Estimation Of Panel Models Research Articles

Study on the influence of global tourism development on the upgrading of county industrial structure

Taking the global tourism as an important grasp, promoting the tourism to the industrial structure optimization and economic development of the driving force, is conducive to the high-quality development of China's county economy. Based on the panel data of 255 demonstration counties of global tourism in 30 provinces in China from 2012 to 2020, and on the basis of analyzing the correlation between the two, this paper empirically tests the relationship between the development of global tourism and the upgrading of county industrial structure by using static panel model and systematic GMM estimation method. It is found that the development of global tourism has a positive and significant impact on the upgrading of county industrial structure, and there are significant differences in this effect between the eastern, central and western regions of China. Therefore, this paper puts forward some countermeasures and suggestions to promote the upgrading of county industrial structure through the development of global tourism.

Natural Disasters and Human Development in Asia–Pacific: The Role of External Debt

The average country in Asia–Pacific experiences more natural disasters than average countries of other developing regions. This paper presents stylized facts on natural disasters, human development, and external debt in Asia–Pacific. The paper also contains estimates of the effects that natural disasters have on human development. Controlling for country- and time-fixed effects, the dynamic panel model estimates show that external debt has a mitigating effect on the adverse impacts that natural disasters have on human development; in countries with low external debt-to-GDP ratios, natural disasters significantly decrease the human development index, but not so in countries with high external debt-to-GDP ratios. External debt (i.e., borrowing from abroad) is a financial contract for obtaining resources from abroad (i.e., imports of goods and services). When a country experiencing a natural disaster borrows from abroad to increase imports of goods and services, the population suffers less when a natural disaster strikes. Natural disasters destroy goods and capital (e.g., food, machinery, buildings, and roads) in the countries in which they occur. If imports of goods and services do not increase, then the population has less goods and services to consume following a natural disaster. By increasing imports, which are mirrored on the financial side by an increase in external debt, the population of a country that was struck by a natural disaster can experience consumption smoothing. As the incidence of natural disasters increases globally, a policy recommendation for disaster-prone countries, supported by the empirical results of this paper, is the need for deeper and innovative mechanisms of access to international financing, including reforms in both domestic and international financial systems. The paper’s most significant contribution is the unique lens through which it analyzes the often-studied subject of natural disasters. Rather than looking at disasters as merely adverse events and debt as an unwelcome obligation in isolation, it connects the two and uncovers the paradoxically positive and beneficial role a healthy level of external debt can play in mitigating the adverse effects of these disasters. It provides a fresh perspective, a shift in thinking that may immensely benefit external debt and disaster management policies.

A Test of the Permanent Income Hypothesis When Households are Less Constrained

AbstractIn 2009, the Australian Government delivered approximately $8 billion in direct payments to households. These payments were randomly allocated over a 5‐week period. Panel model estimates show that for the average household, there was no significant disbursement effect on nondurable consumption. Only for relatively young and low‐income households, for example, at the bottom 10th percentile of each, was there a significant positive effect of the tax bonus payment on nondurable consumption. We argue the null findings on average could be due to macroeconomic and institutional differences leaving Australian households less constrained than their U.S. counterparts.

