Unit Averaging for Heterogeneous Panels

Purpose Motivated by recent rapid exchange rate depreciations, shrank economic growth, high inflation, and persistent trade deficits, this study examines the trade balance (TB) in the face of the recent dynamics of the stated macroeconomic factors, which are also important determinants of the TB. The symmetric test of the J-curve phenomenon for the selected Sub-Saharan African (SSA) countries is revisited in this regard. The study uses panel data from 1970 to 2020 for ten of these countries for the longitudinal panel analysis with the TB as the dependent variable and the real exchange rate, foreign and domestic national incomes, and trade openness as the set of independent variables.Design/methodology/approach Because the underlying data set involves a heterogeneous panel of relatively short N and long T, the pooled mean group (PMG) and mean group (MG) heterogeneous panel models are employed based on the Hausman test for parameter consistency in heterogeneous panels.Findings The findings largely support the domestic income growth– TB worsening and the foreign income growth– TB improvement hypotheses. Trade openness is found to mostly augment the TB performance of the countries. The results also validated the J-curve effect for only 3/10 and 2/10 countries in the PMG and MG models, respectively. The divergence for most of the countries is attributed to possible import compression and institutional structure of SSA countries.Practical implications Given the favorable effects of trade openness on the TB performance of SSA countries, it is recommended that SSA countries place much emphasis on import-substitution industrialization and value addition to their natural resources as well as investment-driven growth policies to improve the competitiveness of their exports and reverse the chronic deficits in their TBs.Originality/value This paper is unique for invoking heterogeneous panel models to analyze the TB in light of recent dynamics of its determinants, as well as providing an update on the symmetric test of the J-curve phenomenon for the selected SSA countries.

A two-stage estimator for heterogeneous panel models with common factors

Domestic air passenger traffic and economic growth in China: Evidence from heterogeneous panel models

PurposeFor mitigating climate change, renewable energy consumption is recognized as one of the policy measures worldwide. However, there is a dearth of empirical studies focusing on education as one of the determinants of renewable energy consumption in the existing literature. Thus, this study aims to explore the impact of education, economic growth and foreign direct investment, financial development, CO2 emissions and urbanization on renewable energy consumption.Design/methodology/approachThis study considers a balanced panel of selected South Asian Association of Regional Cooperation (SAARC) countries, namely, India, Pakistan, Sri Lanka, Nepal and Bangladesh, during the period 1995–2015. The study uses sophisticated second-generation panel data models for empirical analysis.FindingsThe result reveals that education and economic growth significantly enhance renewable energy consumption, whereas foreign direct investment, financial development, CO2 emissions and urbanization reduce it. Further, unidirectional causality from education, economic growth and urbanization to renewable energy consumption was observed, whereas a bidirectional causality was found between renewable energy consumption and financial development.Practical implicationsThe emanated finding of this study is supposed to be helpful for the environmentalists, economists, banking sector and the practitioners in urban development can take insights from the study while framing the energy policy.Originality/valueThis is the first study that examines the role of education on renewable energy consumption in heterogeneous panel data settings for the selected SAARC countries.

This article adopts a new technique, developed by Hurlin (2004 Hurlin, C. 2004. Un test simple de l’Hypothèse de non causalité dans un modèle de panel hétérogène (a simple test of the non causality assumption in a heterogeneous panel model). Revue Economique, 56: 3 [Google Scholar]), to test for Granger causality between capital structure and corporate operating characteristics including time-invariant, firm-specific effects in heterogeneous panel data from five US industries over the period 1980 to 2002. Previous studies addressed the issue of whether corporate operating characteristics cause changes in capital structure while our study focuses on the causal linkages between capital structure and corporate operating characteristics. For robustness, we validated the results using the Mixed Fixed Random (MFR) technique developed by Nair-Reichert and Weinhold (2001 Nair-Reichert, U and Weinhold, D. 2001. Causality tests for cross-country panels: a look at FDI and economic growth in less developed countries. Oxford Bulletin of Economics and Statistics, 63: 153–71. [Crossref], [Web of Science ®] , [Google Scholar]). The results indicate that causality test is more revealing than correlation-based analyses. It is clear that capital structure theories are co-existent in different industries. The study provides ample evidence that simultaneity between corporate operating characteristics and capital structure is prevalent with differential results in different industries and forms of debt.

