TSLS and LIML estimators in panels with unobserved shocks

Giovanni Forchini, Bin Jiang, Bin Peng

Research output: Contribution to journalArticlepeer-review

2 Citations (SciVal)


The properties of the two stage least squares (TSLS) and limited information maximum likelihood (LIML) estimators in panel data models where the observables are affected by common shocks, modelled through unobservable factors, are studied for the case where the time series dimension is fixed. We show that the key assumption in determining the consistency of the panel TSLS and LIML estimators, as the cross section dimension tends to infinity, is the lack of correlation between the factor loadings in the errors and in the exogenous variables—including the instruments—conditional on the common shocks. If this condition fails, both estimators have degenerate distributions. When the panel TSLS and LIML estimators are consistent, they have covariance-matrix mixed-normal distributions asymptotically. Tests on the coefficients can be constructed in the usual way and have standard distributions under the null hypothesis.

Original languageEnglish
Article number19
Number of pages12
Issue number2
Early online date9 Apr 2018
Publication statusPublished - 1 Jun 2018


  • Common shocks
  • Limited information maximum likelihood
  • Two-stage least squares

ASJC Scopus subject areas

  • Economics and Econometrics


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