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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.

In the United States, the 1993 Government Performance and Result Act (GPRA) has increased public demand for governments not only to produce and deliver public goods and services, but also to demonstrate program effectiveness, which is the ultimate goal of a public program's existence, mission, and spending. This public management approach parallels the results-oriented management, which aims to strengthen organizational effectiveness and emphasize the need to integrate all major activities and functions, an activity that will direct them toward advancing organization-wide strategic goals or fundamental policy agendas (Kettl, 1997). Managers use program outputs and outcomes as implementation benchmarks to identify implementation means or directions (Kettl, 1997). By reporting performance measurement results to the public and policy makers, public managers are held accountable for the tax-dollars spent to produce and deliver public services in the most efficient and effective way (Aristigueta, 2007). Performance measurement results, especially those related to outcome achievement, are partially useful in budget allocation from the perspective that the tax-dollars spent are tied to desirable outcomes rather than to program input costs that may or may not correspond with public desires (DuPont-Morales & Harris, 1994).

There is a growing literature in nonparametric econometrics in the recent two decades. Given the space limitation, it is impossible to survey all the important recent developments in nonparametric econometrics. Therefore, we choose to limit our focus on the following areas. In Section 2, we review the recent developments of nonparametric estimation and testing of regression functions with mixed discrete and continuous covariates. We discuss nonparametric estimation and testing of econometric models for nonstationary data in Section 3. Section 4 is devoted to surveying the literature of nonparametric instrumental variable (IV) models. We review nonparametric estimation of quantile regression models in Section 5. In Sections 2–5, we also point out some open research problems, which might be useful for graduate students to review the important research papers in this field and to search for their own research interests, particularly dissertation topics for doctoral students. Finally, in Section 6 we highlight some important research areas that are not covered in this paper due to space limitation. We plan to write a separate survey paper to discuss some of the omitted topics.

The generality of the universe is “everything.” In this regard, the Qur'an declares abundantly on the ultimate and perfect domain of the divine law in creation. This also means the profundity of the divine law in explaining and applying to all things in the knowledge–time–space dimensions of the conscious universe. Such observations or insights that span the complete universe comprise the Signs of Allah (ayath Allah).¹

A dynamic oligopoly model of the cigarette industry is developed to study the responses of firms to various antismoking policies and to estimate the implications for the policy efficacy. The structural parameters are estimated using a combination of micro and macro level data and firms’ optimal price and advertising strategies are solved as a Markov Perfect Nash Equilibrium. The simulation results show that tobacco tax increase reduces both the overall smoking rate and the youth smoking rate, while advertising restrictions may increase the youth smoking rate. Firm’s responses strengthen the impact of antismoking policies in the short run.

This paper develops a cross-sectionally augmented distributed lag (CS-DL) approach to the estimation of long-run effects in large dynamic heterogeneous panel data models with cross-sectionally dependent errors. The asymptotic distribution of the CS-DL estimator is derived under coefficient heterogeneity in the case where the time dimension (T ) and the cross-section dimension (N ) are both large. The CS-DL approach is compared with more standard panel data estimators that are based on autoregressive distributed lag (ARDL) specifications. It is shown that unlike the ARDL-type estimator, the CS-DL estimator is robust to misspecification of dynamics and error serial correlation. The theoretical results are illustrated with small sample evidence obtained by means of Monte Carlo simulations, which suggest that the performance of the CS-DL approach is often superior to the alternative panel ARDL estimates, particularly when T is not too large and lies in the range of 30–50.

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 aim of this chapter is to show the limits of the European policy model and to support the existence, through straightforward empirical analysis, of an inverse relationship both in the short run and in the long run between trust in institutions and unemployment. The empirical methodology relies on dynamic panel data techniques allowing measuring in a single equation both the long-run relationship and the short-run speed of adjustment among variables. This connection appears to be valid both in the Eurozone considered as a whole and in particular in peripheral countries, where the macroeconomic dynamics have been, under this respect, much more divergent from the average. This outcome allows proofing that to consolidate the European process of integration in the long run, institutions should have as main objective not only inflation but especially unemployment.