Bank of Lithuania
Target group
All results 2
No 91

The Factor Analytical Approach in Trending Near Unit Root Panels

  • Abstract

    In this study, we re-visit the factor analytical (FA) approach for (near unit root) dynamic panel data models, whose asymptotic distribution has been shown to be normal and well centered at zero without the need for valid instruments or correction for bias. It is therefore very appealing. The question is: Does the appeal of FA, which so far has only been documented for fixed effects panels, extends to panels with incidental trends? This is an important question, because many persistent variables are trending. The answer turns out to be negative. In particular, while consistent, the asymptotic normality of FA breaks down when there is an exact unit root present, which limits its applicability.

    Keywords: Dynamic panel data models, Unit root, Factor analytical method

    JEL codes: C12, C13, C33

    The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.

No 90

Two-Stage Instrumental Variable Estimation of Linear Panel Data Models with Interactive Effects

  • Abstract

    This paper analyses the instrumental variables (IV) approach put forward by Norkutė et al. (2021), in the context of static linear panel data models with interactive effects present in the error term and the regressors. Instruments are obtained from transformed regressors, thereby it is not necessary to search for external instruments. We consider a two-stage IV (2SIV) and a mean-group IV (MGIV) estimator for homogeneous and heterogeneous slope models, respectively. The asymptotic analysis reveals that: (i) the √NT-consistent 2SIV estimator is free from asymptotic bias that may arise due to the estimation error of the interactive effects, whilst (ii) existing estimators can suffer from asymptotic bias; (iii) the proposed 2SIV estimator is asymptotically as efficient as existing estimators that eliminate interactive effects jointly in the regressors and the error, whilst; (iv) the relative efficiency of the estimators that eliminate interactive effects only in the error term is indeterminate. A Monte Carlo study confirms good approximation quality of our asymptotic results.

    Keywords: Large panel data, interactive effects, common factors, principal components analysis, instrumental variables.

    JEL codes: C13, C15, C23, C26.

    The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.