Devaluation and stock prices

Aim: The study evaluated the causal link between exchange rate devaluation and stock prices in African stock markets within the African region, namely, Zambia, Nigeria, Uganda, Tunisia, Tanzania, Botswana, Indonesia, Egypt, South Africa, and Malaysia. Research method: The Toda-Yamamoto (T-Y) Granger causality methodology which entails a determination of maximum order of integration, determination of optimal lag length, and finally conducting the T-Y causality test based on augmented var k + dmax was used in the study. We also deployed the ARDL model to estimate the effects of devaluation on stock prices in Africa. To accommodate issues of endogeneity, we further implemented dynamic panel model estimation to unravel the true effects of exchange rate devaluation on stock prices. Findings: Currency devaluation and the consumer prices negatively impacted stock prices by 3.6% and 3.2% respectively in the short term period following a 1% rise in devaluation and consumer prices. In the long-term period, a 1% rise in currency devaluation and consumer price level stimulated 1.45% and 0.68% reductions in stock prices respectively in ten developing African countries. The lending interest rate also significantly and positively impacted stock market prices over the long-term period by 0.25% following a 1% rise in lending rate, whereas, in the short-term period, a 1% rise in lending rate stimulated 0.38% decline in stock prices of the ten African countries. Originality: in this research, we executed the Toda-Yamamoto granger causality methodology in explaining the causal relation between exchange rate devaluation and stock prices in ten developing African countries and with the ARDL model, the study estimated the effects of devaluation on stock prices in Africa. Contributions: The research is a contribution to a remarkable long-run connection linking stock market price, exchange rate and price index. Though there was a remarkable long-term association linking them, the outcome also showed an essential short-run relationship linking stock market price, consumer price index and interest rate. Limitations: This research is limited to ten countries within the African region. This choice of countries was made on basis of the available data. Hence, data could be sought on additional developing countries and estimation done for a larger sample of countries to obtain results that can be used to generalize the causality between devaluation and stock price movements.

Lecturers' turnover intention and intention to remain with the organization: a dynamic cross-lagged panel model estimation using the PLSe2 method

PurposeAlthough numerous studies have been conducted to explore the impact of various factors on employees' turnover intention and intention to remain with the organization, the relationship between these two constructs remains largely unexplored. Considering the significance of these constructs, particularly in the context of the COVID-19 pandemic, the authors aimed to investigate their association within an academic environment using a dynamic modeling approach.Design/methodology/approachThis study follows a quantitative approach and utilizes a longitudinal survey design. The authors utilized a cross-lagged panel model (CLPM) and employed the parametric efficient partial least squares (PLSe2) methodology to estimate the dynamic model using data gathered from lecturers associated with both public and private universities in Malaysia. In order to offer methodological insights to applied higher education researchers, the authors also compared the results with maximum likelihood (ML) estimation.FindingsThe findings of the authors' study indicate a reciprocal relationship between turnover intention and intention to remain with the organization, with intention to remain with the organization being a stronger predictor. Moreover, situational factors were found to have a greater influence on eliciting turnover intention within academic settings. As anticipated, the use of the PLSe2 methodology resulted in higher R2 values compared to ML estimation, thereby reinforcing the effectiveness of PLS-based methods in explanatory-predictive modeling in applied studies.Practical implicationsThe authors' findings suggest prioritizing policies that enhance training and consultation sessions to foster positive attitudes among lecturers. Positive attitudes significantly impact judgment-driven behaviors like turnover intention and intention to remain with the organization. Additionally, improving working environments, which indirectly influence judgment-driven behaviors through factors like affective work events, affect and attitudes, should also be considered.Originality/valueThis study pioneers the examination of the causal relationship between turnover intention and intention to remain with the organization, their stability over time and the association of changes in these variables using a dynamic CLPM in higher education. It introduces the novel application of the cutting-edge PLSe2 methodology in estimating a CLPM, providing valuable insights for researchers in explanatory-predictive modeling.

The Concavity of Conditional Maximum Likelihood Estimation for Logit Panel Data Models with Imputed Covariates

In estimating logistic regression models, convergence of the maximization algorithm is critical; however, this may fail. Numerous bias correction methods for maximum likelihood estimates of parameters have been conducted for cases of complete data sets, and also for longitudinal models. Balanced data sets yield consistent estimates from conditional logit estimators for binary response panel data models. When faced with a missing covariates problem, researchers adopt various imputation techniques to complete the data and without loss of generality; consistent estimates still suffice asymptotically. For maximum likelihood estimates of the parameters for logistic regression in cases of imputed covariates, the optimal choice of an imputation technique that yields the best estimates with minimum variance is still elusive. This paper aims to examine the behaviour of the Hessian matrix with optimal values of the imputed covariates vector, which will make the Newton–Raphson algorithm converge faster through a reduced absolute value of the product of the score function and the inverse fisher information component. We focus on a method used to modify the conditional likelihood function through the partitioning of the covariate matrix. We also confirm that the positive moduli of the Hessian for conditional estimators are sufficient for the concavity of the log-likelihood function, resulting in optimum parameter estimates. An increased Hessian modulus ensures the faster convergence of the parameter estimates. Simulation results reveal that model-based imputations perform better than classical imputation techniques, yielding estimates with smaller bias and higher precision for the conditional maximum likelihood estimation of nonlinear panel models.

