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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 article provides an overview of the panel vector autoregressive models (VAR) used in macroeconomics and finance to study the dynamic relationships between heterogeneous assets, households, firms, sectors, and countries. We discuss what their distinctive features are, what they are used for, and how they can be derived from economic theory. We also describe how they are estimated and how shock identification is performed. We compare panel VAR models to other approaches used in the literature to estimate dynamic models involving heterogeneous units. Finally, we show how structural time variation can be dealt with.

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.

This chapter develops robust panel estimation in the form of trimmed mean group estimation for potentially heterogenous panel regression models. It trims outlying individuals of which the sample variances of regressors are either extremely small or large. The limiting distribution of the trimmed estimator can be obtained in a similar way to the standard mean group (MG) estimator, provided the random coefficients are conditionally homoskedastic. The authors consider two trimming methods. The first one is based on the order statistic of the sample variance of each regressor. The second one is based on the Mahalanobis depth of the sample variances of regressors. The authors apply them to the MG estimation of the two-way fixed effects model with potentially heterogeneous slope parameters and to the common correlated effects regression, and the authors derive limiting distribution of each estimator. As an empirical illustration, the authors consider the effect of police on property crime rates using the US state-level panel data.

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.

This paper aims to use the newly developed panel data cointegration analysis and the dynamic time series modeling approach to examine the linkages between financial structure (market‐based vs bank‐based) and economic growth in African economies.

The research investigates the dynamic relationship between financial structure and economic growth in a panel of a group of seven African developing countries over the period of 1986‐2007. The paper uses various indicators/measures of financial structure and financial system, and employs the traditional time‐series analysis for causality as well as the newly developed panel unit root and cointegration techniques and estimated finance‐growth relationship using FMOLS for heterogeneous panel.

From the dynamic heterogeneous panel approach, the paper firstly finds that market‐based financial system is important for explaining output growth through enhancing efficiency and productivity. Second, the authors' empirical evidence supports the view that higher levels of banking system development are positively associated with capital accumulation growth and lead to faster rates of economic growth.

Panel cointegration, group mean panel FMOLS and country‐by‐country time series investigations indicate that the market‐based financial system is important for explaining output growth through enhancing efficiency and productivity, whereas the development of banking system is significantly associated with capital accumulation growth. Further results from the time‐series approach show evidence of unidirectional causality running from market‐oriented as well as bank‐oriented financial systems to economic growth.

With heterogeneous findings dominating the growth and natural resources relations, there is a need to explain the variances in Africa's growth process as induced by robust measures of factor endowments. This study used a comprehensive set of data from the updated database of the World Bank to capture the heterogeneous dimensions of natural resource endowments on growth with a particular focus on establishing complementary evidence on the resource curse hypothesis in energy and environmental economics literature in Africa. These comprehensive data on oil rent, coal rent and forest rent could provide new and insightful evidence on obscure relations on the subject matter.

This paper considers the panel vector error correction model (PVECM) procedure to explain changes in economic growth outcomes as induced by oil rent, coal rent and forest rent. The consideration of the PVECM was premised on the panel unit root process that returns series that were cointegrated at the first-order differentials.

The paper found positive relations between oil rent, coal rent and economic development in Africa. Forest rent, on the other hand, is inversely related to economic growth in Africa. Trade and human capital are positively related to economic growth in Africa, while population growth is negatively associated with economic growth in Africa.

Short-run policies should be tailored towards the stability of fiscal expenditure such that the objective of fiscal policy, which is to maintain the condition of full employment and economic stability and stabilise the rate of growth, can be optimised and sustained. By this, the resource curse will be averted and productive capacity will increase, leading to sustainable growth and development in Africa, where conditions for growth and development remain inadequately met.

The originality of this paper can be viewed from the strength of its arguments and methods adopted to address the questions raised in this paper. This study further illuminated age-long obscure relations in the literature of natural resource endowment and economic growth by taking a disaggregated approach to the component-by-component analysis of natural resources factors (the oil rent, coal rent and forest rent) and their corresponding influence on economic growth in Africa. This pattern remains underexplored mainly in previous literature on the subject. Many African countries are blessed with an abundance of these different natural resources in varying proportions. The misuse and mismanagement of these resources along various dimensions have been the core of the inclination towards the resource curse hypothesis in Africa. Knowing how growth conditions respond to changes in the depth of forest resources, oil resources and coal resources could be useful pointers in Africa's overall energy use and management. This study contributed to the literature on natural resource-induced growth dynamics by offering a generalisable conclusion as to why natural resource-abundance economies are prone to poor economic performance. This study further asks if mineral deposits are a source or reflection of ill growth and underdevelopment in African countries.

This paper documents a robust empirical regularity: higher trade openness is associated with a lower unemployment rate. This paper also examines whether or not the effects of trade liberalisation depend on countries' income levels. Further, the dynamic causation between trade openness and unemployment is also examined.

In order to obtain insight into the openness–unemployment nexus, following empirical methods were utilised - static panel models, dynamic panel models and a novel panel Granger causality approach proposed by Juodis et al. (2021).

Results suggest that openness negatively affects unemployment; the extent to which trade liberalisation affects unemployment depends on the income level of each country. The Juodis, Karavias, and Sarafidis (JKS) test confirmed that the past values of trade openness, inflation, foreign direct investment and gross domestic product per capita contain information that helps to predict unemployment in a more robust manner. To simply put, opening upto trade may eventually become a requirement for creating more job opportunities, but this alone may not be enough. The extent to which nations benefit from trade liberalisation is largely dependent on the overall economic conditions and their capability to move up the income scale.

A major difference between this study and those performed previously is that this study does not only examine the impact of trade openness on unemployment, but also investigates whether the unemployment effect of liberalisation is affected by countries' income levels – an issue that has received little attention in the past. Additionally, the unique panel non-causality approach put forth by Juodis et al. (2021) is used in the first instance to look into the causal link between trade openness and unemployment. This method has advantages in that the method enables capturing Granger-causality in homogeneous or heterogeneous panels amongst multiple variables.

The purpose of this paper is to empirically analyse how different exchange rate regimes affect the links between monetary fundamentals and exchange rates in Sub-Saharan Africa.

Using the Pedroni method for panel cointegration, mean group and pooled mean group and the panel vector autoregressive technique, this study empirically investigates whether monetary fundamentals impact exchange rates similarly in both regimes. Thus, the author acquires needed and credible empirical data.

The result suggests that the impact is dissimilar. In the floating regime, an increase in relative money supply and relative real output depreciates and appreciates the nominal exchange rate in the long run whereas in the non-floating regime, the evidence is mixed. Thus, exchange rates bear a theoretically consistent relationship with monetary fundamentals across SSA countries with floating regimes but fails under non-floating regimes. This provides evidence that regime choice is important if the relationship between monetary fundamentals and exchange rates in SSA are to be theoretically consistent.

This study empirically incorporates the dissimilarities in exchange rate regimes in a panel framework and study the links between exchange rates and monetary fundamentals. The focus on how exchange rate regimes might alter the equilibrium relationships between exchange rates and monetary fundamentals in SSA is a pioneering experiment.