### Abstract

We analyze linear panel regression models with interactive fixed effects and predetermined regressors, for example lagged-dependent variables. The first-order asymptotic theory of the least squares (LS) estimator of the regression coefficients is worked out in the limit where both the cross-sectional dimension and the number of time periods become large. We find two sources of asymptotic bias of the LS estimator: bias due to correlation or heteroscedasticity of the idiosyncratic error term, and bias due to predetermined (as opposed to strictly exogenous) regressors. We provide a bias-corrected LS estimator. We also present bias-corrected versions of the three classical test statistics (Wald, LR, and LM test) and show their asymptotic distribution is a χ^{2}-distribution. Monte Carlo simulations show the bias correction of the LS estimator and of the test statistics also work well for finite sample sizes.

Original language | English |
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Pages (from-to) | 158-195 |

Number of pages | 38 |

Journal | Econometric Theory |

Volume | 33 |

Issue number | 1 |

DOIs | |

Publication status | Published - 2017 Feb 1 |

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### All Science Journal Classification (ASJC) codes

- Social Sciences (miscellaneous)
- Economics and Econometrics

### Cite this

*Econometric Theory*,

*33*(1), 158-195. https://doi.org/10.1017/S0266466615000328

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*Econometric Theory*, vol. 33, no. 1, pp. 158-195. https://doi.org/10.1017/S0266466615000328

**Dynamic linear panel regression models with interactive fixed effects.** / Moon, Hyungsik Roger; Weidner, Martin.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Dynamic linear panel regression models with interactive fixed effects

AU - Moon, Hyungsik Roger

AU - Weidner, Martin

PY - 2017/2/1

Y1 - 2017/2/1

N2 - We analyze linear panel regression models with interactive fixed effects and predetermined regressors, for example lagged-dependent variables. The first-order asymptotic theory of the least squares (LS) estimator of the regression coefficients is worked out in the limit where both the cross-sectional dimension and the number of time periods become large. We find two sources of asymptotic bias of the LS estimator: bias due to correlation or heteroscedasticity of the idiosyncratic error term, and bias due to predetermined (as opposed to strictly exogenous) regressors. We provide a bias-corrected LS estimator. We also present bias-corrected versions of the three classical test statistics (Wald, LR, and LM test) and show their asymptotic distribution is a χ2-distribution. Monte Carlo simulations show the bias correction of the LS estimator and of the test statistics also work well for finite sample sizes.

AB - We analyze linear panel regression models with interactive fixed effects and predetermined regressors, for example lagged-dependent variables. The first-order asymptotic theory of the least squares (LS) estimator of the regression coefficients is worked out in the limit where both the cross-sectional dimension and the number of time periods become large. We find two sources of asymptotic bias of the LS estimator: bias due to correlation or heteroscedasticity of the idiosyncratic error term, and bias due to predetermined (as opposed to strictly exogenous) regressors. We provide a bias-corrected LS estimator. We also present bias-corrected versions of the three classical test statistics (Wald, LR, and LM test) and show their asymptotic distribution is a χ2-distribution. Monte Carlo simulations show the bias correction of the LS estimator and of the test statistics also work well for finite sample sizes.

UR - http://www.scopus.com/inward/record.url?scp=84950294768&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84950294768&partnerID=8YFLogxK

U2 - 10.1017/S0266466615000328

DO - 10.1017/S0266466615000328

M3 - Article

AN - SCOPUS:84950294768

VL - 33

SP - 158

EP - 195

JO - Econometric Theory

JF - Econometric Theory

SN - 0266-4666

IS - 1

ER -