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The aim of this paper is to develop a new analysis method, named dynamic extreme bounds analysis (DEBA), and to determine decisive determinants of foreign direct investment (FDI) by using this new method.

In econometrics, the extreme bounds analysis (EBA) method is a convincing way of examining the strength of independent variables. However, the results obtained when using the EBA method contain little information, since each variable is only either strong or fragile, and some strong variables may be omitted because their significance could be undermined by just one unreasonable regression. Therefore, in order to overcome these limitations, this paper proposes DEBA, a new analysis method.

The authors employ the DEBA method to determine the factors which impact FDI in 86 countries. The authors note that in developing countries, the level of previous FDI, a high degree of openness, large market size and development of infrastructure help to attract FDI, whereas the development of domestic industry deters it. In developed countries, FDI is lured by the level of previous FDI stock, a high degree of openness, large market size, macroeconomic instability and availability of energy.

Although this study is expected to contribute a new methodological approach and define the strong determinants of FDI, the study is not without limitations, such as the unavailability of data. Further studies should improve the DEBA method by developing DEBA packages for use in popular statistical software, enhancing methods for other types of data and more accurately determining the estimation order of variables. In addition, further research should expand the study's FDI model, providing more potential variables for an in-depth overview of this model.

This study is to contribute a new methodological approach (DEBA method) for data analysis and defining of strong determinants of FDI. The study findings are useful for governments, policy-makers and economists in formulating more attractive FDI policies.

This paper examines variable selection among various factors related to motor vehicle fatality rates using a rich set of panel data. Four Bayesian methods are used. These include Extreme Bounds Analysis (EBA), Stochastic Search Variable Selection (SSVS), Bayesian Model Averaging (BMA), and Bayesian Additive Regression Trees (BART). The first three of these employ parameter estimation, the last, BART, involves no parameter estimation. Nonetheless, it also has implications for variable selection. The variables examined in the models include traditional motor vehicle and socioeconomic factors along with important policy-related variables. Policy recommendations are suggested with respect to cell phone use, modernization of the fleet, alcohol use, and diminishing suicidal behavior.

This paper is about the effect of human capital on foreign direct investment (FDI). The purpose of this paper is to find out if developing countries with high levels of human capital (educated people and well-trained labour force) are more successful in attracting FDI. The underlying hypothesis has been tested repeatedly without reaching a consensus view or providing an answer to the basic question. This is to be expected because FDI is determined by a large number of factors, making the results sensitive to the selected set of explanatory variables, which forms the basis of the Leamer (1983) critique of the use of multiple regression to derive inference. Furthermore, confirmation bias and publication bias entice researchers to be selective in choosing the set of results they report.

The technique of extreme bounds analysis, as originally suggested by Leamer (1983) and modified by Sala-i-Martin (1997), is used to determine the importance of human capital for the ability of developing countries to attract FDI. The authors use a cross-sectional sample covering 103 developing and transition countries.

The results show no contradiction between firms seeking human capital and cheap labour. No matter what proxy is used to represent human capital, it turns out that the most important factor for attracting FDI is the variable “employee compensation”, which is the wage bill, implying that multinational firms look for cheap and also skilled labour in the host country.

In this paper, the authors follow the procedure prescribed by Leamer (1983), and modified by Sala-i-Martin (1997), using extreme bounds analysis to distinguish between robust and fragile determinants of FDI, with particular emphasis on human capital. Instead of deriving inference from one regression equation by determining the statistical significance of the coefficient on the variable of interest, the extreme bounds or the distribution of estimated coefficients are used to distinguish between robust and fragile variables. This means that emphasis is shifted from significance, as implied by a single regression equation, to robustness, which is based on a large number of equations. The authors conduct tests on three proxies for human capital to find out if they are robust determinants of FDI and also judge the degree of robustness relative to other determinants.

The Blinder Oaxaca decomposition method for defining wage differentials (generally referred to as discrimination) from the wage equations of two groups has had a wide degree of application. However, the decomposition measures can very dramatically depending on the definition of the non-discriminatory wage chosen for comparison. This paper uses a form of extreme bounds analysis to define the limits on the measure of discrimination that can be obtained from these decompositions. A simple application is presented to demonstrate the use of the bootstrap to define the distributions of the discrimination measure.

The purpose of this paper is to examine whether there is significant evidence that hedge fund managers engage in deceptive manipulation of their reported performance results.

A model of hedge fund performance has been developed using standard regression analysis incorporating dependent lagged variables and an autoregressive process. In addition, the extreme bounds analysis technique has been used to examine the robustness and sensitivity of the explanatory variables. Finally, the conditional influence of the global stock market’s returns on hedge fund performance and the conditional return behavior of the Hedge Fund Index’s performance have been explored.

This paper begins by identifying a model of hedge fund performance using passive index funds that is well specified and robust. Next, the lag structure associated with hedge fund returns has been examined and it has been determined that it seems to take the hedge fund managers two months to integrate the global stock market’s returns into their reported performance; however, the lagged variables were reduced from the final model. The paper continues to explore the smoothing behavior by conditioning the dependent lagged variables on positive and negative returns and find that managers are conservative in their estimates of positive performance events, but, when experiencing a negative result, they seem to attempt to rapidly integrate that effect into the return series. The strength of their integration increases as the magnitude of the negative performance increases. Finally, the performance of returns for both the Hedge Fund Index and the passive indices were examined and no significant differences between the conditional returns were found.

