Dynamic Panel Data Model Research Articles

Empirical likelihood for spatial dynamic panel data models with spatial lags and spatial errors

We study spatial dynamic panel data models with both spatial lags and spatial errors. The empirical likelihood (EL) ratio statistics are constructed for the parameters of the models. It is shown that the limiting distributions of the EL ratio statistics are chi-square distributions, which are used to construct confidence regions for the parameters of the models. A simulation study is conducted to compare the performances of the EL based and the normal approximation (NA) based confidence regions. Simulation results show that the EL can be computationally easier than the NA method to implement in practice.

On the Estimation of True State Dependence in the Persistence of Innovation*

AbstractThis paper explores the persistence often found in firms’ innovation and advances current research by investigating its actual nature. Previous studies have aimed at disentangling true state dependence from spurious state dependence by using a random effects (RE) dynamic panel probit approach, thereby imposing strong conditions on the underlying structure of the unobserved heterogeneity. Building on recent advances in the econometric literature, which allows for true fixed effects estimation of dynamic nonlinear panel data models, we demonstrate that relaxing the assumptions on the unobserved heterogeneity can have a considerable effect on the estimates of true state dependence. While we confirm the existence of a strong persistence of innovation in firms, we however find that true state dependence only explains about half of the persistent behaviour displayed by firms; this is in contrast to the popular RE methodology that attributes 70% to 100% of persistence to true state dependence. Our results suggest that policy programs aimed at encouraging initial innovations alone are useful but may not possess a long‐term stimulating effect on innovation activity.

Determinants of Carbon Dioxide Emissions: New Empirical Evidence from MENA Countries

The paper emplyos the Pooled Mean Group (PMG) estimation to investigate empirically the relationship among carbon dioxide (CO2) emissions and potential determinants for a panel of 18 MENA countries during the period 1980-2018. The good properties in terms of consistency and efficiency of the coefficient estimates make the PMG approach very useful for examining the determinants of pollution emissions in the framework of dynamic heterogeneous panel data models over both the long- and short-run. Unlike the extant literature on MENA economies, many determinants are included in the analysis to avoid the bias problem of omitted variables. Three energy sources and two classes of sub-panels according to regional proximities and oil wealth are considered in order to provide a sensitivity check on the findings and to make the analysis more homogeneous. The results reveal long-run relationships between pollution emissions and the selected variables. All determinants are found to be statistically significant for all panels and energy sources over the short-run. However, some variables are not significant determinants over the long-run. The Environment Kuznet's Curve (EKC) hypothesis is supported only for the panel of non-oil countries, which has meaningful implications and reveals the importance of splitting the global panel in order to appropriately examine the EKC hypothesis and conduct policy debates according to the findings of each panel. The obtained results provide important policy implications.

An Ecological Study of Tuberculosis Incidence in China, From 2002 to 2018.

IntroductionTuberculosis is one of the main infectious diseases threatening global health, which is also the main cause of death from a single source of infection (above HIV/AIDS). China is a country with a high burden of tuberculosis in the world, ranking only behind India and Indonesia. However, there are few ecological studies on the burden of tuberculosis in China. This study aims to provide more research basis for the government to formulate tuberculosis policies by exploring the ecological factors associated with the incidence of tuberculosis, so as to achieve the goal of eliminating tuberculosis by 2030.MethodsWe collected data on the incidence of tuberculosis and ecological factors of 31 provinces, autonomous regions, and municipalities in Mainland China (excluding Taiwan, Hong Kong, and Macau) from 2002 to 2018. We constructed a framework of ecological factors affecting the incidence, which consists of 5 secondary indicators and 35 tertiary indicators. And we construct a dynamic panel data model based on the Lasso Regression to select variables to test the effect of each ecological factor on the incidence.ResultsAmong the 35 tertiary indicators, economy 3,4,6,7, environment 1, recourses 1,3, demography 3, and lifecare 2,4,8,9,13 passed the significance test at the 1% level, economy 1,2,5, environment 2,9, lifecare 6,12 passed the significance test at the 5% level, lifecare 10 passed the significance test at the 10% level. Only economy 5 and economy 6 have a positive impact on the incidence, other statistically significant ecological indicators are negatively correlated with the incidence.ConclusionsOur study indicated that many ecological factors, including residents' income, unemployment rate, educational level, medical resources, population density, sunshine duration and dietary structure, are closely related to the incidence of tuberculosis. These findings contribute the government to taking targeted measures for tuberculosis prevention and control, including improving the level of economic development, increasing employment, expanding the scale of enrollment in colleges and universities, and ensuring that the prices of sources of animal protein are reasonable to meet the residents' intake of protein.

