Search results

This chapter revisits the Hausman (1978) test for panel data. It emphasizes that it is a general specification test and that rejection of the null signals misspecification and is not an endorsement of the fixed effects estimator as is done in practice. Non-rejection of the null provides support for the random effects estimator which is efficient under the null. The chapter offers practical tips on what to do in case the null is rejected including checking for endogeneity of the regressors, misspecified dynamics, and applying a nonparametric Hausman test, see Amini, Delgado, Henderson, and Parmeter (2012, chapter 16). Alternatively, for the fixed effects die hard, the chapter suggests testing the fixed effects restrictions before adopting this estimator. The chapter also recommends a pretest estimator that is based on an additional Hausman test based on the difference between the Hausman and Taylor estimator and the fixed effects estimator.

Hausman (1978) represented a tectonic shift in inference related to the specification of econometric models. The seminal insight that one could compare two models which were both consistent under the null spawned a test which was both simple and powerful. The so-called ‘Hausman test’ has been applied and extended theoretically in a variety of econometric domains. This paper discusses the basic Hausman test and its development within econometric panel data settings since its publication. We focus on the construction of the Hausman test in a variety of panel data settings, and in particular, the recent adaptation of the Hausman test to semiparametric and nonparametric panel data models. We present simulation experiments which show the value of the Hausman test in a nonparametric setting, focusing primarily on the consequences of parametric model misspecification for the Hausman test procedure. A formal application of the Hausman test is also given focusing on testing between fixed and random effects within a panel data model of gasoline demand.

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.

This chapter proposes semiparametric estimation of the relationship between growth rate of GDP per capita, growth rates of physical and human capital, labor as well as other covariates and common trends for a panel of 23 OECD countries observed over the period 1971–2015. The observed differentiated behaviors by country reveal strong heterogeneity. This is the motivation behind using a mixed fixed- and random coefficients model to estimate this relationship. In particular, this chapter uses a semiparametric specification with random intercepts and slopes coefficients. Motivated by Lee and Wand (2016), the authors estimate a mean field variational Bayes semiparametric model with random coefficients for this panel of countries. Results reveal nonparametric specifications for the common trends. The use of this flexible methodology may enrich the empirical growth literature underlining a large diversity of responses across variables and countries.

We investigate the question of whether investing in a child’s development by having a parent stay at home when the child is young is correlated with the child’s adult outcomes. Specifically, do children with stay-at-home mothers have higher adult earnings than children raised in households with a working mother? The major contribution of our study is that, unlike previous studies, we have access to rich longitudinal data that allows us to measure both the parental earnings when the child is very young and the adult earnings of the child. Our findings are consistent with previous studies that show insignificant differences between children raised by stay-at-home mothers during their early years and children with mothers working in the market. We find no impact of maternal employment during the first five years of a child’s life on earnings, employment, or mobility measures of either sons or daughters. We do find, however, that maternal employment during children’s high school years is correlated with a higher probability of employment as adults for daughters and a higher correlation between parent and daughter earnings ranks.

The purpose of this paper is to investigate the impact of firm-specific (i.e. firm size, profitability, leverage, dividend, growth opportunities, management quality and firm age) and country-specific (i.e., gross domestic product [GDP] growth) variables on compensation/remuneration offered to chief executive officers (CEOs) working in different industries of Pakistan.

Panel data techniques, namely, pooled ordinary least squares, fixed effects and random effects methods are used to estimate the results. Moreover, Hausman test is used to choose which estimation method, either fixed effects or random effects, is better to explain the results.

Firm size, profitability, leverage, growth opportunities and age are some important firm-specific factors that have mixed (i.e. positive/negative) impact on CEO compensation in different industries. Variations in results are due to industry dynamics. However, it is important to mention that three key variables, namely, dividend, management quality and GDP growth have shown consistent positive impact on CEO compensation in most of the industries. In sum, results show that firm-specific and country-specific variables have material effects on CEO compensation. Moreover, results are found consistent with the predictions of agency theory and human capital theory.

The authors are sure that findings of this study provide some support to the board of directors to determine the pay slice for CEOs. Moreover, findings provide support to the regulatory authorities in formulating mechanisms to determine the compensation package for CEOs working in different industries and to improve the Code of Corporate Governance.

To the best of the authors’ knowledge, no empirical study in Pakistan has yet estimated the effects of firm-specific and country-specific variables on compensation offered to CEOs working in different industries. Thus, industry-wise analysis provides some new insights to the decision-makers and lays some foundation upon which a more detail analysis could be based.

The purpose of this paper is to examine the impact of the size of government and public debt on real economic growth, for a panel of 175 countries around the world.

The paper utilizes the fixed‐effects and random‐effects techniques to estimate the panel regressions.

The results indicate that both the size of government and the extent of government indebtedness have negative effects on economic growth.

