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In antitrust class-action litigation, courts are increasingly unlikely to accept the presumption that all class members were harmed by price-fixing among a group of firms or by exclusionary behavior by a single firm. Econometric methods typically applied in antitrust and other settings estimate the average effect of the challenged conduct, but do not inform impact for individual class members. We present classwide econometric methods and statistical tests for detecting the existence (or lack thereof) of common impact and determining what proportion (if any) of the proposed class suffered injury in many class actions. We conclude that econometric tools can meaningfully inform the legal process, even when courts demand proof of common impact.

Purpose – Information collected from police crash reports has long been the primary source of data for the analysis of factors that determine the likelihood of a crash (crash frequency) and its resulting severity (measured in terms of the extent of injuries to vehicle occupants). Proper cross-sectional analyses techniques, covered in this chapter, are important for guiding safety policy and countermeasures.

Methodology – This chapter provides an overview of some of the more commonly used cross-sectional statistical and econometric methods, and discusses the nuances and their limitations with regard to how they are applied to typical crash-report data.

Findings – The wide variety of analytic methods available to safety researchers makes the selection of appropriate methods critical. This chapter provides important guidance for safety researchers in their choice of methodological approach.

Implications – Understanding the importance of proper model specification, unobserved heterogeneity, endogeneity and other factors covered in this chapter is extremely important in analysing safety data and must be given full consideration before any results are finalised.

The main task when valuing land is to identify the variables affecting its value. This is critical when a large number of variables is involved. Furthermore, collinearity and other econometric disturbances frequently occur in this type of research. Against this background, and in an effort to surmount these difficulties, this paper proposes and then tests some statistical techniques based on multivariate analysis. Multiple correspondence analysis helps to ensure rigour, simplicity and accuracy in the process of identifying the variables involved in the valuation of farmland. Finally, once the relevant variables have been identified, a model for the valuation of farmland plots is then specified.

The purpose of this paper is to examine the real estate sales in Turkey on a district basis to reveal the current state of real estate sales and any meaningful changes in the last period. The real estate market is important and is an indicator of the country’s general economic health, as real estate is seen as an investment.

As a powerful method of spatial analysis and evaluation, geographic information systems have been used to examine real estate data in both spatial and temporal ways. In this study, 14 years of sales data covering the years 2004 to 2017 obtained from government agencies on a district basis were evaluated using spatiotemporal methods. Several maps were produced using Getis-Ord Gi* and local Moran’s I indices, which showed the spatiotemporal change of sales and sales rates.

When looking at the maps, provinces such as Istanbul, Ankara, Izmir, Antalya and their surrounding districts have buoyant real estate markets compared to the other side of the country. Real estate sales are more stagnant in the eastern and northern parts of the country. In addition, the authors found that the growth rate of annual average real estate sales was approximately seven times higher than the annual average population growth.

This spatiotemporal study, which presents 14 years of performance data of the real estate market and, by extension, the economic situation, also highlights the regions that stand out for investment planning throughout the country. The results of spatiotemporal analysis also present a new way of real estate market visualization using maps with well-designed categorizations.

This chapter reviews the recent developments in the estimation of panel data models in which some variables are only partially observed. Specifically we consider the issues of censoring, sample selection, attrition, missing data, and measurement error in panel data models. Although most of these issues, except attrition, occur in cross-sectional or time series data as well, panel data models introduce some particular challenges due to the presence of persistent individual effects. The past two decades have seen many stimulating developments in the econometric and statistical methods dealing with these problems. This review focuses on two strands of research of the rapidly growing literature on semiparametric and nonparametric methods for panel data models: (i) estimation of panel models with discrete or limited dependent variables and (ii) estimation of panel models based on nonparametric deconvolution methods.

At present, countries are concerned about inflation and the impact of inflation on each country’s economic growth. This inflation has been said by economists that inflation is a phenomenon of currency and currency, which has caused inflation in some countries by their monetary policy. According to the economic theory of Karl Marx, Irving Fisher, Friedman, inflation is caused by a continuous increase in the money supply.