The persistence of wages

This paper documents the extent to which wage persistence can be explained by permanent worker, employer, and match heterogeneity. Standard methods used to perform such decompositions for industry or racial wage gaps are inappropriate for decomposing wage persistence in dynamic panel data models because of the incidental parameter problem. When we apply these methods without bias correction, we find that the majority, 59.3 percent, of wage persistence is explained by worker heterogeneity, with employer and match heterogeneity explaining 29.7 and 11.0 percent, respectively. We evaluate three methods for addressing incidental parameter bias using a Monte Carlo study. An empirical application to Portuguese linked employer–employee data shows that the uncorrected estimates tend to understate wage persistence by around 24 to 42 percent, depending on the choice of the bias correction estimator used, and overstate the extent to which wage persistence arises from permanent unobserved heterogeneity. Furthermore, results indicate that the uncorrected estimates overstate the role of permanent worker heterogeneity, and understate the role of firm heterogeneity.

Explaining Aggregated Recovery Rates

This study on explaining aggregated recovery rates (ARR) is based on the largest existing loss and recovery database for commercial loans provided by Global Credit Data, which includes defaults from 5 continents and over 120 countries. The dependence of monthly ARR from bank loans on various macroeconomic factors is examined and sources of their variability are stated. For the first time, an influence of stochastically estimated monthly growth of GDP USA and Europe is quantified. To extract monthly signals of GDP USA and Europe, dynamic factor models for panel data of different frequency information are employed. Then, the behavior of the ARR is investigated using several regression models with unshifted and shifted explanatory variables in time to improve their forecasting power by taking into account the economic situation after the default. An application of a Markov switching model shows that the distribution of the ARR differs between crisis and prosperity times. The best fit among the compared models is reached by the Markov switching model. Moreover, a significant influence of the estimated monthly growth of GDP in Europe is observed for both crises and prosperity times.

The impact of commodity price volatility on fiscal balance and the role of real interest rate.

The objective of this study is to explore the impact of commodity price volatility on the governments’ fiscal balance. Using a dynamic panel data model for 108 countries from 1993 to 2018, this study finds that governments’ fiscal balance deteriorates with commodity price volatility, especially for commodity-exporting economies. A one standard deviation increase in commodity price volatility leads to a reduction of approximately 0.04 units in the fiscal balance as a percentage of gross domestic product. Further, we examine the role of real interest rates in influencing the relationship between commodity price volatility and fiscal balance. The empirical results suggest that the negative impact of commodity price volatility on fiscal balance can be mitigated with a lower real interest rate. This implies under the sticky price assumption, an accommodative monetary policy could be effective in moderating the negative effect of commodity price volatility on fiscal balance.

Is the sustainability profile of FinTech companies a key driver of their value?

• Financial industry has evolved due to the disruptive innovation and technologies • This work studies the effect of the sustainability profile of Fintech companies • Methodology is based on dynamic (GMM-SYS) and static (PCSE) panel data models • Fintech firm value is driven by rankings or green certificates • Sustainability effort decreases as market capitalization increases - agency problem The digitisation process is affecting all markets and raising consumer awareness about companies’ sustainable behaviour. This work studies the effect of the sustainability profile of FinTech companies on the firm (market value and book value) as the factors that add value to investors and motivate their evolution in markets are still unknown. Using the KBW and Nasdaq FinTech Indices, and the NASDAQ Insurance Index (IXIS), we composed a panel of 95 companies over a 10-year period (2010–2019) with economic-financial variables and data about green certificates and sustainability indices. The applied methodology is based on dynamic (GMM-SYS) and static (PCSE) panel data models. Our results show that the market value of FinTech companies is positively driven by an CSR report being issued, the position in the CSR RepTrak, company size and board size. In contrast, the number of green certificates, particularly their position in the Green Ranking, is negatively related to their market value. Surprisingly in the most transparent companies, the direction of the variables effect evidenced for the book value per share is the opposite to market capitalisation.