The results of this analysis illustrate that hedge fund performance is not all that different from the performance of passive indices included in this paper, although it does offer investors access to a unique return distribution. From a management perspective, we are reminded that we need to be cautious about hastily arriving at conclusions about something that looks different or feels different from everything else, because, at times, our preconceived notions will cause us to avoid participating in something that may add value to our organizations. From an investment perspective, sometimes having something that looks and behaves differently from everything else, improves our investment experience.

This paper provides a well-specified and robust model of hedge fund performance and uses extreme bounds analysis to test the robustness of this model. This paper also investigates the smoothing behavior of hedge fund performance by segmenting the returns into two cohorts, and it finds that the smoothing behavior is only significant after the hedge funds produce positive performance results, the strength of the relationship between the global stock market and hedge fund performance is more economically significant if the market has generated a negative performance result in the previous period, and that as the previous period’s performance becomes increasingly negative, the strength of the relationship between the Hedge Fund Index and the global stock market increases.

This study aims to investigate whether a religious environment affects a firm capital structure.

The authors use data from Saudi Arabia with a highly Islamic religious environment. The authors use an extreme bounds analysis (EBA), which provides a reliable analysis of the determinants of capital structure and aids the process of selecting explanatory variables when there is model uncertainty.

The authors find that firms in such an Islamic environment are relatively less leveraged compared to firms in a non-Islamic environment. The authors also find that firms located in an Islamic environment have different determinants of capital structure than firms located in a non-Islamic environment. Specifically, the Islamic society creates decision makers who are more risk averse, thus leading to a preference for corporate financing using internal funds.

The results imply a potential challenge for growth-seeking firms located in religious Islamic societies.

This study is one of the first to examine the determinants of corporate capital structure in Saudi Arabia using EBA.

This research aims to identify the statistically significant characteristics of a hedonic model to explain the pricing of residential properties in two cities in Pakistan.

The research methodology applies extreme bounds analysis and the least absolute shrinkage and selection operator. Estimators of efficient pricing were measured via stochastic frontier analysis.

The study findings show that the market valuation of residential properties in Islamabad and Rawalpindi is systematically related to numerous factors, including property location, neighborhood characteristics, environmental characteristics, structural characteristics and administrative qualities of local housing societies. The authors also find statistical evidence that suggests that residential estate properties in the two cities are overpriced in the sense that the market transaction prices tend to be higher than the fair prices of the properties in the two cities.

In Pakistan, the term “real estate” is used rather synonymously with the word “investment.” The findings of this research will help investors to identify the measurable factors that affect the transaction prices of residential real estate. These identifications will facilitate the development of strategic plans toward achieving sustainable rates of return in residential real estate markets.

The residential real estate sector in Pakistan is constantly changing. There are myriad causes for these changes, including changes in social structure, cultural attitudes and technology. Customary methods for forecasting market prices for residential properties have been rendered unreliable because of the dynamics of the market. This study will contribute to the understanding of the changing dynamics of residential real estate pricing.

Although Pakistan's residential real estate market is growing very rapidly, there is little published research identifying the drivers of this growth. This study covers these aspects to fill the theoretical gap in a real estate context.

This study examines the effects of the accruals vs. cash flow bases on firm’s MB ratio as a proxy for shareholder value. The methodology utilizes the benefits of the ‘partial adjustment model’ where it addresses the extent to which the shareholder value adjusts to a target level. The final results indicate that (a) the accrual basis helps adjust the shareholder value to a target level more than the cash flow basis, (b) the shareholder value is associated with profitability‐related ratios and dividend‐related ratios, (c) in both bases, the shareholders value is positively associated with earnings per share and price‐to‐earnings ratio, (d) the significant effects of firm‐specific controls indicate that the shareholder value is affected by the accounting base in certain industries, certain size, and affected by the time as well. The results of the sensitivity analysis show that the accruals‐based estimates and cash flow estimates are robust and reliable.

The purpose of this paper is to examine the impact of outward foreign direct investment (FDI) on economic growth.

Two econometric approaches are used: cross‐country regressions for a sample of 50 countries and time‐series estimators for the USA.

Both approaches tell the same story: outward FDI is positively associated with growth. This finding is robust to several model specifications, potential outliers, and different estimation techniques. In addition, Granger‐causality tests for the USA indicate that causality is bidirectional, suggesting that increased outward FDI is both a cause and a consequence of increased domestic output.

Previous studies have primarily examined the firm‐ and industry‐level effects of outward FDI – for example, on domestic investment, employment, and productivity. This paper, in contrast, deals with the effects of aggregate outward FDI on the economy as a whole.

Purpose – We study the determinants of growing wage inequality in 16 OECD countries in the past two decades of the twentieth century. The main independent variables that we consider are those pertaining to labor market institutions, to international trade with less developed nations, and to deindustrialization.

Methodology – We specify a statistical model of pay differentials using first differences over five-year periods. The main estimation method used is weighted ordinary least squares. Where necessary, we use instrumental variables and two-stage least squares. We also undertake extensive robustness exercises, including a version of extreme bounds analysis and deleting each individual country from the analysis.

Findings – The determinants of wage inequality are different in the 1980s and in the 1990s. In the 1980s, growing wage dispersion is due to changes in the institutions of the labor market, including declining unionization and declines in the level at which wages are bargained collectively. In the 1990s, increases in pay inequality are due to increasing trade with less developed nations and weakening of social insurance programs.

Originality – This is the first study to report that the causes for pay inequality differed between the 1980s and the 1990s. It is also the first to document statistically that trade with the less developed nations systematically increases pay inequality in the developed world in the 1990s.