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

  • 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
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Handle: RePEc:rio:texdis:571
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