Investigating Profit Persistence Among Hungarian Plastic Manufacturing Companies

This study examines the competitive nature of the Hungarian plastics industry sector based on 2010-2019 data from the Crefoport database.. The aim of the study is to examine that how close the market of plastic industry companies is to perfect competition. Market efficiency was investigated using a Markov chain and profit persistence estimation (Arellano & Bond, 1991). Corporate profitability was measured using the ROA indicator. Variables reflecting industry and market effects are also included in the analysis as controls. Based on the Markov transition probability matrix, market competition is harmed. Based on the panel model estimation, the profit persistence value shows a low value (0.129) compared to the existing literature. The profitability of plastic companies can be statistically proven to be affected by company size (p = -0.046), short (p = 0.016) and long risk (p=-0.093), and the volatility of profitability (p = 0.633). Among the exogenous variables, industry income (p=0.081) and market concentration (p = 0.974) have a significant effect on the profitability of companies. Limited market competition reduces overall social benefit and efficiency in several ways: it reduces price competition, quality orientation, and the pursuit of innovations. Therefore, from the point of view of economic policy , it is definitely justified that the sector receives subsidies in an appropriate amount that improves the efficiency and productivity of small and medium-sized enterprises, as well as encourage technological development and innovation.

Financial constraints and firms’ markup: evidence from China

Using the micro data of Chinese manufacturing firms, this study analyzes the impact of financial constraints on firms’ markup and their influence mechanism. The findings suggest that financial constraints significantly reduce firms’ markup. The result is still valid after a series of robustness tests, such as key indicator substitution, analysis of endogeneity problems, controlling for other policy changes, dynamic panel model estimation, and quantile regression. An analysis of the influence mechanism shows that, on the one hand, financial constraints inhibit the improvement of firms’ production efficiency and reduce its markup through the productivity channel; on the other hand, financial constraints also reduce firms’ market pricing power and exert a significant negative impact on firms’ markup through the market pricing channel. At the same time, significant heterogeneity exists in the negative impact of financial constraints among different firm types. This paper not only enriches our understanding of the relationship between financial constraints and firms’ markup, but also provides a new perspective for a more comprehensive grasp of the micro economic effects of financial constraints.

Digital transformation, banking stability, and financial inclusion inSub‐SaharanAfrica

AbstractThis article highlights the effects of Digital Transformation as a General Purpose Technology and bank stability on banking inclusion and thus on financial inclusion in Sub‐Saharan African countries. We use simultaneous panel model estimates covering 36 Sub‐Saharan African countries from 2004 to 2017. The results show positive and significant effects of digital transformation as general purpose technology and bank stability on financial inclusion. The results show also that the effect of digital transformation on financial inclusion is more substantial when the banking sector is stable.

Covid-19 and Firms’ Stock Price Growth: The Role of Market Capitalization

ABSTRACT This paper studies the role of capitalization on firms’ stock price growth in response to new cases of Covid-19 infections in the United States. Controlling for firm and time fixed effects, our panel model estimates show that the effect of new cases of Covid-19 infections on firms’ stock price growth is significantly increasing in capitalization: For each one standard deviation increase in capitalization, a one standard deviation increase in new cases of Covid-19 infections increases the weekly growth rate of firms’ stock prices by about 0.7% points. Effects of capitalization on the impact that Covid-19 infections have on firms’ stock price growth are largest in the travel, tourism, and hospitality sector. Smaller but still positive effects of capitalization are present in the pharmaceutical products, high-tech, and banking and finance sectors. The results are robust to controlling firms’ elasticity of demand, productivity, financial constraints, managerial compensations, and aggregate money growth and economic activity.