Revisiting the persistence of real exchange rates

The Supplemental Nutrition Assistance Program (SNAP) has grown rapidly over the past 2 decades. A large literature relies on state‐level panel data on SNAP enrollment and implements traditional two‐way fixed effects estimators to identify the impact of economic conditions on SNAP enrollment. This empirical strategy implicitly assumes slope parameter homogeneity and ignores the possibility of cross‐sectional dependence in the regression error terms. The latter could feasibly arise in state‐level panel data if the time‐varying unobserved common shocks, such as national financial crises, have differential effects on SNAP participation across states in the United States. This study empirically evaluates the appropriateness of these two assumptions by adopting a more general common factor model, allowing for slope parameter heterogeneity and error term cross‐sectional dependence both separately and jointly. We find that although assuming a common slope parameter across states does not seem problematic for identification, allowing for the error term cross‐sectional dependence leads to a roughly 40% reduction in the estimated long‐run impact of the unemployment rate on SNAP enrollment. This finding has important implications for policymaking decisions—even small biases could lead to suboptimal policy responses considering the program's size. Our counterfactual simulations support our main results, implying the importance of carefully accounting for time‐varying unobserved heterogeneity when studying the cyclicality of SNAP enrollment using state‐level panel data.

This paper examines the link between energy consumption and economic growth using a panel data sample of 27 Southern countries observed over the period 2002-2020. To capture long-run effects, we used appropriate panel data econometric techniques in the presence of cross-sectional dependence and slope heterogeneity, while Dumitrescu and Hurlin (2012) causality tests examined short-run causal effects. Our results show that (i) in the long run, energy consumption has a negative and significant effect on economic growth in all countries, while other variables have a positive and significant effect, except institutional quality form, which is not significant both in short and long run on economic growth. (ii) in the short run, all variables have a positive and significant effect on economic and, (iii) causality tests confirm the feedback hypothesis between energy consumption and growth. The study concludes by calling on policymakers to strengthen institutional qualities and adopt financial and energy efficiency policies that could sustain economic development in the long run.

This paper investigates the long-run relationship between health care expenditures (HCE) and income using Canadian provincial data spanning a period of 40years from 1981 to 2020. We study the non-stationary and cointegration properties of HCE and income and estimate the long-run income elasticities of HCE. Using heterogeneous panel models that incorporate cross-section dependence via unobserved common correlated factors to capture global shocks, we estimate long-run income elasticities that lie in the 0.11-0.16 range. Our results indicate that health care is a necessity good for Canada. These elasticity estimates are much smaller than those estimated in other studies for Canada. We find that HCE and income in Canada are cointegrated and that short-run changes in federal transfers significantly and positively affect HCE.

Agriculture is supposed to have a pivotal role in assisting poverty alleviation in Indonesia. Hence, this paper empirically examines the causal link between paddy productivity and poverty rates in Sumatra, retrieving balanced panel data from ten provinces for the period 2010-2022. Dumitrescu-Hurlin (DH) causality and Pooled Mean Group (PMG) methods are applied in order to reveal the causal direction and the elasticity under heterogeneous panel models. This paper integrates slope homogeneity, panel unit root, and panel cointegration tests. The results reveal that poverty rates and paddy productivity, are integrated in mixed order, and , and they are cointegrated. The DH causality test denotes a unidirectional causality from paddy productivity toward poverty rates which implies the absence of a feedback effect. Following the PMG model, there is a positive impact of paddy productivity on poverty rates in the short run (∆β= 0.29); however, this linkage switches to become negative in the long run (β= -0.48). A 1% improvement in paddy productivity will be followed by a 0.48% reduction in poverty rates. Thus, augmenting paddy productivity has a favorable role in declining poverty rates. The estimated parameters of long-run PMG are robust, i.e., consistent with alternative methods of cointegrated regressions.

