Grouped Patterns of Heterogeneity in Panel Data

Bonhomme, Stéphane; Manresa, Elena

doi:10.3982/ecta11319

Cited by 363 publications

(520 citation statements)

References 39 publications

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“…A particular econometric innovation with respect to the existing literature is that we use a group xed eects estimator (Bonhomme and Manresa, 2015) as opposed to a standard xed eects estimator. This grouped xed eects estimator takes into account that dierent regions of the world adopt clean technologies at dierent rates.…”

Section: Introductionmentioning

confidence: 99%

The Trade-off Between Income Inequality and Carbon Dioxide Emissions

Grunewald

Klasen

Martínez‐Zarzoso

et al. 2017

Ecological Economics

261

184

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Based on a substantially larger data set (in both regional and temporal coverage) than the existing literature, we investigate the theoretically ambiguous link between income inequality and per capita emissions using cross-country panel data. We nd that the relationship depends on the level of income. Using an arguably superior group-xed eects estimator, we show that for low and middle-income economies, higher income inequality is associated with lower carbon emissions while in upper middle-income and high-income economies, higher income inequality increases per capita emissions. The result is robust to the inclusion of plausible transmission variables as well as dierent data sources or aggregations.JEL codes: Q0, Q1, Q3

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Section: Introductionmentioning

confidence: 99%

The Trade-off Between Income Inequality and Carbon Dioxide Emissions

Grunewald

Klasen

Martínez‐Zarzoso

et al. 2017

Ecological Economics

261

184

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“…By using Bonhomme and Manresa's (2015) grouped fixed effects algorithm, we do not impose any a priori structure on the group assignment. Third, we use some exogenous variations in military death rates across départements as an instrument along with the difference-indifferences strategy.…”

Section: B Robustness Checksmentioning

confidence: 99%

“…Importantly, we do not impose any a priori structure on group membership such as geographic clustering. Instead, we estimate group membership from the data by using Bonhomme and Manresa's (2015) grouped fixed effects algorithm. Conditional on specifying the total number of groups, the algorithm optimally groups départements with the most similar time profiles in female labor participation, net of the correlation with military death rates and other covariates.…”

Section: B Robustness Checksmentioning

confidence: 99%

“…First, we control for département-specific trends in female labor participation. Second, using Bonhomme and Manresa's (2015) grouped fixed effects, we allow for time-varying heterogeneity across groups of départements, where we do not impose any a priori structure on group membership. Third, exploiting the fact that the recruitment process of the army led to randomness in military death rates, we use an instrumental variables approach combined with the difference-in-differences strategy.…”

Section: Introductionmentioning

confidence: 99%

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The Missing Men: World War I and Female Labor Participation

Boehnke

Gay

2017

SSRN Journal

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We explore the effect of military fatalities from World War I on female labor participation in post-war France. We build a unique dataset containing individual level information for all 1.3 million fallen soldiers, and find that the tightness of the marriage market along with negative income shocks generated by the scarcity of men induced many young single women and older widows to enter the labor force permanently after the war, especially in the industrial sector. These findings are robust to alternative empirical strategies, including an instrumental variables strategy based on idiosyncrasies generated by the recruitment process of the army. (JEL J12, J16, J22, N34)

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“…For example, the function can rely on an arbitrary distance in a similar way, as it is used for random cross-section e ects in panel models. Since our model relies on discrete factor loadings, it is also related to models of grouped patterns of heterogeneity in panel data; see, for example, Bonhomme and Manresa (2015). More broadly, we can relate our model with its challenges in parameter estimation to the literature on mixture state space models; see Frühwirth-Schnatter (2006) for a textbook treatment.…”

Section: Introductionmentioning

confidence: 99%

The Dynamic Factor Network Model with an Application to Global Credit-Risk

Bräuning

Koopman

2016

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract We introduce a dynamic network model with probabilistic link functions that depend on stochastically time-varying parameters. We adopt the widely used blockmodel framework and allow the high-dimensional vector of link probabilities to be a function of a low-dimensional set of dynamic factors. The resulting dynamic factor network model is straightforward and transparent by nature. However, parameter estimation, signal extraction of the dynamic factors, and the econometric analysis generally are intricate tasks for which simulation-based methods are needed. We provide feasible and practical solutions to these challenging tasks, based on a computationally e cient importance sampling procedure to evaluate the likelihood function. A Monte Carlo study is carried out to provide evidence of how well the methods work. In an empirical study, we use the novel framework to analyze a database of significance-flags of Granger causality tests for pair-wise credit default swap spreads of 61 di erent banks from the United States and Europe. Based on our model, we recover two groups that we characterize as "local" and "international" banks. The credit-risk spillovers take place between banks, from the same and from di erent groups, but the intensities change over time as we have witnessed during the financial crisis and the sovereign debt crisis. Terms of use: Documents in

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Grouped Patterns of Heterogeneity in Panel Data

Cited by 363 publications

References 39 publications

The Trade-off Between Income Inequality and Carbon Dioxide Emissions

The Trade-off Between Income Inequality and Carbon Dioxide Emissions

The Missing Men: World War I and Female Labor Participation

The Dynamic Factor Network Model with an Application to Global Credit-Risk

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