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This chapter revisits the Hausman (1978) test for panel data. It emphasizes that it is a general specification test and that rejection of the null signals misspecification and is not an endorsement of the fixed effects estimator as is done in practice. Non-rejection of the null provides support for the random effects estimator which is efficient under the null. The chapter offers practical tips on what to do in case the null is rejected including checking for endogeneity of the regressors, misspecified dynamics, and applying a nonparametric Hausman test, see Amini, Delgado, Henderson, and Parmeter (2012, chapter 16). Alternatively, for the fixed effects die hard, the chapter suggests testing the fixed effects restrictions before adopting this estimator. The chapter also recommends a pretest estimator that is based on an additional Hausman test based on the difference between the Hausman and Taylor estimator and the fixed effects estimator.

The purpose of this study is to verify all factors that promote renewable energy (RE) consumption. Past studies have shown that financial development (FD) and economic growth (EG) are the major drivers toward RE development, while oil prices had mixed outcomes in different regions by different studies.

Global warming effects have been the major reason of the transition by nations from fossil fuel use to RE sources that are considered as friendly to the environment. This research uses the fixed effects and random effects techniques, to ascertain the factors which impact RE development. The generalized linear model is also used to check the robustness of the Fixed Effects and Random Effects models’ results, while the Kao, Pedroni and Westerlund tests are used to check cointegration in the specified model.

The major findings of this study show the importance of EG and FD in promoting RE development. Oil prices, inflation rate and public sector credit present a negative effect on RE development, while foreign direct investment does not significantly impact RE development.

This research recommends the use of FD in promoting RE sources, as well as the stabilization of oil prices and consumer prices.

This research is important because it specifies the three proxies of FD, together with foreign direct investment inflation rate, EG and oil prices, in modeling RE. By investigating the impact of oil prices on RE in the emerging seven economies, this research becomes one of the few studies done in this region, as per the authors’ knowhow.

The purpose of this paper is to investigate the influence of macroeconomic cyclical indicators and country governance on bank non-performing loans in African countries.

Data was collected from the 53 African countries covering 2005–2021. The paper develops an empirical model to examine the impact of country governance in reducing macroeconomic cycle-induced adverse effects on bank credit risk. This research estimates Random Effects models and the General Method of Moment to examine the link between microeconomic and governance factors on bank non-performing loans. Stata version 15.1 was used to conduct panel regression analysis.

The findings of the study revealed that the generalized method of moments findings contributes valuable insights into the persistence of NPLs over time and the specific effects of variables on NPL levels. The study findings highlight that the debt-to-GDP ratio, unemployment, regulatory quality, government effectiveness and inflation have significant relationships with NPLs, shedding light on their specific contributions to credit risk dynamics.

The focus on a specific set of determinants for NPLs, which may not capture all the factors that influence NPL levels. Thus, the study did not consider the impact of macroeconomic shocks, such as natural disasters or global economic crises, which can have a significant impact on NPLs.

Policymakers should prioritize maintaining sustainable debt levels, promoting employment growth and controlling inflation rates to mitigate credit risk and reduce nonperforming loans. Also, enhancing regulatory quality and government effectiveness is crucial in ensuring financial stability and minimizing non-performing loans in Africa.

This paper provides a new possible solution to minimise bank non-performing loans risk by examining interactions of country governance regarding the macroeconomic cycle behaviour.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2022-0729

Today, the sustainability and outreach of microfinance institutions (MFIs) are crucial to the success of microfinance and the sector’s potential to make a lasting impact. The ability of MFIs to operate financially well without sacrificing their social goals has come under scrutiny. This study aims to identify the kind of relationships between the two objectives of MFIs in Ethiopia.

This study investigated the association between the outreach and financial sustainability of Ethiopian MFIs from the years 2012 to 2021 using a balanced set of panel data. The study used secondary data and employed a descriptive research design and a quantitative research approach. To this end, random and fixed effects estimation models, as well as three-stage least squares, with the model of seemingly unrelated regression (SUR) are used.

