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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 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 paper studies the dynamics and elasticities of house prices in Spain and Portugal (Iberia) at the Metropolitan Statistical Area (MSA) level, addressing panel regression problems such as heterogeneity and cross-sectional dependence between MSA.

The authors develop a two steps study. First, five distinct estimation methodologies are applied to estimate the long-term house price equilibrium of the Iberian MSA house market: Mean Group (MG), Fully Modified Ordinary Least Square (FMOLS) MG (FMOLS-MG), FMOLS Augmented MG (FMOLS-AMG), Common Correlated Effects MG (CCEMG) and Dynamic CCEMG (DCCEMG). FMOLS-AMG is found to be the best estimator for the long-term model. Second, an additional five distinct estimation methodologies are applied to estimate the short-term house price dynamics using the long-term FMOLS-AMG estimated price in the error-correction term of the short-term dynamic house price model: OLS Fixed Effects (FE), OLS Random Effects (RE), MG, CCEMG and DCCEMG. DCCEMG is found to be the best estimator for the short-term model.

The results show that in the long run Iberian house prices are inelastic to aggregate income (0.227). This is a much lower elasticity than what was previously found in US MSA house price studies, suggesting that there are other factors explaining Iberian house prices. According to our study, coastal MSA presents an inelastic housing supply and a price to income elasticity close to one, whereas inland MSA are shown to have an elastic supply and a non-significant price to income elasticity. Spatial differences are important and cross-section dependence is prevalent, affecting estimates in conventional methodologies that do not account for these limitations, such as OLS-FE and OLS-RE. Momentum and mean reversion are the main determinants of short-term dynamics.

Recent econometric advances that account for slope heterogeneity and cross-section dependence produce more accurate estimates than conventional panel estimation methodologies. The results suggest that house markets should be analyzed at the metropolitan level, not at the national level and that there are significant differences between short-term and long-term house price determinants.

To the best of the authors' knowledge, this is the first study applying recent econometric advances to the Iberian MSA house market.

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.

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 study aims to examine the relationship between exchange rate risk and export at commodity level for the Indian case.

The monthly panel data used for analysis are at a disaggregated level, which cover around 100 products, encompassing all merchandize sectors for the period spanning from 2012:12 to 2017:11. To measure the exchange rate volatility, the authors use real as well as nominal exchange rate concepts and predict the volatility of exchange rate using the autoregressive conditional heteroscedastic-based model. They use pooled mean group, mean group and common correlated effects mean group estimator that is suitable for the objectives and data frequency.

The empirical analysis indicates both short- and long-term negative effects of exchange rate variations on exporting. Specifically, in the long run, real exchange rate as well as nominal exchange rate volatility has significant effects on export performance, yet, the effects of uncertainty of nominal exchange rate is much severe and intense. In the short run, it is the nominal exchange rate uncertainty that hurts exports from India. Nevertheless, the short-run effect is much lesser than the long-run, supporting the argument that the short-term exchange rate risk can be hedged, at least partially, through financial instruments; however, uncertainty of the long-term horizon cannot be hedged easily and cost-effectively.

Reducing uncertainty and attaining stability in exchange rate and price level should be an important policy objective in developing countries such as India to achieve higher export growth, both in the short and long run.

Unlike previous studies, this paper tests the relationship using micro-level data and uses advanced econometric techniques that are likely to provide more precise information regarding the association between exchange rate volatility and trade flows.

The widening income gap between rich and poor has gained worldwide recognition in recent decades. This income gap between rich and poor is defined as the extent of income unevenly distributed in a host country. This study provides an empirical view of the association between information and communication technology and the widening of the income gap.

The study used panel data from 2005 to 2019. To detect unit root issues, Levin and Lin (LL) and Im, Pesaran and Shin (IPS) tests were first employed. The pooled mean group and mean group estimators were employed to investigate the short and long -term impact of information and communication technology and other control factors on reducing the gap between rich and poor in South Asia.

The results showed that the Pooled mean group's findings are more efficient and consistent as compared to mean group estimators. The results of the paper showed that the greater penetration of information and communication technologies in the economy negatively and significantly affects income inequality. Moreover, the information and communication technology, foreign remittances and foreign direct investment (FDI) significantly reduce the gap between rich and poor in the long run.

At last, the findings of the study serve as an excellent roadmap for policymakers seeking to address the issue of growing income inequality in the South Asian regions and worldwide.

Based on the findings of this study, South Asia can reduce the gap between rich and poor by investing more in the information and communication technology sector.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-10-2021-0638

The purpose of this paper is to fill a gap in the foreign exchange rate exposure management literature as the existing literature has focused only on developed economics, and also the current literature on foreign exchange rate exposure of cedant insurance companies is very limited. As Egyptian insurance companies deal directly with foreign exchange rates, they face exposure to exchange rates through their international reinsurance operations.

Martin and Mauer (2003, 2005) three-stage model is used to estimate foreign exchange rate transaction exposure for the sample of 23 Egyptian insurance companies over the period 2002-2009. However, the author has two innovations to this method. The author's first innovation is that instead of looking at the unanticipated operating income for each cedant company (as in both previous papers), this paper looks at the unanticipated operating income on an aggregate level. The author's second innovation is that instead of the model used in previous papers the author uses a model from the actuarial field that was proposed by Blum et al. (2001) for modelling foreign exchange rates with their relevant constituents (inflation and interest rate).

The central finding of the study is that the foreign exchange rate exposure across the Egyptian insurance industry is not significant (at the 10 per cent level) and investigates this result.

This study has made considerable contributions to the existing academic literature, but the findings also illustrate the limitations of the research undertaken. These limitations, however, provide important directions for future research. This thesis focused exclusively on the transaction exposure that Egyptian insurance companies experience to fluctuations in the US dollar exchange rate in relation to their international reinsurance operations. As a result, investigating both translation and economic exposure was beyond the scope and purpose of this study.

The findings of this research provide meaningful implications for industry practitioners. As Egyptian insurance companies are not immune from exchange rate risks, efforts must be made by each insurer to approximate and quantify their individual foreign exchange rate transaction exposure. Additionally, as Egyptian insurance companies increasingly operate worldwide (through the international reinsurance industry), this research and its results are significant for practitioners not only in Egypt, but also further afield. Finally, it is believed that this research will highlight greater implications for international financial players active in Egyptian financial and non-financial sectors, including banks not exposed singularly to US dollars, but to multiple currencies. One recent Egyptian example is Egypt Air, which lost an estimated US$600 million in 2013 due to foreign exchange rate fluctuations.

Since Egyptian insurance operates worldwide, the results of this paper are of significant not only for Egyptian insurance managers but also to practitioners beyond Egypt.

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.