The economic theories of Fisher, Friedman and an econometric model are applied to analyse the relationship between money supply and inflation. Besides, Vietnam’s and China’s research data are also collected in the period of 2012-2016.

It is found out that the continuous increase in the money supply causes inflation in the long-term, but the continuous increase in the money supply growth does not cause inflation in a short time, this was analyzed based on the theory of monetary quantity. Moreover, Chia’s and Vietnam’s correlations of the money supply growth and inflation are 99.1 per cent. These correlations are very close.

Research results show that money supply and inflation are closely related, and the money supply directly affects economic growth. Therefore, the government should have the relevant monetary policy to grow the economy and proposals to make monetary policy, control inflation levels and stimulate economic growth.

The purpose of this study is to develop a precise Islamic securities index forecasting model using artificial neural networks (ANNs).

The data of daily closing prices of KMI-30 index span from Aug-2009 to Oct-2019. The data of 2,520 observations are divided into training and test data sets by using the 80:20 ratio, which corresponds to 2016 and 504 observations, respectively. In total, 25 features are used; however, in model selection step, based on maximum accuracy, top ten indicators are selected from several iterations of predictive models.

The results of feature selection show that top five influencing indicators on Islamic index include Bollinger Bands, Williams Accumulation Distribution, Aroon Oscillator, Directional Movement and Forecast Oscillator while Mesa Sine Wave is the least important. The findings show that the model captures much of the trend and some of the undulations of the original series.

The findings of this study may have important implications for investment and risk management by using index-based products.

Numerous studies proved that traditional econometric techniques face significant challenges in out-of-sample predictability due to model uncertainty and parameter instability. Recent studies show an upsurge of interest in machine learning algorithms to improve the prediction accuracy.

International Financial Reporting Standards (IFRS) on microfinance services are important for several reasons, such as global financial integration, investor confidence, risk assessment, access to capital markets, creditworthiness, alignment with best practices, international partnerships, legal compliance, capacity building, and risk mitigation. The data is based on reports from all commercial banks in Uzbekistan. The authors developed the econometric equation using the autoregressive distributed lag (ARDL) model. This research hypothesizes that the bank's net profit is positively related to the bank's deposit practices. High net profit in commercial banks indicates financial strength and stability, which can increase customer trust in the bank. Depositors are more likely to entrust their funds to banks with strong financial performance because this increases their deposits' security. Consequently, higher net profit is expected to be associated with increased deposit practices. The factors are econometrically analyzed based on the conceptual model created by the author. The results of this study are of practical importance for developing remote banking services in the banking system of Uzbekistan. In conclusion, the econometric model provides insight into the relationship between various independent variables (loan operations, microfinance services, and net profit) and the dependent variable (deposit operations) for commercial banks.

Corporate governance is the system of rules, practices, and processes used to direct and control a company. It is important to ensure that companies are managed fairly and transparently, protecting all stakeholders' interests. Joint-stock companies (JSCs) are a type of business organization in which ownership is divided into shares. Shareholders are the company's owners; they have the right to vote on important company matters (e.g., electing the board of directors and approving major financial decisions). International standards for corporate governance have been developed by a number of organizations, including the World Bank, the Organization for Economic Cooperation and Development (OECD), and the International Finance Corporation (IFC). These standards provide guidance on how to establish and maintain effective corporate governance systems.

This study analyzes the role of migrant networks in the migration flows in relation to the educational level of the migrants and economic growth of the states of origin and destination in Brazil.

Fixed effects estimator applied to microdata.

The results show migrant networks have a significant and positive impact on migration flows of the different educational levels. The economic growth in the destination state accentuates this effect, while the economic growth in the origin state has distinct impacts according to the educational level of the new migrant.

The authors investigate the importance of migrant networks in the internal immigration within a developing country with large internal movement of people. In Brazil, the socio-economic condition of the population varies considerably in relation to its geography, which explains the country’s large internal migration flows.