Publications

In this paper, a factor-augmented version of the hybrid New Keynesian Phillips curve (NKPC) is assessed using a data set comprised of a large panel of European Union (EU) member countries. The factor-augmentation is natural given that country-level inflation rates are highly co-moving. The presence of unattended common factors is important because it raises the issue of omitted variables bias, as the real marginal cost, which is a regressor of the NKPC, is likely to load on the same factors as inflation. One possibility here is to employ the regular instrumental variables approach. However, if the external instruments are subject to the same factors as those in the error term of the NKPC, the instruments would be invalid and the approach would therefore be inappropriate. We propose a novel econometric approach to estimate the hybrid NKPC, which allows for very general forms of factor dependencies and endogeneity, and should as a result lead to improved identification. Our main findings provide support for the hybrid NKPC when the presence of unknown common factors as well as external instruments are accounted for, although the results differ depending on the countries included in the estimation. More specifically, the evidence is stronger when the full sample of EU or Euro Area countries is used, rather than solely the new EU member countries which joined the EU in 2004 or later.

Keywords: New Keynesian Phillips curve, Inflation, Dynamic panel data model, Cross-sectional dependence, Common factors.

JEL codes: E31; E52; C13; C23.

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

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.

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.