IV Estimation in the presence of serially correlated regressors and disturbance terms

Chang Jin Kim; Donggeun Kim; Geun Hye Yang

IV Estimation in the presence of serially correlated regressors and disturbance terms

Chang Jin Kim, Donggeun Kim, Geun Hye Yang

Department of Economics

Research output: Contribution to journal › Article › peer-review

Abstract

We present a unified framework to solve the endogeneity problem in time-series regression models with serially a correlated disturbance term with ARMA(p,q) dynamics. Our focus is on the case in which lagged regressors are used as instrumental variables. The control function approach provides us with the solution to the problem. Besides, it provides us with an easy method for testing for endogeneity. Our Monte Carlo experiments confirm that the proposed two-step estimation procedure and the proposed test of endogeneity perform well for a sample size as small as 250.

Original language	English
Pages (from-to)	56-70
Number of pages	15
Journal	Journal of Economic Theory and Econometrics
Volume	20
Issue number	3
Publication status	Published - 2009 Sept

Keywords

Alternative two-step procedure
Autoregressive disturbance term
Endogeneity
Generated regressors
IV Estimation

ASJC Scopus subject areas

Economics and Econometrics

Cite this

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AB - We present a unified framework to solve the endogeneity problem in time-series regression models with serially a correlated disturbance term with ARMA(p,q) dynamics. Our focus is on the case in which lagged regressors are used as instrumental variables. The control function approach provides us with the solution to the problem. Besides, it provides us with an easy method for testing for endogeneity. Our Monte Carlo experiments confirm that the proposed two-step estimation procedure and the proposed test of endogeneity perform well for a sample size as small as 250.

KW - Alternative two-step procedure

KW - Autoregressive disturbance term

KW - Endogeneity

KW - Generated regressors

KW - IV Estimation

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IS - 3

ER -

IV Estimation in the presence of serially correlated regressors and disturbance terms

Abstract

Keywords

ASJC Scopus subject areas

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