Unbalanced Panel Data Model Research Articles

Dynamics of Bank Profitability and Growth in West African Economic and Monetary Union (WAEMU) Countries

The banking sector is vital to economic activity when it develops. Its development depends on the ability of banks to generate sufficient profits to meet their commitments. In this context, we set ourselves the objective of analyzing the effect of bank profitability on economic growth. The study covers the period from 1996 to 2023, and covers 7 WAEMU countries except Guinea-Bissau, for lack of data. Methodologically, we apply Bruno's (2005) Least Square Dummy Variable (LSDVC) bias correction approach to the dynamic unbalanced panel data model. The results indicate that bank profitability has a positive influence on economic growth in the WAEMU region, whatever the profitability index used. Inflation,Political stability and investment are not unrelated to economic growth in the union. It follows from these results that the union's authorities need to strengthen supervision of the banking sector to ensure that it remains profitable.

Corporate Governance and Dividend Policy: Evidence from Colombia

We study the impact of the adoption of the corporate governance code denominated Country Code (CC), on the dividend payout ratio of non-financial firms that report to the Superintendency of Companies in Colombia. Using an unbalanced panel data model with a sample of 605 companies from 1997 until 2022, we find that companies that adopted voluntarily the Country Code of corporate governance have paid, on average, higher dividends than those that did not. In addition, the impact of the adoption of the Country Code is amplified by its quality, meaning that higher dividend payments are attached to better Corporate Governance quality. We did not find the impact of the two reforms to the Country Code in 2007 and 2014, which implies that these reforms didn’t help companies to distribute more dividends in Colombia.

Estimation of Matrix Exponential Unbalanced Panel Data Models with Fixed Effects: An Application to US Outward FDI Stock

In this article, we consider a matrix exponential unbalanced panel data model that allows for (i) spillover effects using matrix exponential terms, (ii) unobserved heterogeneity across entities and time, and (iii) potential heteroscedasticity in the error terms across entities and time. We adopt a likelihood based direct estimation approach in which we jointly estimate the common parameters and fixed effects. To ensure that our estimator has the standard large sample properties, we show how the score functions should be suitably adjusted under both homoscedasticity and heteroscedasticity. We define our suggested estimator as the root of the adjusted score functions, and therefore our approach can be called the M-estimation approach. For inference, we suggest an analytical bias correction approach involving the sample counterpart and plug-in methods to consistently estimate the variance-covariance matrix of the suggested M-estimator. Through an extensive Monte Carlo study, we show that the suggested M-estimator has good finite sample properties. In an empirical application, we use our model to investigate the third country effects on the U.S. outward foreign direct investment (FDI) stock at the industry level.

Survival of the Fittest: The Long-run Productivity Analysis of the Listed Information Technology Companies in the US Stock Market

Abstract This article tries to estimate the operating efficiency among 30 years (1989–2018) in the information technology (IT) industry, where technologies are everchanging. The balanced panel data model of Kneip et al. is extended to an unbalanced panel data model for more generic applications. The estimation results based on listed IT firms in the US stock market provide evidence that the law of the jungle is applicable to the IT industry. The industry survivors, which often obtain and maintain market power through merger and acquisition, create economic moats by setting high barriers to entry to defend against risk and uncertainty and dominate the capital-intensive and technology-intensive IT industry. At the same time, the estimation results also demonstrate that the global IT industry is highly sensitive to technological waves and business cycles. Though thriving start-ups and spin-offs stimulate innovation and generate a richer diversity, economies of scale are still essential for sustainable development in the IT industry.

Governmental Support for Entrepreneurship in Spain: An Institutional Approach

Institutions have acted as rules of the game influencing entrepreneurship. However, little is known about their complementary function as knowledge generators. Drawing on institutional economics, this paper explores the role of regional governments and universities in creating public support and knowledge conducive to entrepreneurship in Spain. Unbalanced panel data models are applied to 281 observations taken from GEM; Ministry of Economics, Industry, and Competitiveness; and INE in the 2004-2018 period. Findings suggest that formal institutions as R&D expenditure and staff in the public sector and universities positively predict entrepreneurship. In light of these results, theory and policies are discussed.

