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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 purpose of the paper is to examine the impact of infrastructure investment and development on economic growth in Brazil, Russia, India, China and South Africa (BRICS) countries. The effect is examined for each country separately and also collectively by combining each country.

Ordinary least square regression method is applied to examine the effects of infrastructure investment and development on economic growth for each country. Panel data techniques such as panel least square method, panel least square fixed-effect model and panel least square random effect model are used to examine the collective impact by combining all countries in BRICS. The dynamic panel model is also incorporated for analysis in the study.

The results of the study are mixed. The association between infrastructure investment and development and economic growth for countries within BRICS is not robust. There is an insignificant relationship between infrastructure investment and development and economic growth in Brazil and South Africa. Energy and transportation infrastructure investment and development lead to economic growth in Russia. Telecommunication infrastructure investment and development and economic growth have a negative relationship in India, whereas there is a negative association between transport infrastructure investment and development and economic growth in China. Panel data results conclude that energy infrastructure investment and development lead to economic growth, whereas telecommunication infrastructure investment and development are significant and negatively linked with economic growth.

The study is novel as time series analysis and panel data analysis are used, taking the time span for 38 years (1980–2017) to investigate the influence of infrastructure investment and development on economic growth in BRICS Countries. Time-series regression analysis is used to test the impact for individual countries separately, whereas panel data regression analysis is used to examine the impact collectively for all countries in BRICS.

We examine the roles of sample initial conditions and unobserved individual effects in consistent estimation of the dynamic binary response panel data model. Different specifications of the model are estimated using female welfare and labor force participation data from the Survey of Income and Program Participation. These include alternative random effects (RE) models, in which the conditional distributions of both the unobserved heterogeneity and the initial conditions are specified, and fixed effects (FE) conditional logit models that make no assumptions on either distribution. There are several findings. First, the hypothesis that the sample initial conditions are exogenous is rejected by both samples. Misspecification of the initial conditions results in drastically overstated estimates of the state dependence and understated estimates of the short- and long-run effects of children on labor force participation. The FE conditional logit estimates are similar to the estimates from the RE model that is flexible with respect to both the initial conditions and the correlation between the unobserved heterogeneity and the covariates. For female labor force participation, there is evidence that fertility choices are correlated with both unobserved heterogeneity and pre-sample participation histories.

This paper provides a selective survey of the panel macroeconometric techniques that focus on controlling the impact of “unobserved heterogeneity” across individuals and over time to obtain valid inference for “structures” that are common across individuals and over time. We consider issues of (i) estimating vector autoregressive models; (ii) testing of unit root or cointegration; (iii) statistical inference for dynamic simultaneous equations models; (iv) policy evaluation; and (v) aggregation and prediction.

We analyse the dynamics of social assistance benefit (SA) receipt among working-age adults in Britain between 1991 and 2005. The decline in the annual SA receipt rate was driven by a decline in the SA entry rate rather than by the SA exit rate (which also declined). We examine the determinants of these trends using a multivariate dynamic random effects probit model of SA receipt probabilities applied to British Household Panel Survey data. We show how the model may be used to derive year-by-year predictions of aggregate SA entry, exit and receipt rates. The analysis highlights the importance of the decline in the unemployment rate over the period and other changes in the socio-economic environment including two reforms to the income maintenance system in the 1990s and also illustrates the effects of self-selection (‘creaming’) on observed and unobserved characteristics.

The purpose of this paper is to indicate trade characteristics of Foreign direct investment (FDI) inflows in China and examine the dynamic interaction between FDI inflows and China’s international trade through empirical analysis.

At first, this paper builds the probability distribution model (Poisson and negative binomial (NB)) to capture the characteristics of spatial distribution of all kinds of FDI firms in Chinese cities and provinces based on count data, so as to indicate the potentials for further introducing FDI inflows in China; Second, this paper investigates the effects of trade on FDI firms inflows based on probability regress model (Binary Logit, Tobit, NB, Poisson, zero inflated negative binomial) and shows how international trade accelerates the different kinds of FDI firms to agglomerate in Eastern, Middle and Western region by the endowments of factors; third, this paper empirically examines the magnitude and characteristics of trade effects generated by FDI inflows by building dynamic panel model based on continuous data.

First, statistical tests of probability distribution model based on count data show that there are characteristics of spatial agglomeration of FDI firms such as manufacture firm, R & D firm, managing and marketing firm and total sectors, which obey NB distribution as whole; Second, this study indicate that FDI inflows have strong positive effects on the international trade in China’s provinces and on China’s regional trade, and that most of foreign firms in China are export oriented being strongly characterized as labor-intensive industries, especially, contributions of FDI to imports are greater than the contributions of FDI to exports in China’s Middle and Western trade, and the growth of FDI trade in China’s trade volume has been strong over the past years; third, the empirical results of models based on count data and continuous data indicate that FDI inflows have significantly positive relationship with international trade, that is, the relationship between FDI and international trade in the case of China is the characteristics with complement and imports substituting relationship.

Because of mixed data set for FDI inflows of processing and assembling trade and production-oriented FDI, efficiency-seeking and knowledge or technology – intensive FDI inflows in the past 36 years, the paper only investigate characteristics of FDI inflows in China before the turning point of financial crisis, but it is important for capturing the whole picture of trade characteristics of FDI inflows in China.

The derived quantitative results imply that there are still greater potentials for further introducing FDI inflows in China, and decision-maker should make policy of introducing FDI inflows which are favorable to supporting innovative activities and economic agglomeration, and preferably encourage efficiency-seeking and export-oriented FDI inflows so as enhance quality and efficiency of economic growth, which are also helpful to accelerate upgrade of Chinese industry and gradually shorten gap of growth among Eastern, Middle and Western region.

FDI inflows in China not only stimulate the remarkable growth of bilateral trade between host country and home country, but also promote the growth of international trade between China and the rest of the world. Thus, policies of bilateral or multilateral free-trade and investment area should be encouraged, which will be also favorable to promote the growth and welfare in all the regions.

This paper demonstrates that spatial distributions of FDI firms in Chinese cities and provinces obey NB probability distribution pattern, and puts forward the methodology of model based on count data and continuous data. Besides, this paper quantitatively indicates trade characteristics of FDI inflows in China as well as the dynamic interaction between FDI inflows and China’s international trade.

In this paper, we study a partially linear dynamic panel data model with fixed effects, where either exogenous or endogenous variables or both enter the linear part, and the lagged-dependent variable together with some other exogenous variables enter the nonparametric part. Two types of estimation methods are proposed for the first-differenced model. One is composed of a semiparametric GMM estimator for the finite-dimensional parameter θ and a local polynomial estimator for the infinite-dimensional parameter m based on the empirical solutions to Fredholm integral equations of the second kind, and the other is a sieve IV estimate of the parametric and nonparametric components jointly. We study the asymptotic properties for these two types of estimates when the number of individuals N tends to ∞ and the time period T is fixed. We also propose a specification test for the linearity of the nonparametric component based on a weighted square distance between the parametric estimate under the linear restriction and the semiparametric estimate under the alternative. Monte Carlo simulations suggest that the proposed estimators and tests perform well in finite samples. We apply the model to study the relationship between intellectual property right (IPR) protection and economic growth, and find that IPR has a non-linear positive effect on the economic growth rate.