Panel Data Models and the Uncovered Interest Parity Condition: The Role of Two-Way Unobserved Components

2016 ◽  
Vol 21 (3) ◽  
pp. 294-310 ◽  
Author(s):  
Nils Herger
Keyword(s):  
Panel Data ◽  
Data Models ◽  
Panel Data Models ◽  
Uncovered Interest Parity ◽  
Unobserved Components ◽  
Interest Parity ◽  
Parity Condition

scholarly journals Corporate Governance and Pyramidal Ownership: The Role of Novo Mercado

2018 ◽  
Vol 16 (1) ◽  
pp. 5
Author(s):  
Dante Mendes Aldrighi ◽  
Fernando Antonio Slaibe Postali ◽  
Maria Dolores Montoya Diaz
Keyword(s):  
Corporate Governance ◽  
Panel Data ◽  
Fixed Effects ◽  
Financial Constraints ◽  
Data Models ◽  
Panel Data Models ◽  
Controlling Shareholders ◽  
Binary Dependent Variable ◽  
Pyramidal Ownership

The literature has not reached a consensus on the motivation and implications of pyramidal ownership schemes. For some, such arrangements make it easier for controlling shareholders to expropriate outside investors. More recently, some studies have challenged this view and emphasized that their rationale lies in overcoming financial constraints. This paper focuses on whether firms owned through pyramidal schemes are more likely to be listed on the “Novo Mercado,” the Brazilian stock exchange’s premium listing segment created in 2000, which prohibits firms from issuing non-voting shares. We built a dataset of ownership data with annual observations for a panel of firms over the period 2003-2010 by hand-collecting data drawn from reports that firms submit periodically to the Brazilian securities regulator (CVM). Estimating fixed effects non-linear panel data models of a binary dependent variable, we find that firms listed on the Novo Mercado are less likely to be owned through a pyramid arrangement, result which appears to be consistent with the expropriation view.

NONLINEAR PANEL DATA MODELS WITH DISTRIBUTION-FREE CORRELATED RANDOM EFFECTS

2021 ◽  
pp. 1-25
Author(s):  
Yu-Chin Hsu ◽  
Ji-Liang Shiu
Keyword(s):  
Panel Data ◽  
Data Model ◽  
Conditional Distribution ◽  
Unobserved Heterogeneity ◽  
Random Effect ◽  
Data Models ◽  
Panel Data Model ◽  
Panel Data Models ◽  
Likelihood Functions ◽  
Correlated Random Effects

Under a Mundlak-type correlated random effect (CRE) specification, we first show that the average likelihood of a parametric nonlinear panel data model is the convolution of the conditional distribution of the model and the distribution of the unobserved heterogeneity. Hence, the distribution of the unobserved heterogeneity can be recovered by means of a Fourier transformation without imposing a distributional assumption on the CRE specification. We subsequently construct a semiparametric family of average likelihood functions of observables by combining the conditional distribution of the model and the recovered distribution of the unobserved heterogeneity, and show that the parameters in the nonlinear panel data model and in the CRE specification are identifiable. Based on the identification result, we propose a sieve maximum likelihood estimator. Compared with the conventional parametric CRE approaches, the advantage of our method is that it is not subject to misspecification on the distribution of the CRE. Furthermore, we show that the average partial effects are identifiable and extend our results to dynamic nonlinear panel data models.

scholarly journals Spatiotemporal Econometrics Models for Old Age Mortality in Europe

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1061
Author(s):  
Patricia Carracedo ◽  
Ana Debón
Keyword(s):  
Panel Data ◽  
Goodness Of Fit ◽  
Population Aging ◽  
European Population ◽  
Data Models ◽  
European Countries ◽  
Mortality Data ◽  
Panel Data Models ◽  
Estimation Of Parameters ◽  
Advantages And Disadvantages

In the past decade, panel data models using time-series observations of several geographical units have become popular due to the availability of software able to implement them. The aim of this study is an updated comparison of estimation techniques between the implementations of spatiotemporal panel data models across MATLAB and R softwares in order to fit real mortality data. The case study used concerns the male and female mortality of the aged population of European countries. Mortality is quantified with the Comparative Mortality Figure, which is the most suitable statistic for comparing mortality by sex over space when detailed specific mortality is available for each studied population. The spatial dependence between the 26 European countries and their neighbors during 1995–2012 was confirmed through the Global Moran Index and the spatiotemporal panel data models. For this reason, it can be said that mortality in European population aging not only depends on differences in the health systems, which are subject to national discretion but also on supra-national developments. Finally, we conclude that although both programs seem similar, there are some differences in the estimation of parameters and goodness of fit measures being more reliable MATLAB. These differences have been justified by detailing the advantages and disadvantages of using each of them.

Fitting partially linear functional-coefficient panel-data models with Stata

2020 ◽  
Vol 20 (4) ◽  
pp. 976-998
Author(s):  
Kerui Du ◽  
Yonghui Zhang ◽  
Qiankun Zhou
Keyword(s):  
Monte Carlo ◽  
Panel Data ◽  
Fixed Effects ◽  
Linear Functional ◽  
Semiparametric Estimation ◽  
Data Models ◽  
Panel Data Models ◽  
Panel Models ◽  
Partially Linear ◽  
Functional Coefficient

In this article, we describe the implementation of fitting partially linear functional-coefficient panel models with fixed effects proposed by An, Hsiao, and Li [2016, Semiparametric estimation of partially linear varying coefficient panel data models in Essays in Honor of Aman Ullah ( Advances in Econometrics, Volume 36)] and Zhang and Zhou (Forthcoming, Econometric Reviews). Three new commands xtplfc, ivxtplfc, and xtdplfc are introduced and illustrated through Monte Carlo simulations to exemplify the effectiveness of these estimators.

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