Testing for individual and time effects in unbalanced panel data models with time-invariant regressors

<abstract><p>In this paper, we use a moment-based method to test the existence of the individual and time effects in unbalanced panel data models with time-invariant regressors. Based on the difference of two variance estimators of idiosyncratic errors, three test statistics are proposed. The test statistics for individual (time) effect is robust when the time (individual) effect exists, and is robust for the correlation between explanatory variables and individual or time effect. Additionally, they do not require prior distributional assumptions on the error term. The asymptotic properties of estimators and the test statistics are given in this paper. The Monte Carlo simulations show that the test statistics have good power in finite samples at various situations and a real example is studied for illustration.</p></abstract>

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

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

Determinants of climate financing and the moderating effect of politics: evidence from Bangladesh

PurposeThere was no previous firm-level empirical research to examine cross-sectional differences in climate financing. The purpose of this study is to determine the key elements of the climate investment decision by business management. The study also explores how politics and media influence corporate climate investment decisions.Design/methodology/approachThe study incorporates a theoretical lens of institutional, stakeholder and media setting agenda to explain the relationship of climate finance with political connection and media influence along with other institutional and firm-specific variables. The sample of the study is collected from the financial sector firms that financed climate/green projects. In total, 178 firm-year observations are documented during 2014–2018. The unbalanced panel data model uses a fixed effect and a 2SLS regression model to test a set of hypotheses. The study uses several alternate methods to check and verify the reliability of the study.FindingsThe empirical findings show that climate finance is positively and significantly associated with Islamic Sharīʿah and media visibility, and negatively and significantly related to financial constraints. Moreover, the empirical results document that listing regulation has no significant influence on climate investment. The political connection plays a negative moderating role between media and climate finance. The result indicates that if a former or current politician is on the board, the media’s positive impact on climate financing diminishes.Practical implicationsThe study has significant managerial implications especially to the regulatory bodies, business management and policymakers. The central bank in the developing countries needs to take into consideration the finding of the study promoting climate/environmental/green finance and investment. Islamic Sharīʿah promotes climate finance that would be a prominent indicator for Islamic financial institutions.Social implicationsPolitics can deter positive decisions on climate financing such that it negatively influences the media’s role of a watchdog of the society in developing countries. Climate investment would be an important mechanism to reduce carbon emissions and environmental hazards and to solve many social problems.Originality/valueThe study provides first-ever firm-level evidence of the determinants of climate finance and investment that has a significant value in the area of climate change and green investment by the financial firms.

Determinants of commercial bank loan and advance disbursement: the case of private Ethiopian commercial banks

PurposeThis paper aimed to investigate the determinants of loans and advances from commercial banks in the case of Ethiopian private commercial banks.Design/methodology/approachThe study randomly selected seven commercial banks to represent the population stratified on their asset, deposit and paid-up capital amounts. The study utilized an unbalanced panel data model as each bank started operation at a different period of time and considered the period 1995–2016 for secondary details.FindingsThe findings showed that the deposit size, credit risk, portfolio investment, average lending rate, real gross domestic product (GDP) and inflation rate had significant and optimistic effects on the lending and advancement of private commercial banks. On the contrary, liquidity ratio had significant and negative effects on private commercial bank loans and advances. Finally, the study forwarded a feasible recommendation for concerned organs to focus on deposit size, credit risk, portfolio investment, average lending rate, real GDP, inflation rate and liquidity ratio. The results of this study will help banking industry policymakers and planners understand how to minimize inflation and unemployment by improving development and sustainable economic growth.Originality/valueThe findings of this study can also affect the general attitudes of a society by increasing knowledge and improve the quality of life for the general public.

The importance of natural disasters’ governance for macroeconomic performance and countries resilience

Purpose Natural disasters can undermine decades of development and threaten hundreds of lives. Previous research studies highlighted that natural disasters can cripple economic growth of one or many countries. The purpose of this paper is to ask the usefulness of natural disasters governance for macroeconomic performance and countries’ resilience. For that, this research emphasizes on the effects of natural disasters economic damages on the macroeconomic performance. Design/methodology/approach The used technique is the unbalanced panel data model which allows testing a big cross-section of population despite when one temporal observation for an individual is unavailable. The methodology focuses on testing the effect of natural disasters’ economic damages upon the main macroeconomic aggregates. For the study relevance, various tests were carried out by taking into account the different levels of development. So, two groups have been studied: developing countries (LDCs group) and developed countries (DCs group). Findings Findings point up that the effects of natural disasters’ economic damages differ from aggregate to another. In spite of the differences according to the development levels, effects are more serious for developing countries. Originality/value The originality of paper is justified by the lack of empirical studies that have questioned the links between natural disasters, macroeconomic performance and countries’ resilience. Furthermore, this study can be distinguished by the analysis of the effects for various aggregates at once and, in the same time, by allowing the comparison of these impacts by level of development.

