Artūras Juodis
Dados Biográficos
| ID | 8920555 |
|---|---|
| NOME | Artūras Juodis |
| PRENOMES | Artūras |
| SOBRENOME | Juodis |
| ASSINATURA | JUODIS A |
| AFILIAÇÕES | University of Groningen |
| ORCID | 0000-0003-3973-7221 |
| VERIFICADO | Sim |
| TOTAL DE OBRAS | 5 |
| TOTAL DE CITAÇÕES | 0 |
| TOTAL COMO AUTOR | 5 |
| TOTAL COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2018 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2022 |
| ÍNDICE H | 0 |
A regularization approach to common correlated effects estimation
Cross‐section average‐augmented panel regressions introduced by Pesaran (2006) have been a popular empirical tool to estimate panel data models with common factors. However, the corresponding common correlated effects (CCEs) estimator can be sensitive to the number of cross‐section averages used and/or the static factor representation for observables. In this paper, we show that most of the corresponding problems documented in the literature can …
The Incidental Parameters Problem in Testing for Remaining Cross-Section Correlation
In this article, we consider the properties of the Pesaran CD test for cross-section correlation when applied to residuals obtained from panel data models with many estimated parameters. We show that the presence of period-specific parameters leads the CD test statistic to diverge as the time dimension of the sample grows. This result holds even if cross-section dependence is correctly accounted for and hence constitutes an example of the inciden…
A Linear Estimator for Factor-Augmented Fixed-T Panels With Endogenous Regressors
A novel method-of-moments approach is proposed for the estimation of factor-augmented panel data models with endogenous regressors when T is fixed. The underlying methodology involves approximating the unobserved common factors using observed factor proxies. The resulting moment conditions are linear in the parameters. The proposed approach addresses several issues which arise with existing nonlinear estimators that are available in fixed T panel…
A homogeneous approach to testing for Granger non-causality in heterogeneous panels
This paper develops a new method for testing for Granger non-causality in panel data models with large cross-sectional ( N ) and time series ( T ) dimensions. The method is valid in models with homogeneous or heterogeneous coefficients. The novelty of the proposed approach lies in the fact that under the null hypothesis, the Granger-causation parameters are all equal to zero, and thus they are homogeneous. Therefore, we put forward a pooled least…
Pseudo Panel Data Models With Cohort Interactive Effects
When genuine panel data samples are not available, repeated cross-sectional surveys can be used to form so-called pseudo panels. In this article, we investigate the properties of linear pseudo panel data estimators with fixed number of cohorts and time observations. We extend standard linear pseudo panel data setup to models with factor residuals by adapting the quasi-differencing approach developed for genuine panels. In a Monte Carlo study, we …
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Pseudo Panel Data Models With Cohort Interactive Effects
When genuine panel data samples are not available, repeated cross-sectional surveys can be used to form so-called pseudo panels. In this article, we investigate the properties of linear pseudo panel data estimators with fixed number of cohorts and time observations. We extend standard linear pseudo panel data setup to models with factor residuals by adapting the quasi-differencing approach developed for genuine panels. In a Monte Carlo study, we …
A homogeneous approach to testing for Granger non-causality in heterogeneous panels
This paper develops a new method for testing for Granger non-causality in panel data models with large cross-sectional ( N ) and time series ( T ) dimensions. The method is valid in models with homogeneous or heterogeneous coefficients. The novelty of the proposed approach lies in the fact that under the null hypothesis, the Granger-causation parameters are all equal to zero, and thus they are homogeneous. Therefore, we put forward a pooled least…
A regularization approach to common correlated effects estimation
Cross‐section average‐augmented panel regressions introduced by Pesaran (2006) have been a popular empirical tool to estimate panel data models with common factors. However, the corresponding common correlated effects (CCEs) estimator can be sensitive to the number of cross‐section averages used and/or the static factor representation for observables. In this paper, we show that most of the corresponding problems documented in the literature can …
The Incidental Parameters Problem in Testing for Remaining Cross-Section Correlation
In this article, we consider the properties of the Pesaran CD test for cross-section correlation when applied to residuals obtained from panel data models with many estimated parameters. We show that the presence of period-specific parameters leads the CD test statistic to diverge as the time dimension of the sample grows. This result holds even if cross-section dependence is correctly accounted for and hence constitutes an example of the inciden…
A Linear Estimator for Factor-Augmented Fixed-T Panels With Endogenous Regressors
A novel method-of-moments approach is proposed for the estimation of factor-augmented panel data models with endogenous regressors when T is fixed. The underlying methodology involves approximating the unobserved common factors using observed factor proxies. The resulting moment conditions are linear in the parameters. The proposed approach addresses several issues which arise with existing nonlinear estimators that are available in fixed T panel…
Econometrics (5 obras) · Estimator (5 obras) · Mathematics (5 obras) · Spatial and Panel Data Analysis (5 obras) · Statistics (5 obras) · Computer Science (4 obras) · Fiscal Policy and Economic Growth (3 obras) · Monetary Policy and Economic Impact (3 obras) · Panel data (3 obras) · Null hypothesis (2 obras)