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.

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.

scholarly journals TESTING HOMOGENEITY IN PANEL DATA MODELS WITH INTERACTIVE FIXED EFFECTS

2013 ◽  
Vol 29 (6) ◽  
pp. 1079-1135 ◽  
Author(s):  
Liangjun Su ◽  
Qihui Chen
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Data Models ◽  
Development Economic ◽  
Strong Mixing ◽  
Panel Data Models ◽  
Finite Sample ◽  
Test Statistic ◽  
Lm Test ◽  
Interactive Fixed Effects

This paper proposes a residual-based Lagrange Multiplier (LM) test for slope homogeneity in large-dimensional panel data models with interactive fixed effects. We first run the panel regression under the null to obtain the restricted residuals and then use them to construct our LM test statistic. We show that after being appropriately centered and scaled, our test statistic is asymptotically normally distributed under the null and a sequence of Pitman local alternatives. The asymptotic distributional theories are established under fairly general conditions that allow for both lagged dependent variables and conditional heteroskedasticity of unknown form by relying on the concept of conditional strong mixing. To improve the finite-sample performance of the test, we also propose a bootstrap procedure to obtain the bootstrap p-values and justify its validity. Monte Carlo simulations suggest that the test has correct size and satisfactory power. We apply our test to study the Organization for Economic Cooperation and Development economic growth model.

Estimating the Impact of Avian Flu on International Tourism Demand Using Panel Data

2009 ◽  
Vol 15 (3) ◽  
pp. 501-511 ◽  
Author(s):  
Hsiao-I Kuo ◽  
Chia-Lin Chang ◽  
Bing-Wen Huang ◽  
Chi-Chung Chen ◽  
Michael McAleer
Keyword(s):  
Panel Data ◽  
Pandemic Influenza ◽  
Fixed Effects ◽  
Data Models ◽  
International Tourism ◽  
Panel Data Models ◽  
Tourism Demand ◽  
Avian Flu ◽  
Potential Risks ◽  
The Impact

This paper investigates the impacts of avian flu on global and Asian tourism using panel data procedures. Both static and dynamic fixed effects panel data models are adopted to estimate the impacts of this infectious disease. The empirical results from static and dynamic fixed effects panel data models are consistent and indicate that the number of affected poultry outbreaks has significant impacts on the international tourism of global and Asian affected countries. The high mortality rate among humans, the potential of a global flu pandemic and some media frenzy with hype and speculation might adversely affect the images of these infected destinations as a safe tourist destination. Moreover, it was found that the average damage to Asian tourism was more serious, which might have been induced by an ineffective suppression in numerous Asian infected countries. In addition, Asia was the earliest affected region and the area infected most seriously by avian flu, both in humans and in poultry. Since the potential risks and damage arising from avian flu and the subsequent pandemic influenza are much greater than for previous diseases, the need to take necessary precautions in the event of an outbreak of avian flu and pandemic influenza warrants further attention and action in modelling and managing international tourism demand and risk.

Bootstrap LM Tests for Spatial Dependence in Panel Data Models with Fixed Effects

2019 ◽  
Vol 7 (4) ◽  
pp. 330-343
Author(s):  
Bianling Ou ◽  
Zhihe Long ◽  
Wenqian Li
Keyword(s):  
Panel Data ◽  
Spatial Dependence ◽  
Fixed Effects ◽  
Orthogonal Transformation ◽  
Transformation Method ◽  
Data Models ◽  
Panel Data Models ◽  
Significance Level ◽  
Lm Tests ◽  
Nominal Significance

Abstract This paper applies bootstrap methods to LM tests (including LM-lag test and LM-error test) for spatial dependence in panel data models with fixed effects, and removes fixed effects based on orthogonal transformation method proposed by Lee and Yu (2010). The consistencies of LM tests and their bootstrap versions are proved, and then some asymptotic refinements of bootstrap LM tests are obtained. It shows that the convergence rate of bootstrap LM tests is O((NT)−2) and that of fast double bootstrap LM tests is O((NT)−5/2). Extensive Monte Carlo experiments suggest that, compared to aysmptotic LM tests, the size of bootstrap LM tests gets closer to the nominal level of signifiance, and the power of bootstrap LM tests is higher, especially in the cases with small spatial correlation. Moreover, when the error is not normal or with heteroskedastic, asymptotic LM tests suffer from severe size distortion, but the size of bootstrap LM tests is close to the nominal significance level. Bootstrap LM tests are superior to aysmptotic LM tests in terms of size and power.

Quantile regression for panel data models with fixed effects under random censoring

2019 ◽  
Vol 49 (18) ◽  
pp. 4430-4445
Author(s):  
Dai Xiaowen ◽  
Jin Libin ◽  
Tian Yuzhu ◽  
Tian Maozai ◽  
Tang Manlai
Keyword(s):  
Panel Data ◽  
Quantile Regression ◽  
Fixed Effects ◽  
Data Models ◽  
Panel Data Models ◽  
Random Censoring
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