Forecasting with unbalanced panel data

AbstractThis paper derives the best linear unbiased prediction (BLUP) for an unbalanced panel data model. Starting with a simple error component regression model with unbalanced panel data and random effects, it generalizes the BLUP derived by Taub (Journal of Econometrics, 1979, 10, 103–108) to unbalanced panels. Next it derives the BLUP for an unequally spaced panel data model with serial correlation of the AR(1) type in the remainder disturbances considered by Baltagi and Wu (Econometric Theory, 1999, 15, 814–823). This in turn extends the BLUP for a panel data model with AR(1) type remainder disturbances derived by Baltagi and Li (Journal of Forecasting, 1992, 11, 561–567) from the balanced to the unequally spaced panel data case. The derivations are easily implemented and reduce to tractable expressions using an extension of the Fuller and Battese (Journal of Econometrics, 1974, 2, 67–78) transformation from the balanced to the unbalanced panel data case.

A Study of Capital Structure of Indian Power Generation and Supply Firms: Unbalanced Panel Data

This paper reveals the determinants of capital structure of power generation and supply firms (PGSFs) in India for the period 1993-2004 using unbalanced panel data. We try for several estimation methods of panel data, random effects estimation technique is selected after using specification tests. The results are similar with earlier studies relating to traditional determinants of capital structure like tangibility, profitability, however signs of variables: size, growth opportunities, cost of borrowing, and liquidity in the firm effects and firm and time effects are correct but not significant. Among the macro-economic variables, inflation and bank development are significantly related with leverage of PGSFs. The findings from the unbalanced panel data model reveal presence of major theories of finance in the case of Indian PGSFs. Further study suggests, focus should be on designing pro-bank and stock market finance policies to overcome the problems faced by PGSFs. This study proposes to offer special treatment to individual and institutional taxpayers in tax rebate for increasing investment in the equity and bonds of PGSFs.

Inferences on the regression coefficients in panel data models: parametric bootstrap approach

This article presents a parametric bootstrap approach to inference on the regression coefficients in panel data models. We aim to propose a method that is easily applicable for implement hypothesis testing and construct confidence interval of the regression coefficients vector of balanced and unbalanced panel data models. We show the results of our simulation study to compare of our parametric bootstrap approach with other approaches and approximated methods based on a Monte Carlo simulation study.

Economic policy uncertainty and credit growth: Evidence from a global sample

This study extends the research of Bordo, Duca, and Koch (2016) and Hu and Gong (2018) by examining the influences of economic policy uncertainty (EPU) at domestic and global levels on aggregate bank credit growth. The empirical analysis is conducted through both supply and demand side factors of bank credit growth in 22 economies over the period 2001–2015. This study employs different measures of EPU and applies panel-corrected standard errors (PCSE) and feasible generalized least squares (FGLS), which are suitable for unbalanced panel data models. Three principal findings are follows. First, higher level of EPU has negative impact on bank credit growth, which is significant for domestic EPU measures. Second, the positive change in EPU appears to have favorable effects on bank credit growth. The effects in both cases are different for the credit demand and supply sides. The findings suggest the need for appropriate measures to tackle bank credit risk-taking activities in uncertain conditions. Third, the impacts of EPU in emerging economies are negative and somewhat stronger than in advanced economies.

Domestic Ban Versus Border Rejections: A Case of Oman’s Fish Exports to the EU

The decline of Oman’s fish exports to the European Union (EU) since mid-2000s has caused legitimate concerns among policy makers and exporters. However, the potential reasons for the decline have not been fully elucidated. To ascertain the underlying causes of such decline, this article empirically examines the relative significance of potential economic and policy-related factors such as border rejections influenced by health and safety measures, supply and demand capacities, domestic ban, domestic structural changes, and exchange rate fluctuations on Oman’s fish exports to the EU. The results obtained from the dynamic unbalanced panel data model for the period 2000-2013 indicate that fish exports to the EU markets have been influenced by the domestic ban on export, domestic structural changes, and exchange rate fluctuations rather than by border rejections. These findings provide important signal to policy makers of the respective countries in designing adaptive policy approach to address such influences.

