Search results

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 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.

Empirical accounting research frequently makes use of data sets with a time‐series and a cross‐sectional dimension ‐ a panel of data. The literature review indicates that South African researchers infrequently allow for heterogeneity between firms when using panel data and the empirical example shows that regression results that allow for firm heterogeneity are materially different from regression results that assume homogeneity among firms. The econometric analysis of panel data has advanced significantly in recent years and accounting researchers should benefit from those improvements.

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.

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.

The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

The purpose of this paper is to explore the relationship between the ratification of the United Nations’ (UN’s) Convention on the Elimination of All Forms of Discrimination against Women (CEDAW) and women’s and girls’ health outcomes using a unique longitudinal data set of 192 UN-member countries that encompasses the years from 1980 to 2011.

The authors focus on the impact of CEDAW ratification, number of reports submitted after ratification, years passed since ratification, and the dynamic impact of CEDAW ratification by utilizing ordinary least squares (OLS) and panel fixed effects methods. The study investigates the following women’s and girls’ health outcomes: total fertility rate, adolescent fertility rate, infant mortality rate, maternal mortality ratio, neonatal mortality rate, female life expectancy at birth (FLEB), and female to male life expectancy at birth.

The OLS and panel country and year fixed effects models provide evidence that the impact of CEDAW ratification on women’s and girls’ health outcomes varies by global regions. While the authors find no significant gains in health outcomes in European and North-American countries, the countries in the Northern Africa, sub-Saharan Africa, Southern Africa, Caribbean and Central America, South America, Middle-East, Eastern Asia, and Oceania regions experienced the biggest gains from CEDAW ratification, exhibiting reductions in total fertility, adolescent fertility, infant mortality, maternal mortality, and neonatal mortality while also showing improvements in FLEB. The results provide evidence that both early commitment to CEDAW as measured by the total number of years of engagement after the UN’s 1980 ratification and the timely submission of mandatory CEDAW reports have positive impacts on women’ and girls’ health outcomes. Several sensitivity tests confirm the robustness of main findings.

This study is the first comprehensive attempt to explore the multifaceted relationships between CEDAW ratification and female health outcomes. The study significantly expands on the methods of earlier research and presents novel methods and findings on the relationship between CEDAW ratification and women’s health outcomes. The findings suggest that the impact of CEDAW ratification significantly depends on the country’s region. Furthermore, stronger engagement with CEDAW (as indicated by the total number of years following country ratification) and the submission of the required CEDAW reports (as outlined in the Convention’s guidelines) have positive impacts on women’s and girls’ health outcomes.

A semiparametric stochastic frontier model is proposed for panel data, incorporating several flexible features. First, a constant elasticity of substitution (CES) production frontier is considered without log-transformation to prevent induced non-negligible estimation bias. Second, the model flexibility is improved via semiparameterization, where the technology is an unknown function of a set of environment variables. The technology function accounts for latent heterogeneity across individual units, which can be freely correlated with inputs, environment variables, and/or inefficiency determinants. Furthermore, the technology function incorporates a single-index structure to circumvent the curse of dimensionality. Third, distributional assumptions are eschewed on both stochastic noise and inefficiency for model identification. Instead, only the conditional mean of the inefficiency is assumed, which depends on related determinants with a wide range of choice, via a positive parametric function. As a result, technical efficiency is constructed without relying on an assumed distribution on composite error. The model provides flexible structures on both the production frontier and inefficiency, thereby alleviating the risk of model misspecification in production and efficiency analysis. The estimator involves a series based nonlinear least squares estimation for the unknown parameters and a kernel based local estimation for the technology function. Promising finite-sample performance is demonstrated through simulations, and the model is applied to investigate productive efficiency among OECD countries from 1970–2019.

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.

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.