This paper develops two instrumental variable (IV) estimators for dynamic panel data models with exogenous covariates and a multifactor error structure when both the cross-sectional and time series ...dimensions, N and T respectively, are large. The main idea is to project out the common factors from the exogenous covariates of the model, and to construct instruments based on defactored covariates. For models with homogeneous slope coefficients, we propose a two-step IV estimator. In the first step, the model is estimated consistently by employing defactored covariates as instruments. In the second step, the entire model is defactored based on estimated factors extracted from the residuals of the first-step estimation, after which an IV regression is implemented using the same instruments as in step one. For models with heterogeneous slope coefficients, we propose a mean-group-type estimator, which involves the averaging of first-step IV estimates of cross-section-specific slopes. The proposed estimators do not need to seek for instrumental variables outside the model. Furthermore, these estimators are linear, and therefore computationally robust and inexpensive. Notably, they require no bias correction. We investigate the finite sample performances of the proposed estimators and associated statistical tests, and the results show that the estimators and the tests perform well even for small N and T.
Summary
This paper analyses the instrumental variables (IV) approach put forward by Norkute 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 $\sqrt{\textit {NT}}$-consistent 2SIV estimator is free from asymptotic bias that may arise due to the estimation error of the interactive effects, while (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, while (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.
In this study, we revisit 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.
The estimation of dynamic panel data models with interactive effects and moving average errors is considered. This is accomplished by making an extension to the factor analytical (FA) estimator which ...was originally designed for dynamic panels with fixed effects only and serially uncorrelated errors. The results show that the additional allowances have no effect on the asymptotic properties of the FA estimator. In particular, the asymptotic distribution of the estimator is free of the otherwise so common bias problem, a result that is verified in small samples using Monte Carlo simulation.
In this paper, a factor‐augmented version of the 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 load on the same factors as the error term of the NKPC, the instruments would be invalid and the results would therefore likely be misleading. Motivated by this last observation, the present paper proposes a new estimator of the NKPC that allows for very general forms of factor dependencies and endogeneity. Our results provide evidence in support of the NKPC, but only after the presence of common factors has been appropriately accounted for.
In a recent study, Bai (2013) proposes a new factor analytical (FA) method for estimation of stationary dynamic panel data models with fixed effects. Our interest in this method originates with the ...fact it does not require explicit demeaning of the data, a practice that is known to cause problems of bias and low power in near unit root panels. The purpose is to study the properties of FA when applied to such panels when the common component admits to a interactive effects representation, which is more general than fixed effects. It is shown that the estimator of the autoregressive parameter is consistent with a well centered asymptotic normal distribution, leading to unit root tests with maximal achievable power. In fact, FA is consistent and asymptotically normal regardless of whether the data are near unit root non-stationary or stationary. It is therefore very general and hence widely applicable.
CCE in fixed‐T panels Westerlund, Joakim; Petrova, Yana; Norkute, Milda
Journal of applied econometrics (Chichester, England),
August 2019, Letnik:
34, Številka:
5
Journal Article
Recenzirano
Summary
The presence of unobserved heterogeneity and its likely detrimental effect on inference has recently motivated the use of factor‐augmented panel regression models. The workhorse of this ...literature is based on first estimating the unknown factors using the cross‐section averages of the observables, and then applying ordinary least squares conditional on the first‐step factor estimates. This is the common correlated effects (CCE) approach, the existing asymptotic theory for which is based on the requirement that both the number of time series observations, T, and the number of cross‐section units, N, tend to infinity. The obvious implication of this theory for empirical work is that both N and T should be large, which means that CCE is impossible for the typical micro panel where only N is large. In the current paper, we put the existing CCE theory and its implications to a test. This is done by developing a new theory that enables T to be fixed. The results show that many of the previously derived large‐T results hold even if T is fixed. In particular, the pooled CCE estimator is still consistent and asymptotically normal, which means that CCE is more applicable than previously thought. In fact, not only do we allow T to be fixed, but the conditions placed on the time series properties of the factors and idiosyncratic errors are also much more general than those considered previously.
There is no a priori reason to suppose that price-setting behaviour is homogeneous across sectors and countries. Aggregate data are, however, commonly used to estimate the New Keynesian Phillips ...curve (NKPC), which may very well yield erroneous results if price-setting behaviour is heterogeneous. In this paper, we therefore estimate the hybrid NKPC for the Euro Area using a novel sectoral data set containing quarterly observations from 1999Q1 to 2012Q1. We show that a positive relationship between inflation and real marginal cost cannot be established empirically for a majority of countries and sectors. We also perform a meta-analysis by combining the results of individual significance tests in order to assess the validity of the NKPC in each country across all sectors and in each sector across all countries. We find no empirical evidence for the NKPC in the Euro Area when this meta-analysis is used. Our results therefore raise doubts about the appropriateness of the NKPC for the analysis of inflation dynamics and monetary policy in the Euro Area.
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