Half‐panel jackknife fixed‐effects estimation of linear panels with weakly exogenous regressors

SummaryThis paper considers estimation and inference in linear panel regression models with lagged dependent variables and/or other weakly exogenous regressors when N (the cross‐section dimension) is large relative to T (the time series dimension). It allows for fixed and time effects (FE‐TE) and derives a general formula for the bias of the FE‐TE estimator which generalizes the well‐known Nickell bias formula derived for the pure autoregressive dynamic panel data models. It shows that in the presence of weakly exogenous regressors inference based on the FE‐TE estimator will result in size distortions unless N/T is sufficiently small. To deal with the bias and size distortion of the FE‐TE estimator the use of a half‐panel jackknife FE‐TE estimator is considered and its asymptotic distribution is derived. It is shown that the bias of the half‐panel jackknife FE‐TE estimator is of order T−2, and for valid inference it is only required that N/T3→0, as N,T→∞ jointly. Extension to unbalanced panel data models is also provided. The theoretical results are illustrated with Monte Carlo evidence. It is shown that the FE‐TE estimator can suffer from large size distortions when N>T, with the half‐panel jackknife FE‐TE estimator showing little size distortions. The use of half‐panel jackknife FE‐TE estimator is illustrated with two empirical applications from the literature.

Effect of Private Health Insurance on Medical Care Utilization: Six Year Unbalanced Panel Data Model

Effect of Private Health Insurance on Medical Care Utilization: Six Year Unbalanced Panel Data Model

Parametric bootstrap inferences for unbalanced panel data models

ABSTRACTThis article presents parametric bootstrap (PB) approaches for hypothesis testing and interval estimation for the regression coefficients of panel data regression models with incomplete panels. Some simulation results are presented to compare the performance of the PB approaches with the approximate inferences. Our studies show that the PB approaches perform satisfactorily for various sample sizes and parameter configurations, and the performance of PB approaches is mostly better than the approximate methods with respect to the coverage probabilities and the Type I error rates. The PB inferences have almost exact coverage probabilities and Type I error rates. Furthermore, the PB procedure can be simply carried out by a few simulation steps, and the derivation is easier to understand and to be extended to the multi-way error component regression models with unbalanced panels. Finally, the proposed approaches are illustrated by using a real data example.

Crash Frequency Modeling Using Real-Time Environmental and Traffic Data and Unbalanced Panel Data Models.

Traffic and environmental conditions (e.g., weather conditions), which frequently change with time, have a significant impact on crash occurrence. Traditional crash frequency models with large temporal scales and aggregated variables are not sufficient to capture the time-varying nature of driving environmental factors, causing significant loss of critical information on crash frequency modeling. This paper aims at developing crash frequency models with refined temporal scales for complex driving environments, with such an effort providing more detailed and accurate crash risk information which can allow for more effective and proactive traffic management and law enforcement intervention. Zero-inflated, negative binomial (ZINB) models with site-specific random effects are developed with unbalanced panel data to analyze hourly crash frequency on highway segments. The real-time driving environment information, including traffic, weather and road surface condition data, sourced primarily from the Road Weather Information System, is incorporated into the models along with site-specific road characteristics. The estimation results of unbalanced panel data ZINB models suggest there are a number of factors influencing crash frequency, including time-varying factors (e.g., visibility and hourly traffic volume) and site-varying factors (e.g., speed limit). The study confirms the unique significance of the real-time weather, road surface condition and traffic data to crash frequency modeling.

Discovering the impact of systemic and idiosyncratic risk factors on credit spread of corporate bond within the framework of intelligent knowledge management

This paper exploits the implied information of data collected from credit spreads of Chinese corporate bonds and systemic and idiosyncratic risk factors. We compute contribution of risk factors to credit spreads of Chinese corporate bonds by establishing the unbalanced panel data model, identify the key factors impacting the size of credit spreads of corporate bonds. Knowledge extracted by data mining is helpful to investors for reasonable pricing of bonds and making rational investment decisions. When selecting variables, the unbalanced panel data model is used to calculate the Zero-volatility credit spreads, which are more accurate. We use term structure adjusted return of bond index as the systemic risk factor of corporate bond market, the three Fama/French systemic factors as the systemic risk factors of stock markets and idiosyncratic stock/bond volatility and idiosyncratic bond value-at-risk as the idiosyncratic risk factors. Empirical analysis of corporate bonds sampling China’s listing Corporation issued and traded on Shanghai Stock Exchange from 2008 to 2011 shows that the size of credit spreads is mainly determined by the systemic risk factors of bond market, i.e. risk factors of stock market make very little contribution to the spread; the idiosyncratic risk factors also contribute. An interesting phenomenon is that we find that the relationship between idiosyncratic stock volatility and credit spread is negative, which is contrary to extant research while the relationship is positive and mainly focuses on impact of risk factors on credit spread of corporate bond.