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This paper aims to discuss multilevel modeling for longitudinal data, clarifying the circumstances in which they can be used.

The authors estimate three-level models with repeated measures, offering conditions for their correct interpretation.

From the concepts and techniques presented, the authors can propose models, in which it is possible to identify the fixed and random effects on the dependent variable, understand the variance decomposition of multilevel random effects, test alternative covariance structures to account for heteroskedasticity and calculate and interpret the intraclass correlations of each analysis level.

Understanding how nested data structures and data with repeated measures work enables researchers and managers to define several types of constructs from which multilevel models can be used.

This paper aims to examine the effect of financial restatement on changing the auditor in the following years.

The study uses data of 105 companies (735 company-years) listed on the Tehran Stock Exchange collected during the period 2008-2014. Logistic regression is used to test the hypotheses.

The results of hypotheses present that restatement does not cause auditor changes and that as the severity of a restatement increases, the auditor change in the following year of restatement also does not increase. Restating companies having strong governance do not go for auditor changes as compared with other companies. In addition, in companies that are restating, non-big auditor changes are not more likely than a big auditor. Also, in companies restating simultaneous with a CEO turnover, there is no possibility of auditor change. Furthermore, multinomial logistic regression showed that the adjustments resulting from the correction of errors and changes in procedures and the amount of adjustments do not cause auditor change in the following year. So, the results have shown that the restatement is not an important factor in changing auditor the next year.

The current study analyses the impact of financial restatement on auditor changes in a deep manner in a developing country like Iran.

This paper aims to explore the impact of a recent earthquake activity on house prices and their spatial distribution in the Istanbul housing market.

The paper uses a multi-level approach within an event study framework to model changes in the pattern of house prices in Istanbul. The model allows the isolation of the effects of earthquake risk and explores the differential impact in different submarkets in two study periods – one before (2007) and one after (2012) recent earthquake activity in the Van region, which although in Eastern Turkey served to alter the perceptions of risk through the wider geographic region.

The analysis shows that there are variations in the size of price discounts in submarkets resulting from the differential influence of a recent earthquake activity on perceived risk of damage. The model results show that the spatial impacts of these changes are not transmitted evenly across the study area. Rather it is clear that submarkets at the cheaper end of the market have proportionately larger negative impacts on real estate values.

The robustness of the models would be enhanced by the addition of further spatial levels and larger data sets.

The methods introduced in this study can be used by real estate agents, valuers and insurance companies to help them more accurately assess the likely impacts of changes in the perceived risk of earthquake activity (or other environmental events such as flooding) on the formation of house prices in different market segments.

The application of these methods is intended to inform a fairer approach to setting insurance premiums and a better basis for determining policy interventions and public investment designed to mitigate potential earthquake risk.

The paper represents an attempt to develop a novel extension of the standard use of hedonic models in event studies to investigate the impact of natural disasters on real estate values. The value of the approach is that it is able to better capture the granularity of the spatial effects of environmental events than the standard approach.

Globalization occupies a central research activity and remains an increasingly controversial phenomenon in economics. This phenomenon corresponds to a subject that can be criticized through its impact on national economies. On the other hand, the world economy is evolving in a liberalized environment in which foreign direct investment plays a fundamental role in the economic development of each country. The advent of financial flows – FDI, remittances and official development assistance – can be a key factor in the development of the economy. The subject of this article is to analyses the effect of financial flows on economic growth in developing countries. Empirically, different approaches have been employed. As part of this work, an attempt was made to use a panel data approach. The results indicate ambiguous effects and confirm the results of previous work.

The authors seek to study the effect of foreign direct investment, remittances and official development assistance (ODA) and some control variables i.e. domestic credit, life expectancy, gross fixed capital formation (GFCF), inflation and three institutional factors on economic growth in developing countries by adopting the panel data methodology. Then, the authors will discuss empirical tests to assess the econometric relevance of the model specification before presenting the analysis of the results and their interpretations that lead to economic policy implications. As part of this work, the authors have rolled panel data for developing countries at an annual frequency during the period from 1990 to 2016. In a first stage of empirical analysis, the authors will carry out a technical study of the heterogeneity test of the individual fixed effects of the countries. This kind of analysis makes it possible to identify the problems retained in the specific choice of econometric modeling to be undertaken in the specificities of the panel data.

The empirical results validate the hypotheses put forward and indicate the evidence of an ambiguous effect of financial flows on economic growth. The empirical findings from this analysis suggest the use of economic-type solutions to resolve some of the shortcomings encountered in terms of unexpected effects. Governments in these countries should improve the business environment by establishing a framework that further encourages domestic and foreign investment.

In this article, the authors adopt the panel data to study the links between financial flows and economic growth. The authors considered four groups of countries by income.

The purpose of this study is to investigate the relationship between fiscal policy (tax revenues and government expenditure) and economic happiness. The panel data are used from 2012 to 2016 for 18 countries of the Middle East and North Africa (MENA) region.

The study adopted the Barro (1990) model of endogeneity growth to characterize the relationship between fiscal policy and economic happiness. The study estimated the model by using the pooled ordinary least squares method, the fixed effects method and the random-effects method. In addition, the study used the dynamic estimate of this relationship rather than the conventional static estimate through the generalized method of moments’ method. This leads to overcoming the endogeneity problem between the dependent variable and the independent variables.

The main findings indicated that there is a negative and statistically significant relationship between nondistortionary taxes and economic happiness. Also, there is no relationship between public expenditure and economic happiness, whether productive or nonproductive. The results confirmed a positive and significant relationship between other revenues and economic happiness. The current study recommended the diversification of other public revenue sources to increase its contribution to public expenditure financing and the restructuring of the tax system, particularly nondistortionary taxes. These taxes must be replaced by other revenues or by distortionary taxes to increase economic happiness.