According to the study, outreach performance enables MFIs to achieve sustainability/financial performance. On the other side, MFI that are financially sound improve social performance. There was therefore no trade-off between the two objectives.

As Ethiopia’s microfinance sector shifts away from government and non-government backing and toward commercialization, such research is crucial. This aspect of the Ethiopian microfinance industry has gotten little consideration in research. The SUR model was used in the study together with random and fixed effect estimators, and the most reliable estimation result was chosen based on the necessary tests.

Entrepreneurial activity is a phenomenon that increases the economic growth of countries and improves their social welfare. The economic development levels of countries have significant effects on these entrepreneurial activities. This research examines which institutional and macroeconomic variables explain early-stage entrepreneurship activities in developed and developing economies.

The authors conducted panel data analysis on the data from the Global Entrepreneurship Monitor (GEM) and International Monetary Fund (IMF) surveys covering the years 2009–2018.

First, the authors' results reveal that cognitive, normative and regulatory institutions and macroeconomic factors affect early-stage entrepreneurial activity in developed and developing countries differently. Second, the authors' findings indicate that cognitive, normative and regulatory institutions affect early-stage entrepreneurship more positively in developed than developing countries. Finally, the authors' results report that macroeconomic factors are more effective in early-stage entrepreneurial activity in developing countries than in developed countries.

This study provides a better understanding of the components that help explain the differences in entrepreneurship between developed and developing countries regarding institutions and macroeconomic factors. In this way, it contributes to developing entrepreneurship literature with the theoretical achievements of combining institutional theory and macroeconomic indicators with entrepreneurship literature.

The purpose of this paper is to measure technical efficiency and examine its determinants while disentangling unobserved time-invariant heterogeneity from actual inefficiency using comprehensive household-level panel data.

This paper estimates technical efficiency based on the true random-effects stochastic production frontier estimator with a Mundlak adjustment. By utilising comprehensive panel data with 4,694 observations from 39 districts of four major maize-producing regions in Ethiopia, the author measures technical efficiency and examine its determinants while disentangling unobserved time-invariant heterogeneity from technical inefficiency. By using competing stochastic production frontier estimators, the author provides insights into the influence of farm heterogeneity on measuring farm efficiency and the subsequent impact on the ranking of farmers based on their efficiency scores.

The study results indicate that ignoring unobservable farmer heterogeneity leads to a downwards bias of technical efficiency estimates with a consequent effect on the ranking of farmers based on their efficiency scores. The mean technical efficiency score implied that about a 34% increase in maize productivity can be achieved with the current input use and technology in Ethiopia. The key determinants of the technical inefficiency of maize farmers are the age, gender and formal education level of the household head, household size, income, livestock ownership, and participation in off-farm activities.

While the findings of this study are critical for informing policy on improving agricultural production and productivity, a few important things are worth considering in terms of the generalisability of the findings. First, the study relied on secondary data, so only a snapshot of environmental factors was accounted for in the empirical estimations. Second, there could be other sources of unmeasured potential sources of heterogeneity caused by persistent technical inefficiency and endogeneity of inputs. Third, the study is limited to one country. Therefore, future research should extend the analysis to ensure the generalisability of the empirical findings regarding the extent to which unmeasured potential sources of heterogeneity caused by persistent technical inefficiency, endogeneity of inputs and other unobservable country-specific features – such as geographical differences.

This paper contributes to the literature on agricultural productivity and efficiency by providing new evidence on the influence of unobservable heterogeneity in a farm efficiency analysis. While agricultural production is characterised by heterogeneous production conditions, the influence of unobservable farm heterogeneity has generally been ignored in technical efficiency estimations, particularly in the context of smallholder farming. The value of this paper comes from disentailing producer-specific random heterogeneity from the actual inefficiency.

