scholarly journals Random Effects Endogeneity: A Variable Pretest

2021 ◽  
pp. 19-25
Author(s):  
Mitch Kunce
Keyword(s):  
Panel Data ◽  
Prior Knowledge ◽  
Random Effects ◽  
Instrumental Variable ◽  
Jel Classification ◽  
Explanatory Variables ◽  
Error Components ◽  
Latent Group ◽  
Instrumental Variable Estimator ◽  
Group Effects

Abstract The appealing but complex Hausman and Taylor (1981) random effects (instrumental variable) estimator requires prior knowledge that certain explanatory variables in a panel are uncorrelated with the latent group effects. The purpose of this examination is to outline a tractable variable pretest that facilitates the initial sorting of regressors as likely exogenous or endogenous. The variable pretest proposed herein builds on the pretest estimator suggested by Baltagi et al (2003) by providing the necessary foundation for regressor identification. Extensions are suggested for the two-way error components construct. Keywords: Panel data, Random effects, Variable pretest, Hausman-Taylor. JEL Classification: C12, C13, C23.

scholarly journals A DESIGUALDADE DE RENDA E A EDUCAÇÃO PODEM EXPLICAR A CRIMINALIDADE? uma análise para os estados brasileiros

2015 ◽  
Vol 18 (2) ◽  
pp. 343
Author(s):  
Renata Costa de Carvalho ◽  
Fernando Henrique Taques
Keyword(s):  
Panel Data ◽  
Random Effects ◽  
Fixed Effects ◽  
Random Effect ◽  
Random Effect Model ◽  
Federal District ◽  
Economics Of Crime ◽  
Fixed Effects Model ◽  
Explanatory Variables ◽  
Relevant Today

O presente trabalho tem como objetivo entender a Economia do Crime, um tema muito pertinente nos dias de hojenos estados brasileiros. A partir de levantamento teórico e testes empíricos, verifica a hipótese de que a taxa de homicídios estápositivamente relacionada com o nível de desigualdade de renda e negativamente com o nível de educação, despesa com segurançae renda. Mostra que o teste foi realizado para todos os estados brasileiros, exceto Distrito Federal, no período de nove anos, entreos anos 2001 e 2009. Apresenta uma metodologia que está baseada no modelo econométrico em dados em painel com efeitos fixose efeitos aleatórios, ressaltando que o estimador usado no modelo foi o Mínimo Quadrado Ordinários (MQO). Indica, ainda, que o resultado encontrado foi que o modelo de efeitos fixos apresentou estatística mais significativa, porém o modelo de efeitos aleatórios apresentou o sinal esperado em seu coeficiente em 50% das variáveis explicativas. Por fim, aponta que o teste de Hausman provouque o modelo de efeito aleatório é melhor para explicar a hipótese.Palavras-chave: Crime, despesa com segurança, dados em painel.INCOME INEQUALITY AND EDUCATION CAN EXPLAIN CRIMINALITY? an analysis for the Brazilian statesAbstract: This study aims to understand the Economics of Crime, an issue very relevant today in Brazilian states. From theoretical and empirical research and test, it will analyze the hypothesis that the homicide rate is positively related to the level of income inequalityand negatively with the level of education, expenses with security and income. The test will be conducted for all states, except the Federal District, in the nine years between 2001 and 2009. The methodology is based on the econometric model on panel data withfixed effects and random effects estimator which model was used in the Ordinary Minimum Square (OLS). As the result has found that the fixed effects model showed the most meaningful statistics, the random effects model showed the expected sign on its coefficientin 50 % of the explanatory variables, while the Hausman test proved that the random effect model is better to explain the hypothesis.Key words: Crime, safety expenses, panel data. 

scholarly journals Factors of investments in automobile companies’ R&D

2016 ◽  
Vol 13 (4) ◽  
pp. 27-32
Author(s):  
Dmitry Nikolaev ◽  
Lena Akimova ◽  
Ilya Mylov ◽  
Daria Kareva ◽  
Timur Azimov
Keyword(s):  
Panel Data ◽  
Key Words ◽  
Market Share ◽  
Random Effects ◽  
Automobile Industry ◽  
Fixed Effects ◽  
Regression Models ◽  
Jel Classification ◽  
Total Revenue ◽  
Income Share