Empirijsko modelovanje stope popunjenosti kapaciteta hotela dinamičkim panel podacima

The previous research studies used mainly the occupancy rate as one of the key indicators of hotel performance. As the hotel occupancy rate varies both throughout the year and for different types of hotels, the use of panel data is more appropriate and more comprehensive compared to the cross-sectional data or time series, which have so far been most commonly used in similar research. Also, the previous research did not take into account the great heterogeneity among the analyzed hotels, nor the correlation of the occupancy rate in relation to its past values. By using the generalized method of moments within the dynamic panel data model, it is possible to take both properties into account. The analyzed data pertain to the hotel industry of Spain. Specifically, the given panel data include a sample of 49 hotels observed over a period of 12 years. The application of dynamic panel analysis shows that the values of hotel occupancy rate are influenced by the values of hotel occupancy rate with a lag one, as well as the values of total marketing expenses with a lag one. It was further determined that the values of incentive management fees, as well as the average daily rate and the consumer price index also have an impact on the observed variable. We are convinced that the presented analysis results will be of significant benefit to hotel managers.

Inflación y Crédito Bancario

e objective of this paper is to assess empirically the effects of inflation rates on bank credit using panel data of the 32 Mexican states during 2003-2015. Our research method utilizes static models (pooled OLS, fixed effects, and random effects) and dynamic models (mean group, pooled mean group, and dynamic fixed effects) to analyze the relationship in the short and long runs. e main empirical result indicates that inflation rates exert negative effects on credit in the long run, but those effects tend to be positive in the short run. Concerning originality and findings, few papers study inflation and bank credit under macroeconomic stability, or in the case of Mexico with static and dynamic panel data models. However, one research limitation is the lack of data to apply the methodology before 1999 when inflation rates used to be higher. is would be useful to compare macroeconomic stability with instability.

Penetration Effect of Informatization on Technological Progress in Logistics Industry: Empirical Evidence from Inter-provincial Panel Data in China

This paper investigates pervasiveness effect of information technology on the technological progress of logistics in China. Using panel data of 30-regions in China during 2008 to 2020, we propose both static and dynamic panel data models, and find both resource level and spreading rate of information technology have negative influences on technological progress of logistics. The resource level of information technology has a short-term effect on technological progress of logistics, whereas the spreading rate of information technology has a long-term effect. Moreover, the result reveals that the discordant problem of information construction of logistics between resource level and spreading rate cause the negative influence of information technology.

"Effects of some insurance variables on sustainable economic growth. Evidence from EU using GMM panel data analysis "

The present paper analyses the impact of financial intermediation, and implications to economic growth. The main object of the paper is to prove empirically the existence of a positive association between insurance market indicators and economic development. The novelty of our study is related to the use of variables from the insurance sector, which is considered, along with the banking sector (and capital markets), as a major player involved in financial intermediation. The variables of interest used in our sample refer to the macroeconomic levels in the Member States of the European Union, and the time span being 2007-2019. For econometric analysis, we chose to use a dynamic panel data model using the generalized method of moments. Thus, we investigate how the variation of economic growth measured by the level of GDP per capita is influenced by the first lag of it, together with the insurance sector, the banking sector and the government expenditures. The variables used in our study are insurance penetration rates, measured as a percentage of gross written premiums and GDP, inflation, private consumption, balance of payments, government spending, and level of private credit (calculated as a percentage of GDP).

The mechanism and heterogeneity of environmental regulations' impact on the technological progress of dairy farming

The study analyses the theoretical mechanism through which environmental regulation affects the dairy industry's technological progress, with a particular focus on how the effect is conditional on farm size. Using the input–output data of dairy farms of different sizes from 2009 to 2019 in 10 Chinese provinces/autonomous regions in China and the quantitative measurement index of environmental regulation, the study estimates environmental regulation's heterogeneous influences on the dairy industry's technological progress by dynamic panel data models. The empirical results suggest that, first, environmental regulation has a U-type influence on the technological progress of dairy farming. The U-type influence means moving from pollution control's high cost and low technology progress to the high profit and high innovation input generated by optimizing the breeding structure. Second, the promotion of dairy farming technology depends on farm size. The effect of environmental regulation on technological progress in moderately large-farms showed a U-type relationship. In contrast, the effect in free-range and large-size dairy farms showed a linear and positive relationship. The government should further strengthen environmental regulation based on advancing moderately large-farms in compliance with market mechanisms in the long run. Particular attention should be paid to the forms of environmental regulation so that dairy cattle breeding technology can break through the inflection point of the “U” curve as soon as possible and ensure the significance of the rising stage. Along the way, technical support should be provided for realizing environmental protection and economic growth.