An attempt is made in this research to examine the relationship between income and health by testing the Absolute Income-Health Hypothesis (AIH). The study primarily focuses on 34 Sub-Saharan Africa (SSA) countries for the period of 2001-2016. The data for the study were mainly sourced from World Development Indicators (WDI) and the World Health Organization (WHO) Global Health Observatory Data Repository. Using heterogeneous slopes modelling set-up that incorporates series of non-stationarity, cross-section dependence, and group-specific trends, we failed to find evidence in support of the AIH. Our empirical outcome cast doubts on the robustness of previous studies that ignored such modelling attributes, while we deduced that methodology matters in analysing income-health nexus and testing the validity of the AIH for cross-section of countries. By contrast, we find income to be an insignificant determinant of health in SSA compared to health spending and improved sanitation.

This paper revisits the direction of causality between institutions and economic growth for a sample of 119 countries over the period 1999-2018, divided into four groups according to income level: high income, upper middle income, lower middle income and low income. The study uses two institutional datasets, the International Country Risk Guide (ICRG) for the main estimation and the World Governance Indicators (WGI) for check the robustness of the results. Using the non-causality Granger test in a heterogeneous panel model with fixed coefficients, developed by Dumitrescu and Hurlin (2012), the empirical results show a unidirectional relationship for all panels except for lower middle-income countries, where causality is bidirectional. The findings also suggest that causality patterns are heterogeneous and depend on the level of development of the countries. Based on these results, we propose some interesting recommendations. The types of reforms to prioritize must be determined according to the direction of causality between institutions and economic growth. Moreover, heterogeneous causality implies the implementation of different policies adapted to the level of development of each panel, rather than considering a common policy.

This paper analyzes the key macroeconomic consequences that are directly determined by the pandemic and geopolitical crisis in the form of growing inflationary pressures, reflecting a high level of uncertainty during decision-making and planning at the individual, business and macroeconomic level. The subject of the econometric analysis aims to see how the movement of oil prices affects the consumer price index (CPI) on a sample of 15 developed European economies in the period from 2020q1-2023q4. Using heterogeneous panel models, specifically Mean Group (MG), and Pooled Mean Group (PMG) methods positive and heterogeneous impact of the increase in the price of crude oil on CPI is detected. Research indicates that the long-run relationship and speed of adjustment of individual economies to the long-run equilibrium relationship is heterogeneous during the analyzed period, indicating that the effect of macroeconomic uncertainties represented in crude oil price increase had different magnitude of influence in developed European economies. Individual adjustments were the most intensive in Greece, France, and Portugal, meaning that those economies were more exposed to higher inflationary pressures, while a slower intensity of adjustment and lower inflationary pressures were present in Austria, Belgium, Finland, and Luxembourg. Detected vulnerability of developed European economies in the circumstances of global uncertainties is expected due to absence of mechanisms to achieve countercyclical effects on the growth of inflation.

SummaryThis paper proposes a quantile regression estimator for a heterogeneous panel model with lagged dependent variables and interactive effects. The paper adopts the Common Correlated Effects (CCE) approach proposed in the literature and demonstrates that the extension to the estimation of dynamic quantile regression models is feasible under similar conditions to the ones used in the literature. The new quantile regression estimator is shown to be consistent and its asymptotic distribution is derived. Monte Carlo studies are carried out to study the small sample behavior of the proposed approach. The evidence shows that the estimator can significantly improve on the performance of existing estimators as long as the time series dimension of the panel is large. We present an application to the evaluation of Time‐of‐Use pricing using a large randomized control trial.

This paper proposes a quantile regression estimator for a heterogeneous panel model with lagged dependent variables and interactive effects. The paper adopts the Common Correlated Effects (CCE) approach proposed by Pesaran (2006) and Chudik and Pesaran (2015) and demonstrates that the extension to the estimation of dynamic quantile regression models is feasible under similar conditions to the ones used in the literature. We establish consistency and derive the asymptotic distribution of the new quantile regression estimator. Monte Carlo studies are carried out to study the small sample behavior of the proposed approach. The evidence shows that the estimator can significantly improve on the performance of existing estimators as long as the time series dimension of the panel is large. We present an application to the evaluation of Time-of-Use pricing using a large randomized control trial.