Hostname: page-component-78c5997874-fbnjt Total loading time: 0 Render date: 2024-11-09T06:05:28.711Z Has data issue: false hasContentIssue false

INSTRUMENTAL VARIABLE ESTIMATION OF A THRESHOLD MODEL

Published online by Cambridge University Press:  01 October 2004

Mehmet Caner
Affiliation:
University of Pittsburgh
Bruce E. Hansen
Affiliation:
University of Wisconsin

Abstract

Threshold models (sample splitting models) have wide application in economics. Existing estimation methods are confined to regression models, which require that all right-hand-side variables are exogenous. This paper considers a model with endogenous variables but an exogenous threshold variable. We develop a two-stage least squares estimator of the threshold parameter and a generalized method of moments estimator of the slope parameters. We show that these estimators are consistent, and we derive the asymptotic distribution of the estimators. The threshold estimate has the same distribution as for the regression case (Hansen, 2000, Econometrica 68, 575–603), with a different scale. The slope parameter estimates are asymptotically normal with conventional covariance matrices. We investigate our distribution theory with a Monte Carlo simulation that indicates the applicability of the methods.We thank the two referees and co-editor for constructive comments. Hansen thanks the National Science Foundation for financial support. Caner thanks University of Pittsburgh Central Research Development Fund for financial support.

Type
Research Article
Copyright
© 2004 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Andrews, D.W.K. & V. Ploberger (1994) Optimal tests when a nuisance parameter is present only under the alternative. Econometrica 62, 13831414.Google Scholar
Bai, J. (1997) Estimation of a changepoint in multiple regression models. Review of Economics and Statistics 79, 551563.Google Scholar
Barnett, S.A. & P. Sakellaris (1998) Nonlinear response of firm investment to q: Testing a model of convex and non-convex adjustment costs. Journal of Monetary Economics 42, 261288.Google Scholar
Bhattacharya, P.K. & P.J. Brockwell (1976) The minimum of an additive process with applications to signal estimation and storage theory. Zeitschrift für Wahrscheinlichskeitstheorie und Verwandte Gebiete 37, 5175.Google Scholar
Caner, M. (2002) A note on LAD estimation of a threshold model. Econometric Theory 18, 800814.Google Scholar
Chamberlain, G. (1987) Asymptotic efficiency in estimation with conditional Monet restrictions. Journal of Econometrics 34, 305334.Google Scholar
Chan, K.S. (1993) Consistency and limiting distribution of the least squares estimator of a threshold autoregressive model. Annals of Statistics 21, 520533.Google Scholar
Davies, R.B. (1977) Hypothesis testing when a nuisance parameter is present only under the alternative. Biometrika 64, 247254.Google Scholar
Erickson, T. & T.M. Whited (2000) Measurement error and the relationship between investment and q. Journal of Political Economy 108, 10271057.Google Scholar
Fazzari, S.M., R.G. Hubbard, & B.C. Petersen (1988) Financing constraints and corporate investment. Brookings Papers on Economic Activity 1, 141195.Google Scholar
Hansen, B.E. (1996) Inference when a nuisance parameter is not identified under the null hypothesis. Econometrica 64, 413430.Google Scholar
Hansen, B.E. (1999) Threshold effects in non-dynamic panels: Estimation, testing, and inference. Journal of Econometrics 93, 345368.Google Scholar
Hansen, B.E. (2000) Sample splitting and threshold estimation. Econometrica 68, 575603.Google Scholar
Hu, X. & F. Schiantarelli (1998) Investment and capital market imperfections: A switching regression approach using U.S. firm panel data. Review of Economics and Statistics 80, 466479.Google Scholar
Kim, J. & D. Pollard (1990) Cube root asymptotics. Annals of Statistics 18, 191219.Google Scholar
Picard, D. (1985) Testing and estimating change-points in time series. Advances in Applied Probability 17, 841867.Google Scholar