The Impact of Changes in Financial Supervision on the Profitability of the Hungarian Banking Sector

Since 2013, the central bank has been responsible for supervision in Hungary. In addition to the regulatory change, a law was published in the same year that started the process of abolishing the savings co-operative system. This paper investigates the impact of these two significant changes on the profitability of the Hungarian banking sector between 2003 and 2019 using dynamic panel model estimates. The supervisory change has reduced the profitability of credit institutions and tighter supervision has been implemented. The transformation of the savings co-operative system was in fact an integration that led to the disappearance of savings co-operatives by 2019. Competition in the market has been weakened, which has increased the profitability of the remaining financial institutions. The results were robust in terms of the multiple specifications and profitability ratio.

Statistical Attribution of the Influence of Urban and Tree Cover Change on Streamflow: A Comparison of Large Sample Statistical Approaches

AbstractThe strengths and weaknesses of different statistical methodologies for attributing changes in streamflow to land cover are still poorly understood. We examine the relationships between high (Q99), mean (Qmean), and low (Q01) streamflow and urbanization or tree cover change in 729 catchments in the United States between 1992 and 2018. We apply two statistical modeling approaches and compare their performance. Panel regression models estimate the average effect of land cover changes on streamflow across all sites, and show that on average, a 1%‐point increase in catchment urban area results in a small (0.6%–0.7%), but highly significant increase in mean and high flows. Meanwhile, a 1%‐point increase in tree cover does not correspond to strongly significant changes in flow. We also fit a generalized linear model to each individual site, which results in highly varied model coefficients. The medians of the single‐site coefficients show no significant relationships between either urbanization or tree cover change and any streamflow quantile (although at individual sites, the coefficients may be statistically significant and positive or negative). On the other hand, the GLM coefficients may provide greater nuance in catchments with specific attributes. This variation is not well represented through the panel model estimates of average effect, unless moderators are carefully considered. We highlight the value of statistical approaches for large‐sample attribution of hydrological change, while cautioning that considerable variability exists.

Identification and (Fast) Estimation of Large Nonlinear Panel Models with Two-Way Fixed Effects

Identification and (Fast) Estimation of Large Nonlinear Panel Models with Two-Way Fixed Effects

Effects of air pollution on regional innovation and the mediator role of health: Evidence from China

AbstractEnvironmental regulation has attracted a great deal of attention between air pollution and innovation, but rarely individual perspective. This study attempts to reveal the impact of air pollution on regional innovation through individual health. The methodology applies dynamic panel model and system GMM estimation dealing with endogeneity issue and using China’s provincial panel data from 1998 to 2018. Empirical analysis shows that air pollution has significant negative effects on regional innovation. With every 1% increase in PM2.5concentration, patent applications decrease by 0.186%. Correction and replacement of the dependent variable, classifying samples, and using subsamples all show that the results are robust. Parallel multiple mediator model is adopted to analyze the influence mechanism. It can be found that the physical and mental health of R&D personnel plays a significant mediator role. It can be constructive to regional innovation to reduce air pollution while maintaining innovation capital investment; and improve R&D personnel health while reducing their work pressure.

Entrepreneurship Recovery in Romania after the Great Recession. A Dynamic Spatial Panel Approach

Entrepreneurship plays a key role in transforming the economy and society by stimulating economic development, testing innovative ideas, creating new jobs, and by enriching the quality of life and human existence. Entrepreneurship dynamics depend upon a series of local and national economic factors, but are also affected by the international environment, such as the current COVID-19 pandemic. Statistical data show that new businesses are created at a slower rate during an economic crisis, when the economic climate is harsh, and business opportunities are scarce. Nevertheless, there are local differences in the reaction to crises, and new business formation tends to decline with variable intensity from one region to another, even in the same country. The crises are acting as a trigger for some opportunity-driven entrepreneurs, and resilient regions can thrive even in times of crisis or recover faster after a depression. To capture spatial interactions, as well as spatial short- and long-term effects, the method employed in our analysis relies on the estimation of dynamic spatial panel models. We tested the potential impact of a large variety of social and economic indicators on the creation of new firms and found that the most consequential factors of influence are the economic crisis (expressed through a binary variable), GDP per capita, FDI per capita, inflation, unemployment, and education. Our results convey a powerful policy message for both national and regional decision makers. We believe that, while putting entrepreneurial initiative to the test, the current COVID-19 crisis might act as a catalyst that leads to innovation and reshapes the economy and society.

