Search results

This chapter extends the work of Baltagi, Bresson, Chaturvedi, and Lacroix (2018) to the popular dynamic panel data model. The authors investigate the robustness of Bayesian panel data models to possible misspecification of the prior distribution. The proposed robust Bayesian approach departs from the standard Bayesian framework in two ways. First, the authors consider the ε-contamination class of prior distributions for the model parameters as well as for the individual effects. Second, both the base elicited priors and the ε-contamination priors use Zellner’s (1986) g-priors for the variance–covariance matrices. The authors propose a general “toolbox” for a wide range of specifications which includes the dynamic panel model with random effects, with cross-correlated effects à la Chamberlain, for the Hausman–Taylor world and for dynamic panel data models with homogeneous/heterogeneous slopes and cross-sectional dependence. Using a Monte Carlo simulation study, the authors compare the finite sample properties of the proposed estimator to those of standard classical estimators. The chapter contributes to the dynamic panel data literature by proposing a general robust Bayesian framework which encompasses the conventional frequentist specifications and their associated estimation methods as special cases.

Panel data-based demand forecasting models have been widely adopted in various industrial settings over the past few decades. Despite being a highly versatile and intuitive method, in the literature, there is a lack of comprehensive review examining the strengths, the weaknesses, and the industrial applications of panel data-based demand forecasting models. The purpose of this paper is to fill this gap by reviewing and exploring the features of various main stream panel data-based demand forecasting models. A novel process, in the form of a flowchart, which helps practitioners to select the right panel data models for real world industrial applications, is developed. Future research directions are proposed and discussed.

It is a review paper. A systematically searched and carefully selected number of panel data-based forecasting models are examined analytically. Their features are also explored and revealed.

This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. A novel model selection process is developed to assist decision makers to select the right panel data models for their specific demand forecasting tasks. The strengths, weaknesses, and industrial applications of different panel data-based demand forecasting models are found. Future research agenda is proposed.

This review covers most commonly used and important panel data-based models for demand forecasting. However, some hybrid models, which combine the panel data-based models with other models, are not covered.

The reviewed panel data-based demand forecasting models are applicable in the real world. The proposed model selection flowchart is implementable in practice and it helps practitioners to select the right panel data-based models for the respective industrial applications.

This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. It is original.

The FDIC Improvement Act of 1991 sets out five categories of capital and mandates corrective action for banks. Each bank based on its capital amount fall in the certain categories or states. The purpose of this paper is to consider the effect of banking regulations and supervisory practices on capital state transition.

First, the authors investigate how much the practices influence banks' capital adequacy using a dynamic panel data method, the generalized method of moments. Then, to scrutinize the results of the first phase, the authors estimate the effect of practices on some characteristics of capital state transition such as transition intensity, transition probability and state sojourn time using multi-state models for panel data in 107 developing countries over the period 2000 to 2012.

The dynamic regression results show that capital guidelines, supervisory power and supervisory structure can have significantly positive effects on the capital adequacy state. Moreover, the multi-state Markov panel data model estimation results show that the significantly positive-effect practices can change the capital state transition intensity considerably; for example, they can transmit the critical-under-capitalized (the lowest) capital state of banks directly to a well or the adequate-capitalized (the highest) capital state without passing through middle states (under-capitalized and significantly-undercapitalized). Moreover, the results present some new evidence on transition probability and state sojourn time.

The main contribution of this paper, unlike the existing literature, is to consider the power of banking regulations and supervisory practices to improve the capital state using a multi-state Markov panel data model.

The study aims to explore and establish the relationship that exists between board independence and corporate social responsibility (CSR) practices of Indian firms.

A sample of 76 non-financial companies listed on the National Stock Exchange has been considered for a period of seven years (from 2013 to 2019). The study has used several statistical tools such as the static panel data model and the Arellano–Bond dynamic panel data model based on generalized method of moments approach.

The results of the analysis have indicated board independence to have a significant positive relationship with the firms’ CSR performance. However, board size and number of board meetings have been found to have a negative relationship with CSR. Further, outcomes have also revealed that variables such as companies’ size and liquidity have a positive effect on the extent of CSR activities performed.

The firms which have the intention to engage in impactful CSR activities should support the independent directors’ participation in companies’ boards. The study’s findings suggest the companies to appoint independent directors strategically, keeping in mind the requirements of their board. Also, the independent directors selected should be independent in true sense, i.e. they should not be acquaintances of the company’s chief executive officer. This would ensure unbiased decision-making and would enhance the company’s CSR performance.

In India, CSR has gained great importance. So much so that it was made mandatory by the Companies Act, 2013. However, research studies that may assist in understanding the influence of board independence on Indian firms’ CSR performance are still scarce. The present study would foster value to the existing set of limited literature. Besides, the study has considered the dynamic nature of the relationship and has also controlled the endogeneity bias which has been examined by few studies in the past.

This paper aims to empirically analyze the impact of export diversification on gross domestic product (GDP) per capita growth.

Using system generalized method of moments (GMM), a nonlinear model in a dynamic panel data growth framework for 101 countries between 1995 and 2019 was estimated.

Results evidenced that export concentration, measured by the Herfindahl–Hirschman Index (HHI), is negatively associated with GDP per capita growth after controlling for the effects of other explanatory variables. Further, the squared term of HHI used in the model to measure the nonlinear relationship between export concentration and economic growth indicated that the low-income and lower-middle-income countries benefited from export diversification. At the same time, high-income and upper-middle-income countries perform well with their export specialization. The results of the robustness check validate the findings of nonlinear estimation.