Robust Dynamic Space-Time Panel Data Models Using?-Contamination: an Application to Crop Yields and Climate Change

Robust Dynamic Space-Time Panel Data Models Using?-Contamination: an Application to Crop Yields and Climate Change

Identification and Estimation of Dynamic Heterogeneous Unbalanced Panel Data Models with Clustering

Identification and Estimation of Dynamic Heterogeneous Unbalanced Panel Data Models with Clustering

Exploring Brexit implications: the impact of longer journey times

Brexit implies longer journey times between UK and EU regions. In this paper the elasticity of trade with respect to journey time by goods vehicles is estimated, and the impact of this on employment is evaluated using a dynamic spatial panel data model. The estimator allows for the presence of endogenous and predetermined causal variables, regional interdependence, and attempts to control for common factors causing macro-economic variation over the estimation period. The estimates show that a job shortfall can be expected in both the UK and EU regions, with considerable diversity of outcome across regions.

Testing Endogeneity of Spatial Weights Matrices in Spatial Dynamic Panel Data Models

Testing Endogeneity of Spatial Weights Matrices in Spatial Dynamic Panel Data Models

Effect of Infrastructural Development on Kenya’s Manufacturing Exports to EAC Region

Manufacturing industries have played a significant role in boosting economic wellbeing in the world through accelerating and maintaining greater productivity growth, boosting employment options for semi-skilled workers, and increasing country competitiveness through exports. Kenya, like many other developing nations, is working to build a strong manufacturing industry. Agriculture and services have been the primary drivers of growth in the country. Historically, the manufacturing sector's contribution to Kenya's economy has remained constant at 10% of GDP, and in 2021 it was around 8.4 percent. As a result, the country has seen an early deindustrialization, as evidenced by the manufacturing sector's contribution to GDP, which was just 8.4% in 2021 and 9.2% in 2016. Boosting manufacturing sector results remains a key priority for Kenya, as evidenced by the slew of planned interventions for the industry that have been created over the years. The government has established Vision 2030, the Kenya Industrial Transformation Programme (KITP), and, most recently, the Big 4 Agenda to modernize the industrial sector. The major goal of this research was to see how infrastructure development (ID) affected Kenya's manufactured exports to the East African Community (EAC). The specific goals were to determine the impact of infrastructure development on Kenya's manufacturing exports to the EAC region. Gravity model was used as the theoretical framework for the study, which is based on the theory of international trade and employs a correlation research design that is ideal for dynamic panel data models. Each country's data for the study variables was obtained from the United Nations Conference on Trade and Development (UNCTAD), Kenya Nation Bureau of Statistics, World Bank development and African Development Bank for six EAC members for the period 2007–2021. Unit root test, Im-Pesaran and Shin, Levin-Li-Chu tests were used in the study. The Im-Pesaran unit root test results at Levels indicated that all the variables except inflation had unit root at levels as indicated by the p-values>0.05, except inflation which had a p-value of 0.0006

Financial development, income inequality and governance institutions

The paper investigates empirically how governance institutions mediate the link between financial development and inequality. To this aim, we assemble a dataset of 48 middle- and high-income countries for the period 1996-2014. Results, obtained by means of instrumental variables dynamic panel data models, reveal that financial development is pro-inequality; however, the strength of the relationship is attenuated in contexts with stricter control of corruption, better regulatory quality, political stability and rule of law. Institutional domains less directly related to the market economy ? political voice and accountability and government effectiveness ? do not play any mediating role.

Bayesian Analysis of Spatial Dynamic Panel Data Model with Convex Combinations of Different Spatial Weight Matrices: A Reparameterized Approach ⋆

Bayesian Analysis of Spatial Dynamic Panel Data Model with Convex Combinations of Different Spatial Weight Matrices: A Reparameterized Approach ⋆