The research represents a strong starting base that can help researchers to conduct more studies on economic happiness by using different measures and comparing their results to find out the determinants of happiness. The relationship between economic happiness and fiscal policy with its different aspects requires more studies, especially the relationship between taxes and economic happiness in our region. The study of the relationship between public expenditure and economic happiness according to economic activities can guide decision-makers to direct the expenditure toward economic activities that achieve the happiness of their citizens. Enriching this study requires the availability of fiscal data for the entire MENA region for longer periods, which allow us to divide the countries of the region into petroleum and nonpetroleum countries, but the scarcity of data is one of the limitations of the study.

The governments of MENA countries should diversify other public revenue sources to increase the financing public expenditure by the expense of tax revenues, especially nondistortionary taxes, which would increase the economic happiness of their citizens.

This study is one of the rare studies that investigate the relationship between fiscal policy and economic happiness at the global level. This study contributed to filling the gap of this issue in the MENA region and enriching global literature through the experience of the MENA region. Moreover, this study investigated all aspects of fiscal policy, in contrast to other studies that focused on one of its aspects. The weakness in these studies is because of the lack of correlation between the sources of revenues and the face of their spending.

The purpose of this paper is to examine empirically the efficiency types of Islamic and conventional banks. It seeks to show whether the efficiency level of conventional and Islamic banks significantly differs from each other. In addition, it investigates the influential factors on each type of efficiency.

The paper utilises the data envelopment analysis in its windows version to estimate the efficiency scores reflecting the time variance and compares between banking models. The paper uses pure technical efficiency (TE) and scale efficiency to achieve the objective of the study. In addition, the panel data technique is adopted to assess the determinants of the efficiency of the banks econometrically.

The findings of panel regression initially indicate that the pure TE is higher for conventional banks compared to Islamic banks. However, the Islamic banks are more scale efficient than their conventional counterpart. Macro and micro indicators have different impacts on the both types of efficiency. However, the unique factors that show consistent influence on the efficiency types were loans/finance, non-interest income/finance/liquidity and GDP. Furthermore, the determinants are shaped differently for Islamic and conventional banks when the banking model is controlled for.

This paper examines the efficiency types using a unique window analysis approach to examine the types of efficiency with a longitudinal set of data from 1996 to 2011.

The purpose of this paper is to examine bank performance using the different performance measures, namely, return on assets, return on equity and bank margins (MAR).

Unbalanced panel data were constructed to test the related hypotheses and provide evidence on the relationship between ownership types, banking models and performance indicators adopting the random effects techniques.

The findings of the paper substantiate that the banking models are significant performance indicators. However, the results are contingent on the GDP growth of the country. Moreover, the evidence indicates that the impact of ownership types is inconclusive in all measures of performance. However, the GDP is significant when it interacts with the types of ownership, particularly for foreign and government banks, although the evidence is mixed and unfavourable for government banks.

The results of the study provide insights for bankers and policymakers to enhancement Yemen’s banking sector.

This study is considered as the first attempt in examining the role of banking model and ownership type and their link to banking model.

Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.

Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.

The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.

One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.

This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.

This paper aims to empirically investigate the factors attracting foreign direct investment (FDI) inflows into emerging economies.

This study uses secondary data from the World Bank and the Global State of Democracy Indices of 16 West African countries (WACs) over the period from 1989 to 2018. Fixed- and random-effects econometric regression models are used to assess the nexus between 12 macroeconomic indicators (including political risk and cultural factors) and FDI inflows into WACs.

The critical drivers of FDI inflows into WACs are the richness of natural resources, market size or gross domestic product (GDP), imports and exports of goods and services, trade openness and the currency's strength as measured by the exchange rate. The result also reveals that French-speaking countries attract more FDI than other English-speaking countries. The previously cited determinants of FDI, such as infrastructural development, inflation, tax and political stability, are insignificant in determining FDI inflows into WACs.

This study uncovers the critical drivers explaining the FDI inflows into WACs, where FDI accounts for 39% of external finance. The study's contribution is that Francophone WACs attract more FDI than Anglophone WACs. The most important drivers of FDI are abundant natural resources, GDP, imports, exports, trade openness and exchange rate.

The paper aims at providing insights on the relationship between capital structure and performance of the firm by employing meta-analytical approach to obtain a synthesized result out of controversial studies as well as the sources for such inconsistency.

Using secondary data, the analysis is divided into two main parts with concerns to the overall strength of the relationship, the effect size and the potential paper-specific characteristics influencing the magnitude of impacts between leverage and firm performance (moderators of the relationship). Overall, a total number of 32 journals, reviews and school presses were selected besides online libraries and publishing platforms. There were 50 papers with 340 studies chosen from 2004 to 2019, of which data range from 1998 to 2017.

Using Hedges et al. (1985,1988), descriptive and quantitative analysis have been conducted to confirm that corporate performance is negatively related to capital decisions, which inclines toward trade-off model with agency costs and pecking order theory. The estimation induces rather small effect size that implies sufficiently large sample size to be effectively investigated. In terms of moderator analysis, random-effects meta-regression models of three different techniques are used to increase the robustness in research findings, showing statistically significant elements as publication status, factor of industry and proxy of firm performance.

This paper is one of the first papers presenting meta-analysis in capital structure and performance for two languages, Vietnamese and English, providing a consistent result with previous worldwide papers.