The findings recommend that low-income and lower-middle-income countries diversify their export basket to improve economic growth by generating stable export earnings. Similarly, high-income and upper-middle-income countries should focus on measures to close the product lines which no longer belong to their factor endowments and rebalance their export basket.

This study contributes to the existing literature by using the system GMM method, which is most appropriate for a dynamic panel data growth framework with up-to-date data. Further, this study segregates a large panel into 43 concentrated and 58 diversified countries to test the robustness of the empirical results.

The purpose of this paper is to examine the ability of well known fund characteristics such as the recent past performance, fund size, management fees, fund age, net asset value and fund growth so as to explain Tunisian equity mutual fund performance.

The sample was split according to investment objectives, and the advanced dynamic panel data approach was used over the period 1999‐2006.

The authors find that past performance and fund size have a positive and significant influence on future performance for all fund categories, irrespective of what performance measure was used. This may indicate the existence of scale economies in the Tunisian equity mutual fund industry. The author also find that the other fund characteristics play an important role in explaining performance, but their impact varies among the fund categories. In all, regression results support the dynamic links between fund characteristics and future performance.

The findings do not take into account the behaviour of fund managers and their ability to extend the investment opportunities set. It seems that there are more complex factors related to the strategic behaviour of the manager and driving differences in performance across funds than previous studies have indicated.

The authors confirm the empirical evidence that historical performance contains some information about future performance and such information may be important to mutual fund investors. It was also found that fund size is positively related to future performance of small fund category as well as of large fund category. This may indicate the existence of scale economies in the Tunisian equity mutual fund industry. In addition, the influence of the other control variables varies among the fund categories, but often is the same as in earlier studies.

The paper provides information to foreign investors for investing in Tunisian capital market.

In this regard, the study of literature revealed that the explanation of performance, based on quantitative factors, is often limited to a static approach that involves making estimates resting on multiple regression, regression in cross section and principal component analysis for short periods. However, several empirical studies highlight the impact of past performance on future performance. It seemed essential to enrich the analysis by using a dynamic approach.

The main purpose of this study is to describe and analyse the relationship between the 2008–2009 global financial crisis and small and medium-sized enterprises' cost of debt capital.

Statistical methods, including multiple OLS and dynamic panel data, were used to analyse a longitudinal cross-sectional panel dataset of 3865 Swedish SMEs operating in five industry sectors over the 2008–2015 period.

The results suggest that the cost of debt was influenced by the financial crisis and another macroeconomic factor, i.e. the interbank interest rate, and by firm-specific factors such as firm size and lagged cost of debt.

To the authors' best knowledge, this is one of few studies to examine the cost of debt among SMEs during the crisis and post-crisis periods using data from a large-scale, longitudinal, cross-sectional database.

We use panel data models to examine variations and changes in faculty employment at four-year colleges and universities in the United States. The share of part-time faculty among total faculty has continued to grow during the last two decades, while the share of full-time lecturers and instructors has been relatively stable. Meanwhile, the share of nontenure track faculty among full-time faculty has been growing, especially among the professorial ranks. Dynamic panel data models suggest that employment levels of different types of faculty respond to a variety of economic and institutional factors. Colleges and universities have increasingly employed faculty whose salaries and benefits are relatively inexpensive; the slowly deteriorating financial situations at most colleges and universities have led to an increasing reliance on a contingent academic workforce. A cross-sectional comparison of the share of full-time nontenure track faculty also reveals significant variations across institutions.

The purpose of this paper is to establish the empirical link between real exchange rate (RER) undervaluation and sectoral growth in South Africa between 1984 and 2014.

The study employs a dynamic panel data approach estimated by the system generalised method of moments technique in a bid to control for endogeneity.

The authors find a significant positive impact of undervaluation on sectoral growth which increases with capital accumulation. Also, the authors confirm that undervaluation promotes sectoral growth up to a point where further increases in undervaluation retards growth.

The results confirm the importance of policies that keep the domestic currency weaker to foster sectoral growth.

The originality of this paper lies in establishing the impact of exchange rate undervaluation on growth at a sector level in the context of South Africa using a dynamic panel data approach.

The purpose of this paper is to test the main theories of corporate debt maturity in a multi‐country framework, in an attempt to understand country‐specific constraints.

Dynamic panel data analysis estimated by the generalized method of moments, techniques that account properly for cross‐section and time series variation allowing for dynamic effects.

There is a substantial dynamic component in the determination of a firm's maturity structure; firms face moderate adjustment costs towards its optimal maturity, and the determinants of maturity structure and their effects are similar between Latin American countries and the USA; and there is a partial empirical support for each of the theoretical hypotheses tested.

Firm ownership, accounting standards, financial market depth, and the degree of supervision on financial reporting may vary across countries, which may affect the quality and consistency of some variables.

Firms face costs in adjusting the maturity of their debt, which gives such decision a long‐term character, and the determinants of debt maturity do not seem very sensitive to a country's business and financial environment.

The paper focuses on a sample of developing countries that have so far been ignored in empirical studies, employs empirical techniques that account properly for cross‐section and time series variation, and the model allows for dynamic effects that have seldom been considered in previous research.