Double Filter Instrumental Variable Estimation of Panel Data Models with Weakly Exogenous Variables PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Double Filter Instrumental Variable Estimation of Panel Data Models with Weakly Exogenous Variables PDF full book. Access full book title Double Filter Instrumental Variable Estimation of Panel Data Models with Weakly Exogenous Variables by Kazuhiko Hayakawa. Download full books in PDF and EPUB format.
Author: Kazuhiko Hayakawa Publisher: ISBN: Category : Languages : en Pages : 64
Book Description
In this paper, we propose instrumental variables (IV) and generalized method of moments (GMM) estimators for panel data models with weakly exogenous variables. The model is allowed to include heterogeneous time trends besides the standard fixed effects. The proposed IV and GMM estimators are obtained by applying a forward filter to the model and a backward filter to the instruments in order to remove fixed effects, thereby called the double filter IV and GMM estimators. We derive the asymptotic properties of the proposed estimators under fixed T and large N, and large T and large N asymptotics where N and T denote the dimensions of cross section and time series, respectively. It is shown that the proposed IV estimator has the same asymptotic distribution as the bias corrected fixed effects estimator when both N and T are large. Monte Carlo simulation results reveal that the proposed estimator performs well in finite samples and outperforms the conventional IV/GMM estimators using instruments in levels in many cases.
Author: Kazuhiko Hayakawa Publisher: ISBN: Category : Languages : en Pages : 64
Book Description
In this paper, we propose instrumental variables (IV) and generalized method of moments (GMM) estimators for panel data models with weakly exogenous variables. The model is allowed to include heterogeneous time trends besides the standard fixed effects. The proposed IV and GMM estimators are obtained by applying a forward filter to the model and a backward filter to the instruments in order to remove fixed effects, thereby called the double filter IV and GMM estimators. We derive the asymptotic properties of the proposed estimators under fixed T and large N, and large T and large N asymptotics where N and T denote the dimensions of cross section and time series, respectively. It is shown that the proposed IV estimator has the same asymptotic distribution as the bias corrected fixed effects estimator when both N and T are large. Monte Carlo simulation results reveal that the proposed estimator performs well in finite samples and outperforms the conventional IV/GMM estimators using instruments in levels in many cases.
Author: Guowei Cui Publisher: ISBN: Category : Languages : en Pages :
Book Description
This paper puts forward a new instrumental variables (IV) approach for linear panel datamodels with interactive effects in the error term and regressors. The instruments are transformed regressors and so it is not necessary to search for external instruments. The proposed method asymptotically eliminates the interactive effects in the error term and in the regressors separately in two stages. We propose a two-stage IV (2SIV) and a mean-group IV (MGIV) estimator for homogeneous and heterogeneous slope models, respectively. The asymptotic analysis for the models with homogeneous slopes reveals that: (i) the√NT-consistent 2SIV estimatoris free from asymptotic bias that could arise due to the correlation between the regressors and the estimation error of the interactive effects; (ii) under the same set of assumptions, existing popular estimators, which eliminate interactive effects either jointly in the regressors and the error term, or only in the error term, can suffer from asymptotic bias; (iii) the proposed 2SIV estimator is asymptotically as efficient as the bias-corrected version of 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 in determinate. A Monte Carlo study confirms good approximation quality of our asymptotic results and competent performance of 2SIV and MGIV in comparison with existing estimators. Furthermore, it demonstrates that the bias-corrections can be imprecise and noticeably inflate the dispersion of the estimators in finite samples.
