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Econometrics
Volume 10
Issue 3
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Econometrics, Volume 10, Issue 3 (September 2022) – 4 articles

Cover Story (view full-size image): CCE is one of the most common methods with which to estimate panels with multifactor error structures. In this paper, we extend the CCE estimation to a dynamic panel data model with nonstationary multifactor error structures and establish the associated asymptotics. Monte Carlo simulations and empirical examples are also conducted to consider the finite sample performances of CCE estimations. View this paper
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17 pages, 3908 KiB  
Article
Modelling and Diagnostics of Spatially Autocorrelated Counts
by Robert C. Jung and Stephanie Glaser
Econometrics 2022, 10(3), 31; https://doi.org/10.3390/econometrics10030031 - 13 Sep 2022
Viewed by 2059
Abstract
This paper proposes a new spatial lag regression model which addresses global spatial autocorrelation arising from cross-sectional dependence between counts. Our approach offers an intuitive interpretation of the spatial correlation parameter as a measurement of the impact of neighbouring observations on the conditional [...] Read more.
This paper proposes a new spatial lag regression model which addresses global spatial autocorrelation arising from cross-sectional dependence between counts. Our approach offers an intuitive interpretation of the spatial correlation parameter as a measurement of the impact of neighbouring observations on the conditional expectation of the counts. It allows for flexible likelihood-based inference based on different distributional assumptions using standard numerical procedures. In addition, we advocate the use of data-coherent diagnostic tools in spatial count regression models. The application revisits a data set on the location choice of single unit start-up firms in the manufacturing industry in the US. Full article
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41 pages, 2020 KiB  
Article
A Parsimonious Test of Constancy of a Positive Definite Correlation Matrix in a Multivariate Time-Varying GARCH Model
by Jian Kang, Johan Stax Jakobsen, Annastiina Silvennoinen, Timo Teräsvirta and Glen Wade
Econometrics 2022, 10(3), 30; https://doi.org/10.3390/econometrics10030030 - 24 Aug 2022
Cited by 1 | Viewed by 2295
Abstract
We construct a parsimonious test of constancy of the correlation matrix in the multivariate conditional correlation GARCH model, where the GARCH equations are time-varying. The alternative to constancy is that the correlations change deterministically as a function of time. The alternative is a [...] Read more.
We construct a parsimonious test of constancy of the correlation matrix in the multivariate conditional correlation GARCH model, where the GARCH equations are time-varying. The alternative to constancy is that the correlations change deterministically as a function of time. The alternative is a covariance matrix, not a correlation matrix, so the test may be viewed as a general test of stability of a constant correlation matrix. The size of the test in finite samples is studied by simulation. An empirical example involving daily returns of 26 stocks included in the Dow Jones stock index is given. Full article
(This article belongs to the Special Issue Special Issue on Time Series Econometrics)
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27 pages, 701 KiB  
Article
Common Correlated Effects Estimation for Dynamic Heterogeneous Panels with Non-Stationary Multi-Factor Error Structures
by Shiyun Cao and Qiankun Zhou
Econometrics 2022, 10(3), 29; https://doi.org/10.3390/econometrics10030029 - 11 Aug 2022
Cited by 4 | Viewed by 3501
Abstract
In this paper, we consider the estimation of a dynamic panel data model with non-stationary multi-factor error structures. We adopted the common correlated effect (CCE) estimation and established the asymptotic properties of the CCE and common correlated effects mean group (CCEMG) estimators, as [...] Read more.
In this paper, we consider the estimation of a dynamic panel data model with non-stationary multi-factor error structures. We adopted the common correlated effect (CCE) estimation and established the asymptotic properties of the CCE and common correlated effects mean group (CCEMG) estimators, as N and T tend to infinity. The results show that both the CCE and CCEMG estimators are consistent and the CCEMG estimator is asymptotically normally distributed. The theoretical findings were supported for small samples by an extensive simulation study, showing that the CCE estimators are robust to a wide variety of data generation processes. Empirical findings suggest that the CCE estimation is widely applicable to models with non-stationary factors. The proposed procedure is also illustrated by an empirical application to analyze the U.S. cigar dataset. Full article
(This article belongs to the Special Issue Special Issue on Time Series Econometrics)
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24 pages, 1175 KiB  
Article
Structural Compressed Panel VAR with Stochastic Volatility: A Robust Bayesian Model Averaging Procedure
by Antonio Pacifico
Econometrics 2022, 10(3), 28; https://doi.org/10.3390/econometrics10030028 - 12 Jul 2022
Cited by 1 | Viewed by 2429
Abstract
This paper improves the existing literature on the shrinkage of high dimensional model and parameter spaces through Bayesian priors and Markov Chains algorithms. A hierarchical semiparametric Bayes approach is developed to overtake limits and misspecificity involved in compressed regression models. Methodologically, a multicountry [...] Read more.
This paper improves the existing literature on the shrinkage of high dimensional model and parameter spaces through Bayesian priors and Markov Chains algorithms. A hierarchical semiparametric Bayes approach is developed to overtake limits and misspecificity involved in compressed regression models. Methodologically, a multicountry large structural Panel Vector Autoregression is compressed through a robust model averaging to select the best subset across all possible combinations of predictors, where robust stands for the use of mixtures of proper conjugate priors. Concerning dynamic analysis, volatility changes and conditional density forecasts are addressed ensuring accurate predictive performance and capability. An empirical and simulated experiment are developed to highlight and discuss the functioning of the estimating procedure and forecasting accuracy. Full article
(This article belongs to the Special Issue Special Issue on Time Series Econometrics)
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