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Econometrics
Volume 12
Issue 2
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Econometrics, Volume 12, Issue 2 (June 2024) – 4 articles

Cover Story (view full-size image): Econometrics has had an impressively successful start, with articles published by leading econometricians that are accessible with a minimum of delay. Econometrics has already established a reputation for the quality of its published papers and the fairness and consistency of the editorial process. Econometrics takes as its standard to publish research of international significance that will have a lasting impact on the direction of econometric theory and practice. It is competitive with leading journals in the field, with the advantage of timely, open-access publication.
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23 pages, 374 KiB  
Article
Stein-like Common Correlated Effects Estimation under Structural Breaks
by Shahnaz Parsaeian
Econometrics 2024, 12(2), 11; https://doi.org/10.3390/econometrics12020011 - 18 Apr 2024
Viewed by 321
Abstract
This paper develops a Stein-like combined estimator for large heterogeneous panel data models under common structural breaks. The model allows for cross-sectional dependence through a general multifactor error structure. By utilizing the common correlated effects (CCE) estimation technique, we propose a Stein-like combined [...] Read more.
This paper develops a Stein-like combined estimator for large heterogeneous panel data models under common structural breaks. The model allows for cross-sectional dependence through a general multifactor error structure. By utilizing the common correlated effects (CCE) estimation technique, we propose a Stein-like combined estimator of the CCE full-sample estimator (i.e., estimation using both the pre-break and post-break observations) and the CCE post-break estimator (i.e., estimation using only the post-break sample observations). The proposed Stein-like combined estimator benefits from exploiting the pre-break sample observations. We derive the optimal combination weight by minimizing the asymptotic risk. We show the superiority of the CCE Stein-like combined estimator over the CCE post-break estimator in terms of the asymptotic risk. Further, we establish the asymptotic properties of the CCE mean group Stein-like combined estimator. The finite sample performance of our proposed estimator is investigated using Monte Carlo experiments and an empirical application of predicting the output growth of industrialized countries. Full article
16 pages, 505 KiB  
Article
The Gini and Mean Log Deviation Indices of Multivariate Inequality of Opportunity
by Marek Kapera and Martyna Kobus
Econometrics 2024, 12(2), 10; https://doi.org/10.3390/econometrics12020010 - 17 Apr 2024
Viewed by 385
Abstract
The most common approach to measuring inequality of opportunity in income is to apply the Gini inequality index or the Mean Log Deviation (MLD) index to a smoothed distribution (i.e., a distribution of type mean incomes). We show how this approach can be [...] Read more.
The most common approach to measuring inequality of opportunity in income is to apply the Gini inequality index or the Mean Log Deviation (MLD) index to a smoothed distribution (i.e., a distribution of type mean incomes). We show how this approach can be naturally extended to include life outcomes other than income (e.g., health, education). We propose two measures: the Gini and MLD indices of multivariate inequality of opportunity. We show that they can be decomposed into the contribution of each outcome and the dependence of the outcomes. Using these measures, we calculate inequality of opportunity in health and income across European countries. Full article
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15 pages, 312 KiB  
Article
A Pretest Estimator for the Two-Way Error Component Model
by Badi H. Baltagi, Georges Bresson and Jean-Michel Etienne
Econometrics 2024, 12(2), 9; https://doi.org/10.3390/econometrics12020009 - 16 Apr 2024
Viewed by 381
Abstract
For a panel data linear regression model with both individual and time effects, empirical studies select the two-way random-effects (TWRE) estimator if the Hausman test based on the contrast between the two-way fixed-effects (TWFE) estimator and the TWRE estimator is not rejected. Alternatively, [...] Read more.
For a panel data linear regression model with both individual and time effects, empirical studies select the two-way random-effects (TWRE) estimator if the Hausman test based on the contrast between the two-way fixed-effects (TWFE) estimator and the TWRE estimator is not rejected. Alternatively, they select the TWFE estimator in cases where this Hausman test rejects the null hypothesis. Not all the regressors may be correlated with these individual and time effects. The one-way Hausman-Taylor model has been generalized to the two-way error component model and allow some but not all regressors to be correlated with these individual and time effects. This paper proposes a pretest estimator for this two-way error component panel data regression model based on two Hausman tests. The first Hausman test is based upon the contrast between the TWFE and the TWRE estimators. The second Hausman test is based on the contrast between the two-way Hausman and Taylor (TWHT) estimator and the TWFE estimator. The Monte Carlo results show that this pretest estimator is always second best in MSE performance compared to the efficient estimator, whether the model is random-effects, fixed-effects or Hausman and Taylor. This paper generalizes the one-way pretest estimator to the two-way error component model. Full article
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16 pages, 2945 KiB  
Article
Biases in the Maximum Simulated Likelihood Estimation of the Mixed Logit Model
by Maksat Jumamyradov, Murat Munkin, William H. Greene and Benjamin M. Craig
Econometrics 2024, 12(2), 8; https://doi.org/10.3390/econometrics12020008 - 27 Mar 2024
Viewed by 670
Abstract
In a recent study, it was demonstrated that the maximum simulated likelihood (MSL) estimator produces significant biases when applied to the bivariate normal and bivariate Poisson-lognormal models. The study’s conclusion suggests that similar biases could be present in other models generated by correlated [...] Read more.
In a recent study, it was demonstrated that the maximum simulated likelihood (MSL) estimator produces significant biases when applied to the bivariate normal and bivariate Poisson-lognormal models. The study’s conclusion suggests that similar biases could be present in other models generated by correlated bivariate normal structures, which include several commonly used specifications of the mixed logit (MIXL) models. This paper conducts a simulation study analyzing the MSL estimation of the error components (EC) MIXL. We find that the MSL estimator produces significant biases in the estimated parameters. The problem becomes worse when the true value of the variance parameter is small and the correlation parameter is large in magnitude. In some cases, the biases in the estimated marginal effects are as large as 12% of the true values. These biases are largely invariant to increases in the number of Halton draws. Full article
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