A Method of Moments Estimator of Tail Dependence in Elliptical Copula Models

In this article, we describe the xtfesing command. The command implements a generalized method of moments estimator that allows exploiting singleton information in fixed-effects panel-data regression as in Bruno, Magazzini, and Stampini (2020, Economics Letters 186: Article 108519).

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Dependence structure between money and economic activity: A Markov-switching copula VEC approach

Macroeconomic Dynamics ◽

10.1017/s1365100521000250 ◽

2021 ◽

pp. 1-17

Author(s):

Apostolos Serletis ◽

Libo Xu

Keyword(s):

Error Correction ◽

Economic Activity ◽

Regime Switching ◽

Markov Switching ◽

Tail Dependence ◽

Error Correction Model ◽

Dependence Structure ◽

Copula Models ◽

Correction Model ◽

Dependence Structures

Abstract This paper examines correlation and dependence structures between money and the level of economic activity in the USA in the context of a Markov-switching copula vector error correction model. We use the error correction model to focus on the short-run dynamics between money and output while accounting for their long-run equilibrium relationship. We use the Markov regime-switching model to account for instabilities in the relationship between money and output, and also consider different copula models with different dependence structures to investigate (upper and lower) tail dependence.

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Analysing dynamic dependence between gold and stock returns: Evidence using stochastic and full-range tail dependence copula models

Finance Research Letters ◽

10.1016/j.frl.2018.12.008 ◽

2019 ◽

Vol 31 ◽

Cited By ~ 6

Author(s):

Gideon Boako ◽

Aviral Kumar Tiwari ◽

Muazu Ibrahim ◽

Qiang Ji

Keyword(s):

Stock Returns ◽

Full Range ◽

Tail Dependence ◽

Copula Models

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A Crook is a Crook . . . But is He Still a Crook Abroad? On the Effect of Immigration on Destination-Country Corruption

German Economic Review ◽

10.1111/geer.12064 ◽

2015 ◽

Vol 16 (4) ◽

pp. 464-489 ◽

Cited By ~ 13

Author(s):

Eugen Dimant ◽

Margarete Redlin ◽

Tim Krieger

Keyword(s):

Method Of Moments ◽

Fixed Effects ◽

Generalized Method Of Moments ◽

Destination Country ◽

Estimation Methods ◽

Econometric Methodology ◽

Migration Flows ◽

The Difference ◽

Method Of Moments Estimator ◽

The Impact

AbstractThis paper analyzes the impact of migration on destination-country corruption levels. Capitalizing on a comprehensive dataset consisting of annual immigration stocks of OECD countries from 207 countries of origin for the period 1984-2008, we explore different channels through which corruption might migrate. We employ different estimation methods using fixed effects and Tobit regressions in order to validate our findings. Moreover, we also address the issue of endogeneity by using the Difference- Generalized Method of Moments estimator. Independent of the econometric methodology, we consistently find that while general migration has an insignificant effect on the destination country’s corruption level, immigration from corruption-ridden origin countries boosts corruption in the destination country. Our findings provide a more profound understanding of the socioeconomic implications associated with migration flows.

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A Method for Systemic Risk Estimation Based on CDS Indices

Review of Economic and Business Studies ◽

10.1515/rebs-2016-0018 ◽

2015 ◽

Vol 8 (1) ◽

pp. 103-124

Author(s):

Gabriel Gaiduchevici

Keyword(s):

Goodness Of Fit ◽

Risk Estimation ◽

Multivariate Time Series ◽

Tail Dependence ◽

Dependence Structure ◽

Parameter Estimates ◽

Copula Models ◽

Goodness Of Fit Tests ◽

Multi Stage ◽

Stage Estimation

AbstractThe copula-GARCH approach provides a flexible and versatile method for modeling multivariate time series. In this study we focus on describing the credit risk dependence pattern between real and financial sectors as it is described by two representative iTraxx indices. Multi-stage estimation is used for parametric ARMA-GARCH-copula models. We derive critical values for the parameter estimates using asymptotic, bootstrap and copula sampling methods. The results obtained indicate a positive symmetric dependence structure with statistically significant tail dependence coefficients. Goodness-of-Fit tests indicate which model provides the best fit to data.

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