Random Effects Model in Panel Data: Empirical analysis based on Poverty Governance in China

This paper uses panel data of Insurance Statistics Yearbook and the National Bureau of Statistics of China in 31 provinces and cities from 2008 to 2018. The random effects model is used to study the direction and intensity of agricultural production impacts from agricultural insurance and its compensation. The collinearity robustness and endogeneity tests are carried out as the empirical results. It shows that agricultural insurance has a significant promotion effect on the agricultural output, and its influence will increase with the growth of risks. From the regional sub-sample regression results, agricultural insurance in the central and eastern of China is more significant than the western. Therefore, China should continue to vigorously promote the reform and innovation of agricultural insurance and its system, expand the coverage of agricultural insurance, and accelerate its high-quality.

Download Full-text

Why Do Household Businesses Stay Informal?

Micro, Small, and Medium Enterprises in Vietnam ◽

10.1093/oso/9780198851189.003.0007 ◽

2020 ◽

pp. 134-157

Author(s):

Thi Bich Tran ◽

Hai Anh La

Keyword(s):

Developing Countries ◽

Panel Data ◽

Random Effects ◽

Random Effects Model ◽

Qualitative And Quantitative ◽

Quantitative Studies ◽

Medium Enterprise ◽

Enterprise Surveys ◽

Net Benefits ◽

Unbalanced Panel Data

Using unbalanced panel data from the small and medium enterprise surveys in 2005, 2007, 2009, 2011, 2013, and 2015, this chapter investigates factors associated with informality in Vietnam. We assume that household businesses, especially the top tier firms, become formal either because they perceive benefits of formalization such as an increase in the household performance, or because they want to escape bribes and harassment. Using the random effects model with controlling for the pre-formalization trends, our results show that productive household businesses stay informal because net costs from tax payment may surpass net benefits from formalization. Moreover, government controls do not promote formalization, especially among the ‘upper’ tiers of informal households. Our findings raise suspicions of collusion corruption between informal households in top tiers and officials. Future steps could be qualitative and quantitative studies to investigate collusion corruption as a determinant of informality in developing countries.

Download Full-text

FORECASTING MONEY DEMAND WITH THE INCLUSION OF FINANCIAL INNOVATION FOR THE ASSOCIATION OF SOUTHEAST ASIAN NATIONS: EVIDENCE FROM PANEL DATA ANALYSIS

Advanced International Journal of Banking, Accounting and Finance ◽

10.35631/aijbaf.11006 ◽

2019 ◽

Vol 1 (1) ◽

pp. 59-74

Author(s):

Payam Mohammad Aliha ◽

Tamat Sarmidi ◽

Fathin Faizah Said

Keyword(s):

Time Series ◽

Panel Data ◽

Random Effects ◽

Money Demand ◽

Fixed Effects ◽

Random Effects Model ◽

Fixed Effects Model ◽

Demand For Money ◽

Financial Innovations ◽

The Impact

This paper investigates the impact of financial innovations on the demand for money using a dynamic panel data for 10 ASEAN member states from 2004 to 2012 and attempt to forecast the demand for money during 2013 – 2016 to compare between forecasting performance of the fixed effects model with that of random effects model and also to compare the forecasting accuracy of dynamic forecasting and static forecasting obtained from these two models. An autoregressive model by definition is when a value from a time series is regressed on previous values from that same time series. There are two types of forecasting namely dynamic forecast and static forecast. “Dynamic forecast will take previously forecasted values while static forecast will take actual values to make next step forecast. Panel effects models assist in controlling for unobserved heterogeneity when this heterogeneity is constant over time and correlated (fixed effects) or uncorrelated (random effects) with independent variables. Hausman test indicates that the random-effects model is appropriate. We use the conventional money demand that is enriched with the number of automated teller machines (ATM) to proxy for the effect of financial innovations on money demand. By comparing the magnitude of “Root Mean Squared Error” (RMSE) as a benchmark for the two forecasts (0.1164 for dynamic forecast versus 0.0635 for static forecast) we simply find out that static forecast is superior to dynamic forecast meaning that static forecast provides more accurate forecast compared to a dynamic forecast for the fixed-effects model. Therefore, we conclude the static forecast on the basis of the random-effects model provides the most accurate forecasting. The estimation result of the chosen random-effects regression also indicates the estimated coefficient of ATM is not significant meaning that ATM does not impact money demand in ASEAN countries.

