scholarly journals Associations of employment sector and occupational exposures with full and part-time sickness absence: random and fixed effects analyses on panel data

2021 ◽  
Author(s):  
Elli Hartikainen ◽  
Svetlana Solovieva ◽  
Eira Viikari-Juntura ◽  
Taina Leinonen
Keyword(s):  
Panel Data ◽  
Sickness Absence ◽  
Fixed Effects ◽  
Occupational Exposures ◽  
Employment Sector ◽  
Part Time

Using information from singletons in fixed-effects estimation: xtfesing

2020 ◽  
Vol 20 (4) ◽  
pp. 965-975
Author(s):  
Laura Magazzini ◽  
Randolph Luca Bruno ◽  
Marco Stampini
Keyword(s):  
Panel Data ◽  
Method Of Moments ◽  
Fixed Effects ◽  
Generalized Method Of Moments ◽  
Panel Data Regression ◽  
Data Regression ◽  
Method Of Moments Estimator ◽  
Fixed Effects Estimation

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).

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.

Determinants of Corporate Cash Holdings

2017 ◽  
Vol 18 (2) ◽  
pp. 416-427 ◽  
Author(s):  
Yogesh Maheshwari ◽  
K.T. Vigneswara Rao
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Capital Expenditure ◽  
Cash Holdings ◽  
Working Capital ◽  
Financial Policies ◽  
Panel Data Regression ◽  
Strong Positive Relationship ◽  
Development Expenditure ◽  
Corporate Cash Holdings

This article aims at examining the financial determinants of corporate cash holdings. The study employs panel data regression method. It uses the fixed-effects method based on Hausman test results for the estimation of panel data model. This study has implications that are beneficial for the business managers to have a better understanding and appreciation of the role and importance of the determinants of corporate cash holdings in formulating and evaluating the corporate financial policies. The results of the study indicate a strong positive relationship between cash holdings and cash flow, dividend payment, market-to-book ratio, net debt issuance and net equity issuance of the sample firms. It is also found that the cash holdings of these firms are negatively affected by net working capital, leverage, research and development expenditure as well as capital expenditure of the firm. The article will help researchers as well as managers to understand as to what motivates the firms to hold cash, given the fact that despite being often termed as a non-earning asset, firms generally hold more cash than their normal working capital requirement.

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.

Assessing the Impact of the Factors in the Digital Transformation on the Regional Economic Growth

REGIONOLOGY ◽  
2021 ◽  
Vol 29 (3) ◽  
pp. 486-510
Author(s):  
Tatyana V. Mirolyubova ◽  
Marina V. Radionova
Keyword(s):  
Economic Growth ◽  
Panel Data ◽  
Fixed Effects ◽  
Digital Transformation ◽  
Regional Economic Growth ◽  
Regional Economic ◽  
The Russian Federation ◽  
Regional Product ◽  
The Impact ◽  
Regions Of Russia

Introduction. The scientific problem under consideration is of particular relevance due to the need to assess the impact of the factors in the digital transformation of the regional economy and in the economic growth on the economic development of the regions of the Russian Federation. Based on the research conducted, the article presents an econometric assessment of the dependence of the level of the gross regional product per capita in the regions of Russia on such factors as digital labor and digital capital. Materials and Methods. The authors analyzed panel data from the Federal State Statistics Service covering 87 regions of Russia for the period from 2010 to 2018. The research methodology is based on the use of the Cobb–Douglas production function, statistical and correlation data analysis, as well as on econometric methods for studying panel data. Results. To analyze the impact of the digital transformation of the economy on the regional economic growth of the regions of Russia, various models based on panel data have been considered, such as the pooled model, fixed effects models, random effects models, as well as time-varying effects models using dummy variables. Based on statistical criteria, the best model has been chosen and conclusions have been drawn about the nature of the impact of the digital transformation indicators on the gross regional product per capita in the regions of Russia. Discussion and Conclusion. The results of econometric modeling have demonstrated that digital factors in economic growth (digital labor, digital capital), along with common factors in economic growth (labor and capital), affect the regional economic growth. According to the regional data for the period from 2010 to 2018, the time fixed effects model has proved to be the best model of the impact of the factors in economic growth and digital transformation on the economic development of the regions of the Russian Federation. The research results can be used when developing a public policy aimed at stimulating the digital transformation of the regional economy.

scholarly journals Nonparametric Estimation of a Conditional Quantile Function in a Fixed Effects Panel Data Model

2018 ◽  
Vol 11 (3) ◽  
pp. 44 ◽  
Author(s):  
Karen Yan ◽  
Qi Li
Keyword(s):  
Panel Data ◽  
Data Model ◽  
Numerical Optimization ◽  
Fixed Effects ◽  
Quantile Function ◽  
Panel Data Model ◽  
Nonparametric Method ◽  
Conditional Quantile ◽  
Finite Samples ◽  
Conditional Quantile Function

This paper develops a nonparametric method to estimate a conditional quantile function for a panel data model with an additive individual fixed effects. The proposed method is easy to implement, it does not require numerical optimization and automatically ensures quantile monotonicity by construction. Monte Carlo simulations show that the proposed estimator performs well in finite samples.

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