The findings suggest that the authorities ought to take the necessary steps to curtail excessive government spending and public debts, in order to promote economic growth.

The contribution of the paper is its application of the fixed‐ and random‐effects techniques in modeling the relation of real economic growth to the size of government and public debt, for a panel of 175 countries around the world.

An adverse health event can affect women’s work capacity as they need time to recover. The institutional framework in the Netherlands provides employment protection during the first two years after the diagnosis. In this study, we have assessed the extent to which women’s employment is affected in the short- and long term by an adverse health event. We have used administrative Dutch data which follow women aged 25 to 55 years for four years after a medical diagnosis. We found that diagnosed women start leaving employment during the protection period and four years later they were about one percentage point less likely to be employed. Women in permanent employment did not reduce their employment during the protection period and reduced their employment with less than 0.5 percentage points thereafter. Furthermore, we found minor adjustments in the working hours in the short term and no adjustments in the long term. Lastly, we found that for wages, and not for employment and hours, adjustments could be related to the severity of the health condition: women diagnosed with temporary health conditions experienced a short-term wage penalty of about 0.5–1.7 percent and those diagnosed with chronic and incapacitating conditions experienced a long-term wage penalty of about 0.5 percent, while women diagnosed with some chronic and nonincapacitating conditions, such as respiratory conditions, experienced no wage changes in the short or long term.

Renewable energy (RE) is an important component to the complex portfolio of technologies that have the potential to reduce CO₂ emissions and to enhance the security of energy supplies. Despite RE's potential to reduce CO₂ emissions, the expenditure on renewable energy research, development, and demonstration (RERD&D) as a percentage of total government energy research, development, and demonstration (ERD&D) investment remains low in developed countries. The declining ERD&D expenditure prompted this research to explore the relationship between CO₂ emissions per capita and RERD&D as opposed to ERD&D.

An econometric analysis of annual CO₂ emissions per capita during the period 1990‐2004 for the 15 pre‐2004 European Union (EU15) countries was carried out. It was hypothesized that the impact of RERD&D expenditure on the reduction of CO₂ emissions would be higher than that of ERD&D expenditure, primarily due to several RE technologies being close to carbon neutral. Country‐level gross domestic product per capita and an index of the ratio between industry consumption and industrial production were introduced in the analysis as proxies to control for activities that generate CO₂ emissions. A number of panel data econometric models that are able to take into account both country‐ and time‐specific unobserved effects were explored.

It was found that random effect models were more appropriate to examine the study hypothesis. The results suggest that expenditure on RERD&D is statistically significant and negatively associated with CO₂ emissions per capita in all models, whereas expenditure on ERD&D is statistically insignificant (ceteris paribus).

The findings of this paper provide useful insight into the effectiveness of RERD&D investment in reducing CO₂ emissions and are of value in the development of policies for targeted research, development, and demonstration investment to mitigate the impacts of climate change.

The purpose of this paper is to examine the variation and dispersion of prime retail yields in eight Asia‐Pacific centres. It seeks to provide empirical evidence on the significance of real estate and capital market influences as systematic drivers of retail yields in the sample of eight cities. The aim is to build a model that enables market participants to obtain base case yield forecasts.

A panel model is deployed in this study utilising a database of yields of eight years (2001‐2007). The small number of observations for retail yields across cities is addressed with this approach, which combines time‐series and cross‐section data. A fixed‐effect specification allows for city specific influences that partially capture the heterogeneity of cities in the sample. Within this framework the influence of time varying factors across markets and random effects on yields is examined.

The empirical estimates established significant influences from real rent growth and interest rates on retail yields explaining 78 per cent of their variation when allowed for fixed effects. Systematic time influences and market size are not significant. Retail yields are found fairly sensitive to long‐term interest (LTI) rates with 1 per cent change in LTI rates resulting in an over 80 basis points shift in yields. In general, investors should be aware of interest rate shocks as these can move retail yields in the region significantly. Based on the actual and simulated values for 2007 Shanghai and Hong Kong are broadly fairly priced. In Tokyo, Sydney and Singapore retail yields are somewhat lower than the simulated values, which are attributed to greater liquidity and transparency in these markets than indicating over‐pricing. In Delhi, the prime yield above the actual a sign of a possible outward movement is found. Beijing appears under‐priced. Finally, in Mumbai, which has the highest yield in the sample, the simulated yield is below actual as per 2007. An adjustment may not be expected as this difference is attributed to the pricing of supply risks in this market.

This study addresses the dearth of research work on retail yields in the Asia‐Pacific region. Through the panel methodology proposed market participants can obtain fundamentals‐based forecasts for prime retail yields in the sample of the eight cities, understand the exposure to interest rate movements and make calls as to whether markets are mispriced. The study shows that pooling data and panel techniques represent a good option to study market dynamics in situations of small datasets.