2SLS and IV Estimation of Dynamic Panel Models with Heterogeneous Trend*

AbstractIn this paper, we consider two‐stage least squares (2SLS) and simple instrumental variable (IV) type estimation of dynamic panel data models with both individual‐specific effects and heterogeneous time trend when both N and T tend to infinity. We consider the forward orthogonal deviations (FOD) proposed by (Hayakawa, et al. Econometric Reviews, 2019. Vol. 38, pp. 1055–1088) and the double first difference (2FD) to remove both the individual‐specific effects and heterogeneous trend. As the main theoretical contribution, we establish the asymptotic properties of the 2SLS estimation of the lag coefficient and find that the 2SLS estimation using FOD and optimal 2SLS estimation using 2FD are asymptotically biased of order , while the 2SLS based on 2FD using non‐optimal weighting matrix is asymptotically biased of order . We also establish the asymptotic unbiasedness of the simple IV estimation using first differenced lagged dependent variable as instrument, and establish the invalidity of using level lagged dependent variable as instrument for the simple IV estimation. Monte Carlo simulations confirm our findings in this paper.

DOES DIGITALIZATION REDUCE ELECTRICITY CONSUMPTION? EVIDENCE FROM SPATIAL ANALYSIS

This survey addresses the issue of assessing and identifying the role of digital transformation in electrical energy consumption. It is shown that spatial effects in the level of electricity consumption among Russian regions are significant. The work is based on regional data for 2010-2018. Research methods: construction of the Moran and Geary indices, estimation of spatial regression panel models with fixed effects. The Implementation of digital technologies in Russia is at the initial stage, so their impact on energy consumption is ambiguous. The model with a spatial autoregressive lag revealed that the change (increase or decrease) of the electricity consumption level in the one region entails a change of energy consumption in other regions. Since the results indicate the importance of the spatial factor in electricity consumption, the government can implement a differentiated regional policy aimed at improving the efficiency of energy use in certain regions.Keywords: Spatial panel data models, electricity consumption, digital transformation, energy saving, Russian regionsJEL Classifications: O1, Q4, R1DOI: https://doi.org/10.32479/ijeep.10500

Effect of corporate income tax on investment decisions of Indian manufacturing firms

This study is an attempt to empirically analyse the effect of corporate income tax on investment of manufacturing firms in India during 2005–2019, using the standard static panel model estimation techniques. It is found that the effective corporate tax rate has a negative and significant impact on the corporate investment. Moreover, the estimated effective tax elasticity is relatively low as compared to the magnitude found in other countries. The adverse impact of tax on investment is higher in private firms than in public firms and higher in pre-economic crisis period than in post-crisis period. The effective rate increases with age and size of firms. It is our hope that these results will be useful to policymakers and other stakeholders to take appropriate strategies to design the corporate tax policy such that it will not hinder business investment in India.

Impact of Construction Industry on Economic Growth in Africa: Granger Causality and GMM Estimation of Dynamic Panel Models

Impact of Construction Industry on Economic Growth in Africa: Granger Causality and GMM Estimation of Dynamic Panel Models

Do International Sanctions Reduce Household and Government Consumption in Developing Countries?

AbstractThis paper uses a panel of 30 developing countries over the 1996‐2015 period to study the effect that international sanctions have on household and government consumption in target countries. I use a broad set of sanctions covering different aspects of sanctions. The panel model estimates show that sanctions have heterogeneous effect on household and government consumption. Household consumption responds significantly negative to noneconomic sanctions. Government consumption, in contrast, is negatively associated with intensity of sanctions, economic sanctions, plurilaterial sanctions, EU sanctions and U.S. sanctions. Disaggregating government consumption reveals that the intensity of sanctions increases government expenditure on subsidies and transfers, while government expenditure on goods and services is not correlated with sanctions. With regard to health outcomes, there is no clear evidence that sanctions are significantly associated with either life expectancy or infant mortality in target countries.