Author: Milda Norkute Publisher: ISBN: Category : Languages : en Pages : 98
Book Description
This paper develops two instrumental variable (IV) estimators for dynamic panel data models with exogenous covariates and a multifactor error structure when both crosssectional and time series dimensions, N and T respectively, are large. Our approach initially projects out the common factors from the exogenous covariates of the model, and constructs instruments based on this defactored covariates. For models with homogeneous slope coe_cients, we propose a two-step IV estimator: the _rst step IV estimator is obtained using the defactored covariates as instruments. In the second step, the entire model is defactored by the extracted factors from the residuals of the _rst step estimation and subsequently obtain the _nal IV estimator. For models with heterogeneous slope coe _cients, we propose a mean-group type estimator, which is the cross-sectional average of _rst-step IV estimators of cross-section speci_c slopes. It is noteworthy that our estimators do not require us to seek for instrumental variables outside the model. Furthermore, our estimators are linear hence computationally robust and inexpensive. Moreover, they require no bias correction, and they are not subject to the small sample bias of least squares type estimators. The _nite sample performances of the proposed estimators and associated statistical tests are investigated, and the results show that the estimators and the tests perform well even for small N and T.
Author: Hashimzade, Nigar Publisher: Edward Elgar Publishing ISBN: 1788976487 Category : Business & Economics Languages : en Pages : 672
Book Description
Written in a comprehensive yet accessible style, this Handbook introduces readers to a range of modern empirical methods with applications in microeconomics, illustrating how to use two of the most popular software packages, Stata and R, in microeconometric applications.
Author: Alexander Chudik Publisher: ISBN: Category : Languages : en Pages : 70
Book Description
This paper considers estimation and inference in fixed effects (FE) panel regression models with lagged dependent variables and/or other weakly exogenous (or predetermined) regressors when NN (the cross section dimension) is large relative to TT (the time series dimension). The paper first derives a general formula for the bias of the FE estimator which is a generalization of the Nickell type bias derived in the literature for the pure dynamic panel data models. It shows that in the presence of weakly exogenous regressors, inference based on the FE estimator will result in size distortions unless NN/TT is sufficiently small. To deal with the bias and size distortion of FE estimator when NN is large relative to TT, the use of half-panel Jackknife FE estimator is proposed and its asymptotic distribution is derived. It is shown that the bias of the proposed estimator is of order TT -2, and for valid inference it is only required that NN/TT3 --> 0, as NN, TT --> 00 jointly. Extensions to panel data models with time effects (TE), for balanced as well as unbalanced panels, are also provided. The theoretical results are illustrated with Monte Carlo evidence. It is shown that the FE estimator can suffer from large size distortions when NN > TT, with the proposed estimator showing little size distortions. The use of half-panel jackknife FE-TE estimator is illustrated with two empirical applications from the literature.
Author: Alexander Chudik Publisher: ISBN: Category : Languages : en Pages : 187
Book Description
This paper considers estimation and inference in linear panel regression models with lagged dependent variables and/or other weakly exogenous regressors when N (the cross section dimension) is large relative to T (the time series dimension). It allows for fixed and time effects (FE-TE) and derives a general formula for the bias of the FE-TE estimator which generalizes the well known Nickell bias formula derived for the pure autoregressive dynamic panel data models. It shows that in the presence of weakly exogenous regressors, inference based on the FE-TE estimator will result in size distortions unless N/T is sufficiently small. To deal with the bias and size distortion of FE-TE estimator the use of half-panel Jackknife FE-TE estimator is considered and its asymptotic distribution is derived. It is shown that the bias of the half-panel Jackknife FE-TE estimator is of order T ̅2, and for valid inference it is only required that N/T3 → 0, as N,T → ∞ jointly. Extensions to unbalanced panel data models is also provided. The theoretical results are illustrated with Monte Carlo evidence. It is shown that the FE-TE estimator can suffer from large size distortions when N > T, with the half-panel Jackknife FE-TE estimator showing little size distortions. The use of half-panel Jackknife FE-TE estimator is illustrated with two empirical applications from the literature.
Author: Kazuhiko Hayakawa
Author: Kazuhiko Hayakawa
Author: Donald J. Wyhowski
Author: Guowei Cui
Author: Milda Norkute
Author: Hashimzade, Nigar
Author: Alexander Chudik
Author: Manuel Arellano
Author: Weihao Chen
Author: Alexander Chudik
Author: Carrie A. Falls