Download Full-text

MODELLING AND ESTIMATING INDIVIDUAL AND FIRM EFFECTS WITH COUNT PANEL DATA

Astin Bulletin ◽

10.1017/asb.2018.19 ◽

2018 ◽

Vol 48 (3) ◽

pp. 1049-1078 ◽

Cited By ~ 2

Author(s):

Jean-François Angers ◽

Denise Desjardins ◽

Georges Dionne ◽

François Guertin

Keyword(s):

Panel Data ◽

Random Effects ◽

Fixed Effects ◽

Parametric Model ◽

Random Effects Model ◽

Road Accidents ◽

Fixed Effects Model ◽

Insurance Pricing ◽

Count Panel Data ◽

Unobservable Factors

AbstractWe propose a new parametric model for the modelling and estimation of event distributions for individuals in different firms. The analysis uses panel data and takes into account individual and firm effects in a non-linear model. Non-observable factors are treated as random effects. In our application, the distribution of accidents is affected by observable and non-observable factors from vehicles, drivers and fleets of vehicles. Observable and unobservable factors are significant to explain road accidents, which mean that insurance pricing should take into account all these factors. A fixed effects model is also estimated to test the consistency of the random effects model.

Download Full-text

Global Versus Non-Global Banks: A Capital Ratios-Based Analysis

Journal of Central Banking Theory and Practice ◽

10.2478/jcbtp-2021-0011 ◽

2021 ◽

Vol 10 (2) ◽

pp. 5-22

Author(s):

Konstantinos Drakos ◽

Ioannis Malandrakis

Keyword(s):

Panel Data ◽

Financial Crisis ◽

Random Effects ◽

Financial Institutions ◽

Random Effects Model ◽

Data Set ◽

Leverage Ratio ◽

Total Capital ◽

Capital Ratios ◽

Global Banks

Abstract This paper examines the Leverage Ratio and Total Capital Ratio of global versus non-global banks in both the pre- and post-crisis periods. A panel data set of 165 global and non-global financial institutions from 38 countries is used for the period 1999-2015 and a random effects model is employed to examine whether global banks perform better or not compared to their non-global counterparts. This study comes up with two important findings. First, global banks do not exhibit heterogeneous behaviour with respect to both ratios neither in the pre- and especially nor in the post-crisis period. Second, the Leverage Ratio is crisis-insensitive, but the Total Capital Ratio is not. Our findings encourage further research on the topic of the contribution of global banks to the financial crisis propagation (at least as far as leverage is concerned).

Download Full-text

The Impact of Foreign Direct Investment in the Western Balkan Countries - A Panel Data Analysis

International Journal of Sustainable Development and Planning ◽

10.18280/ijsdp.160619 ◽

2021 ◽

Vol 16 (6) ◽

pp. 1185-1190

Author(s):

Nexhat Shkodra ◽

Xhevat Sopi ◽

Florentina Xhelili Krasniqi

Keyword(s):

Economic Growth ◽

Foreign Direct Investment ◽

Panel Data ◽

Random Effects ◽

Direct Investment ◽

Fixed Effects ◽

Panel Data Analysis ◽

Random Effects Model ◽

Balkan Countries ◽

The Impact

Foreign Direct Investment (FDI) has a significant effect on the economic growth and development of host economies, but also on international economic integration through globalization. Particular aspects of this topic are being extensively addressed by scientific research in recent decades. The purpose of this paper is to determine whether globalization and through it the Foreign Direct Investment (FDI) has an impact on the economic growth (GDPgr) of the Western Balkan countries which are facing a transitional phase. The relation between FDI and economic growth has been analyzed by employing econometric models with panel data approach: linear regression with poled data, the Fixed Effects model, and the Random-Effects model (GLS). The study is based on panel data of six countries for the period between 2004-2018, obtained by the World Bank. The results of the Random Effects model (GLS) shown that lagged FDI has a significant impact on the economic growth (GDPgr) of the Western Balkans (p<0.05%), as well as gross capital formation (Cap) and government expenditure (Gov) whereas export (Ex) has been excluded from the model. The results also shown that there are significant differences in the factors influencing economic growth among countries in the region (LM Method - Breusch-Pagan test; p=0.02455 < 0.05).

Download Full-text

Trends in COVID-19 Infected Cases Based on Panel Data Regression Modelling

10.21203/rs.3.rs-668769/v1 ◽

2021 ◽

Author(s):

A. RAJARATHINAM ◽

P TAMILSELVAN

Keyword(s):

