and Political Science, vol. 43(17), pages 151-158, May.
  11. Galí, Jordi & Gertler, Mark, 1999. "Inflation Dynamics: A Structural Economic Analysis," CEPR Discussion Papers 2246, C.E.P.R. Discussion Papers.
  12. Alberto Musso & Livio Stracca & Dick van Dijk, 2009. "Instability and Nonlinearity in the Euro-Area Phillips Curve," International Journal of Central Banking, International Journal of Central Banking, vol. 5(2), pages 181-212, June.
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  20. Dionísio Dias Carneiro, 2000. "Inflation targeting in Brazil: what difference does a year make?," Textos para discussão 429, Department of Economics PUC-Rio (Brazil).
  21. Saikkonen, Pentti & Choi, In, 2004. "Cointegrating Smooth Transition Regressions," Econometric Theory, Cambridge University Press, vol. 20(02), pages 301-340, April.
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  23. Gali, Jordi & Gertler, Mark, 1999. "Inflation dynamics: A structural econometric analysis," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 195-222, October.
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  26. McAleer, Michael, 2005. "Automated Inference And Learning In Modeling Financial Volatility," Econometric Theory, Cambridge University Press, vol. 21(01), pages 232-261, February.
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  28. Frederic S. Mishkin & Klaus Schmidt-Hebbel, 2001. "One Decade of Inflation Targeting in the World: What Do We Know and What Do We Need to Know?," NBER Working Papers 8397, National Bureau of Economic Research, Inc.
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