Focused Information Criterion and Model Averaging for Large Panels With a Multifactor Error Structure

Yin, Shou Yung; Liu, Chu An; Lin, Chang Ching

doi:10.1080/07350015.2019.1623044

Cited by 6 publications

(16 citation statements)

References 65 publications

(79 reference statements)

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“…This way we can easily compare our results with those of Yin et al. (2021). The results for the pooled estimators are presented in Table 7.…”

Section: Empirical Illustrationsupporting

confidence: 62%

“…As the proposed procedure mostly addresses the way factor proxies

{\overset{bold-italicF}{true}}_{r}

are constructed (and corresponding sampling uncertainty), and can be used for any model that uses

\overset{bold-italicZ}{true}

for factor proxies, for example, Focused Information Criterion based model averaging of Yin et al. (2021), the gravity model of Desbordes and Eberhardt (2019), or the discrete choice model of Boneva and Linton (2017).…”

Section: Discussion Of the Main Resultsmentioning

confidence: 99%

“…In what follows we use the empirical strategy of Yin et al. (2021) to investigate the potential causes of the historically increasing wage inequality between high‐skilled and low‐skilled workers in the US manufacturing industries. The empirical specification they consider is a linear regression model of the form:

normalln ((), \frac{w_{L . i, t}}{w_{H . i, t}}) = α_{i} + β_{1 i} normalln false(σ_{i, t} false) + β_{2 i} normalln ((), \frac{H_{i, t}}{L_{i, t}}) + θ_{i}^{'} {bold-italicz}_{i, t} + λ_{i}^{'} {bold-italicf}_{t} + ε_{i, t},

where the variable

\frac{w_{L . i, t}}{w_{H . i, t}}

is the relative wage of low‐skilled workers to high skilled workers.…”

Section: Empirical Illustrationmentioning

confidence: 99%

“…Among other models, Yin et al. (2021) estimated the “full model” in Equation (), as well as the “narrow model” without any controls using the CCE‐MG estimator. Motivated by their setup we estimate these two specifications also using the regularized versions of the CCEP and the CCE‐MG estimators.…”

Section: Empirical Illustrationmentioning

confidence: 99%

“…In our empirical illustration, we re‐evaluate the results from the recent studies in Voigtländer (2014) and Yin et al. (2021), and investigate the causes of the historically increasing wage inequality between high‐skilled and low‐skilled workers in the US manufacturing industries. Our procedure provides strong evidence that, irrespective of the setup considered, the number of the underlying factors is small in comparison with the total number of cross‐section averages.…”

Section: Introductionmentioning

confidence: 99%

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A regularization approach to common correlated effects estimation

Juodis

2022

J of Applied Econometrics

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Summary 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 be solved once cross‐section averages are appropriately regularized, thus extending the applicability of the CCE setup. As the standard plug‐in variance estimators are not able to account for all sources of estimation uncertainty, we suggest the use of cross‐section bootstrap to construct confidence intervals. The proposed procedure is illustrated both using real and simulated data.

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“…This way we can easily compare our results with those of Yin et al. (2021). The results for the pooled estimators are presented in Table 7.…”

Section: Empirical Illustrationsupporting

confidence: 62%

“…As the proposed procedure mostly addresses the way factor proxies

{\overset{bold-italicF}{true}}_{r}

are constructed (and corresponding sampling uncertainty), and can be used for any model that uses

\overset{bold-italicZ}{true}

Section: Discussion Of the Main Resultsmentioning

confidence: 99%

normalln ((), \frac{w_{L . i, t}}{w_{H . i, t}}) = α_{i} + β_{1 i} normalln false(σ_{i, t} false) + β_{2 i} normalln ((), \frac{H_{i, t}}{L_{i, t}}) + θ_{i}^{'} {bold-italicz}_{i, t} + λ_{i}^{'} {bold-italicf}_{t} + ε_{i, t},

where the variable

\frac{w_{L . i, t}}{w_{H . i, t}}

is the relative wage of low‐skilled workers to high skilled workers.…”

Section: Empirical Illustrationmentioning

confidence: 99%

Section: Empirical Illustrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A regularization approach to common correlated effects estimation

Juodis

2022

J of Applied Econometrics

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A model‐averaging treatment of multiple instruments in Poisson models with errors

Zhang

2021

Can J Statistics

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We analyze Poisson regression when covariates contain measurement errors and when multiple potential instrumental variables are available. Without empirical knowledge to select the most suitable variable as an instrument, we propose a novel model-averaging approach to resolve this issue. We prescribe an implementation and establish its optimality in terms of minimizing prediction risk. We further show that, as long as one model is correctly specified among all potential instrumental variable models, our method will lead to consistent prediction. The performance of our method is illustrated through simulations and a movie sales example.

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