The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

The purpose of this study is to investigate the role of foreign aid in the Western Balkans countries’ economic growth between 2009 and 2021.

This paper uses a panel data approach to examine the effects of foreign aid on economic growth in the region and incorporates a random-effects model to accommodate the unique cross-country variations and time-specific factors, as well as a pooled OLS and fixed-effects model for a comprehensive, comparative analysis.

The in-depth regression analysis shows that foreign aid has not had a significant impact on the economic growth of the region. Further evidence suggests that trade openness exhibited a significant positive correlation with economic growth, while gross capital formation, although positively associated, did not significantly impact it, indicating the complexity of its role in the region’s economies.

The analysis presented in this study has significant practical implications, particularly for policymakers in the Western Balkans. Given the region’s ambitions for European Union membership and the challenges of high unemployment and inflation, understanding the role of foreign aid is crucial.

This research provides a unique contribution to the field of development economics by examining foreign aid effectiveness within the context of a region often overlooked in the literature. The analysis also offers fresh insights into the complex dynamics of foreign aid and its implications for policy and development strategies.

Stock markets are highly sensitive to foreign and domestic events. Stock exchange markets react promptly to news and are known as an indicator of good and bad trading conditions. Terrorist attacks leave adverse effects on the economy and cause stock price volatility and, consequently, stock return volatility. Therefore, this paper aims to analyze the spatial effects of terrorism on stock market returns in the Middle East from 2008 to 2019.

This paper uses analytical research design and estimates spatial model. Before estimating the spatial model, the spillover effects were confirmed for the spatial autoregressive model using Moran’s diagnostic test for spatial dependence, Geary’s C test and Akaike statistic.

The results of this study on spatial panel data and based on spatial autoregressive estimator indicated that terrorism and associated neighborhood effects had a negative impact on stock returns in Middle East countries. Also, the corruption index and oil price negatively affected stock market return in these countries, while the democracy index had a positive effect on stock market returns. According to the results, to achieve a high and stable stock market return, it is recommended that high-level consultation is pursued with leaders of involved countries to reduce the devastating effects of terrorist activities, increase political and economic stability, attract stockholders to stock markets and spend corresponding incomes developing the infrastructures in this sector in countries of this region.

Most of the studies have investigated the impact of terrorism on the stock market returns at the national or provincial level. However, the effect of terrorism on the stock market index in the tense region of the Middle East, which is the center of terrorist attacks in the world, has not been dealt with by considering the spatial econometric effects. Thus, to the best of the authors’ knowledge, this research is a first attempt to study the impact of terrorism on the stock market returns in Middle East countries using the spatial econometric approach.

This paper aims to estimate a statistical model of the country risk determination as represented by the country price earnings ratio (PE) to identify potentially mispriced countries. It uses the gross domestic product (GDP) growth rate and a dummy indicator for market-related events (i.e. financial crises), both approximating the business cycle. The model is used to compare a major Asian country’s (i.e. Japan) risk with Western countries’ risk.

The model used finance variables such as the systemic, non-diversifiable, risk and foreign direct investments to characterize any country risk. A random effects model with panel data estimated the effects of macroeconomic and financial variables on PE. The simultaneity problem was checked using two stage least squares and some lagged independent variables.

The results explained to investors the country risk contributing factors: PE was positively correlated with variables that may increase dividends and market risk premia similar to GDP growth rates and total risk and negatively correlated with variables that increase market risk, namely, nominal risk-free interest rates and financial crises. Japan’s PE seemed to exceed most of the Western countries considered here, implying lower risks, lower interest rates and higher growth in the major Asian country Japan.

This paper focuses on the effectiveness of country risk measures in predicting periods of intense instability, similar to financial crises. This study contributes a model to measure market risk premium, using PE (or inversely, the earnings yield) as a proxy variable. Investors can use this risk measure in picking less risky stocks to include in their portfolio, calling for liberalizing Asian countries’ financial markets to improve their stock market capitalization.