Panel Data ◽

Regression Model ◽

Random Effects ◽

Tamil Nadu ◽

Random Effects Model ◽

Infected Case ◽

Panel Data Regression ◽

Data Regression ◽

Tamil Nadu State ◽

The Government

Abstract Background and Objective: The novel coronavirus pandemic, known as COVID-19, could not have been more predictable; thus, the world encountered health crises and substantial economic crises. This paper analysed the trends in COVID-19 cases in October 2020 in four southern districts of Tamil Nadu state, India, using a panel regression model. Materials and Methods: Panel data on the number of COVID-19-infected cases were collected from daily bulletins published through the official website www.stopcorona.tn.gov.in maintained by the Government of Tamil Nadu state, India. Panel data regression models were employed to study the trends. EViews Ver.11. Software was used to estimate the model and its parameters. Results: In all four districts, the COVID-19-infected case data followed a normal distribution. Maximum numbers of COVID-19-infected cases were registered in Kanniyakumari, followed by Tirunelveli, Thoothukudi and Tenkasi districts. The fewest COVID-19 cases were registered in Tenkasi, followed by Tirunelveli, Thoothukudi and Kanniyakumari districts. A random effects model was found to be an appropriate model to study the trend.Conclusion: The panel data regression model is found to be more appropriate than traditional models. The Hausman test and Wald test confirmed the selection of the random effects model. The Jarque-Bera normality test ensured the normality of the residuals. In all four districts under study, the number of COVID-19 infections showed a decreasing trend at a rate of 1.68% during October 2020.

Download Full-text

Does Corruption Affect Foreign Direct Investment (FDI) Inflows in SADC Countries?

Journal of Applied Accounting and Taxation ◽

10.30871/jaat.v5i2.1873 ◽

2020 ◽

Vol 5 (2) ◽

pp. 166-174

Author(s):

Moses Garai Chamisa

Keyword(s):

Foreign Direct Investment ◽

Panel Data ◽

Random Effects ◽

Direct Investment ◽

Economic Transformation ◽

Random Effects Model ◽

Foreign Investors ◽

Fdi Inflows ◽

The Impact

Foreign Direct Investment (FDI) is a crucial factor to development in SADC, while at the same time corruption continues to be an obstacle to economic transformation in these countries. Empirically, studies provide controversial results on the effect of corruption on FDI. Some studies conclude that corruption negatively impacts FDI inflows in a country, while others provide evidence that corruption can act as a ‘helping hand’ to FDI inflows in a country. Given this ambiguity in the results of previous studies, using panel data for the period 2000-2016 for 15 SADC countries, this study examines the impact of corruption on FDI inflows in these countries. Lack of attention in previous studies on the impact of corruption on FDI inflows in SADC motivated this research. Estimation results using robust random effects model show that when corruption is widespread in a country, foreign investors are reluctant to invest. Thus, corruption negatively affects FDI inflows in SADC countries. The study recommends that SADC countries should develop and implement efficient, effective and strong anti-corruption measures to reduce corruption and hence increase FDI inflows.

Download Full-text

Determinants of Corporate Dividend Policy in Polish Companies Listed on WSE

e-Finanse ◽

10.2478/fiqf-2019-0019 ◽

2019 ◽

Vol 15 (3) ◽

pp. 47-55

Author(s):

Magdalena Gostkowska-Drzewicka ◽

Ewa Majerowska

Keyword(s):

Panel Data ◽

Random Effects ◽

Stock Exchange ◽

Structural Parameters ◽

Random Effects Model ◽

Profit Distribution ◽

Dividend Payments ◽

Warsaw Stock Exchange ◽

History Of ◽

Selection Of

AbstractThe purpose of this paper is to identify the factors influencing the level of dividend payments in the companies listed on the Warsaw Stock Exchange in 1998-2017 as well as to provide empirical evidence for their significance, using a panel data approach. The object of research comprised the companies listed on WSE, as of February 01, 2019. The subject of the analysis are the dividends paid by the companies and the factors potentially influencing the decisions regarding profit distribution. The models estimated for the panel data, based on the theory, allowed selection of the best model, which is the random-effects model. Moreover, these models allowed identification of the factors determining the changes in the level of dividend per share. The best model was the random-effects model. This model allowed identification of the factors impacting the changes in the level of dividend per share, that is, the value of the company’s total assets and the history of the company’s operation on the stock exchange market. All structural parameters (except the intercept) were positive. It means that growth of each of these variables causes an increase in the dividend per share.

Download Full-text

The Effect of Local Taxes and Retribution on Economic Growth in Indonesia

Jurnal Analisis Bisnis Ekonomi ◽

10.31603/bisnisekonomi.v18i1.2955 ◽

2020 ◽

Vol 18 (1) ◽

pp. 14-23

Author(s):

Bayu Rhamadani Wicaksono

Keyword(s):

Economic Growth ◽

Panel Data ◽

Random Effects ◽

Secondary Data ◽

Random Effects Model ◽

Good Strategy ◽

Local Taxes ◽

Panel Data Regression ◽

Data Regression ◽

The Government

The purpose of the study is to analyze the effect of local taxes and retributions on the economic growth in Indonesia. The data used are secondary data from provinces in Indonesia 2014-2017 using panel data regression with Random Effects Model (REM). The results are as follows, first, the local taxes has a negative and significant impact on the economic growth in Indonesia. Second, the retributions have a positive and significant effect on the economic growth in Indonesia. The government should evaluate and plan a good strategy for the next period so that the potential revenues of local taxes and retributions can increase economic growth gradually.

Download Full-text