This paper studies what determines investment in R&D at major automobile companies of Japan and Germany as of from 2001 to 2015. For that purpose, accounting records and OECD and WWID databases were used. Based on regression models of panel data with random effects and fixed effects, some factors considerably influencing upon investment in R&D were found out. Such factors include: the total assets, amount of exports, market share, return on total assets, total revenue, profits and the income share of the wealthiest residents. Key words: factors of investment in R&D, automobile industry, Japan, Germany. JEL Classification: G39

A mechanism to improve the allocation of public investment funds

2020 ◽  
Vol 26 (7) ◽  
pp. 1522-1533
Author(s):  
A.V. Larionov
Keyword(s):  
Panel Data ◽  
Random Effects ◽  
Public Investment ◽  
Allocative Efficiency ◽  
Investment Funds ◽  
The Public ◽  
Panel Data Regression ◽  
Data Regression ◽  
Efficiency And Effectiveness ◽  
Effective System

Subject. This article deals with the issue of improving the public investment allocative efficiency. Objectives. The article aims to develop an approach to improve the efficiency and effectiveness of public investment in the economy. Methods. The study is based on a panel data regression with random effects. Conclusions and Relevance. All sectors of the economy have different demand for investment resources attracted, determined by operational and technological aspects. The results of the study can be used to develop an effective system of public investment.

scholarly journals Estimating latent group structure in time-varying coefficient panel data models

2019 ◽  
Author(s):  
Jia Chen
Keyword(s):  
Panel Data ◽  
Data Models ◽  
Panel Data Models ◽  
Time Varying ◽  
Clustering Method ◽  
Varying Coefficient ◽  
Varying Coefficients ◽  
Time Varying Coefficients ◽  
Latent Group ◽  
Group Structures

Summary This paper studies the estimation of latent group structures in heterogeneous time-varying coefficient panel data models. While allowing the coefficient functions to vary over cross-sections provides a good way to model cross-sectional heterogeneity, it reduces the degree of freedom and leads to poor estimation accuracy when the time-series length is short. On the other hand, in a lot of empirical studies, it is not uncommon to find that heterogeneous coefficients exhibit group structures where coefficients belonging to the same group are similar or identical. This paper aims to provide an easy and straightforward approach for estimating the underlying latent groups. This approach is based on the hierarchical agglomerative clustering (HAC) of kernel estimates of the heterogeneous time-varying coefficients when the number of groups is known. We establish the consistency of this clustering method and also propose a generalised information criterion for estimating the number of groups when it is unknown. Simulation studies are carried out to examine the finite-sample properties of the proposed clustering method as well as the post-clustering estimation of the group-specific time-varying coefficients. The simulation results show that our methods give comparable performance to the penalised-sieve-estimation-based classifier-LASSO approach by Su et al. (2018), but are computationally easier. An application to a panel study of economic growth is also provided.

Personality, Prior Knowledge, Social Capital and Entrepreneurial Intentions: Entrepreneurial Alertness as Mediator

2014 ◽  
Vol 2 (1) ◽  
pp. 68-78
Author(s):  
Wei Lee Lim ◽  
Yvonne Lean-Ee Lee ◽  
Ravindran Ramasamy
Keyword(s):  
Social Capital ◽  
Personality Traits ◽  
Prior Knowledge ◽  
Structural Equation ◽  
Entrepreneurial Intention ◽  
Jel Classification ◽  
Entrepreneurial Intentions ◽  
Mediation Effect ◽  
Kuala Lumpur ◽  
Entrepreneurial Alertness

Objective – This empirical study aims to examine the role of entrepreneurial alertness as a mediator to personality traits, prior knowledge and social capital in influencing the potential of entrepreneurial intention. Methodology/Technique – A total of 212 questionnaires were personally distributed in the Kuala Lumpur area. The data were analyzed using structural equation modelling (SEM) employing the Baron and Kenny method with the bootstrapping method in testing for mediation effect. Findings – The findings concluded that there are significant relationships between the three variables; prior knowledge, social capital and personality traits with the entrepreneurial intention which is mediated by entrepreneurial alertness. Social capital and prior knowledge in the form of entrepreneurships courses and entrepreneurial experience have a positive relationship with entrepreneurial intention and hence attention should be directed towards them. Novelty – Demonstrate the originality or value of the paper which makes it different from prior studies. Type of Paper: Empirical Keywords: Entrepreneurial intention; Alertness; Prior knowledge; Social capital; Personality; Mediator. JEL Classification: L26, L31.

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