Moment-bases estimation of smooth transition regression models with endogenous variables
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.
|Date of creation:||16 Dec 2008|
|Date of revision:|
|Contact details of provider:|| Postal: |
Phone: 31 10 4081111
Web page: http://www.eur.nl/ese
More information through EDIRC
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- 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?," Working Papers Central Bank of Chile 101, Central Bank of Chile.
- Nobay, A. R. & Peel, D. A., 2000. "Optimal monetary policy with a nonlinear Phillips curve," Economics Letters, Elsevier, vol. 67(2), pages 159-164, May.
- Medeiros, Marcelo & Veiga, Alvaro, 2000. "A Flexible Coefficient Smooth Transition Time Series Model," SSE/EFI Working Paper Series in Economics and Finance 360, Stockholm School of Economics, revised 10 Feb 2000.
- 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.
- Mayte Suarez Farinãs & Carlos Eduardo Pedreira & Marcelo C. Medeiros, 2003. "Local-global neural networks: a new approach for nonlinear time series modelling," Textos para discussão 470, Department of Economics PUC-Rio (Brazil).
- Newey, W.K., 1989.
"Efficient Instrumental Variables Estimation Of Nonlinear Models,"
341, Princeton, Department of Economics - Econometric Research Program.
- Newey, Whitney K, 1990. "Efficient Instrumental Variables Estimation of Nonlinear Models," Econometrica, Econometric Society, vol. 58(4), pages 809-37, July.
- Li, W K & Ling, Shiqing & McAleer, Michael, 2002. " Recent Theoretical Results for Time Series Models with GARCH Errors," Journal of Economic Surveys, Wiley Blackwell, vol. 16(3), pages 245-69, July.
- Gali, Jordi & Gertler, Mark, 1999.
"Inflation dynamics: A structural econometric analysis,"
Journal of Monetary Economics,
Elsevier, vol. 44(2), pages 195-222, October.
- Jordi Gali & Mark Gertler, 2000. "Inflation Dynamics: A Structural Econometric Analysis," NBER Working Papers 7551, National Bureau of Economic Research, Inc.
- Jordi Galí & Mark Gertler, 1998. "Inflation dynamics: A structural econometric analysis," Economics Working Papers 341, Department of Economics and Business, Universitat Pompeu Fabra.
- McAleer, Michael, 2005. "Automated Inference And Learning In Modeling Financial Volatility," Econometric Theory, Cambridge University Press, vol. 21(01), pages 232-261, February.
- Martin Cerisola & Gaston Gelos, 2005.
"What Drives Inflation Expectations in Brazil? An Empirical Analysis,"
IMF Working Papers
05/109, International Monetary Fund.
- Martin Cerisola & Gaston Gelos, 2009. "What drives inflation expectations in Brazil? An empirical analysis," Applied Economics, Taylor & Francis Journals, vol. 41(10), pages 1215-1227.
- Whitney K. Newey & James L. Powell, 2003. "Instrumental Variable Estimation of Nonparametric Models," Econometrica, Econometric Society, vol. 71(5), pages 1565-1578, 09.
- Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119, March.
- Sergio A. L. Alves & Waldyr D. Areosa, 2005. "Targets and Inflation Dynamics," Working Papers Series 100, Central Bank of Brazil, Research Department.
- Amemiya, Takeshi, 1974. "The nonlinear two-stage least-squares estimator," Journal of Econometrics, Elsevier, vol. 2(2), pages 105-110, July.
When requesting a correction, please mention this item's handle: RePEc:ems:eureir:14154. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (RePub)
If references are entirely missing, you can add them using this form.