Foreign inflows and economic growth: An emiprical study of the SAARC region

The study examines and highlights the impact of selected foreign inflows (aid, trade, FDI, debt and remittances) on the economic growth of the South Asian Association for Regional Cooperation (SAARC) countries. The existent literature lacks a comprehensive analysis of the SAARC countries as countries like Afghanistan, Bhutan and the Maldives have largely been ignored due to the shorter time periods of available data. The study is empirical in nature and utilizes panel data techniques on macroeconomic data for the period 2008–2015. Foreign aid and foreign direct investment are found to impact economic growth positively. Foreign debt and trade flows are found to adversely affect economic growth. No relationship is established between the flow of remittances and the economic growth of these countries. The obtained results are robust to different proxy variables and the addition of macroeconomic variables. For the first time, the study provides policy implications based on the data of all SAARC countries. The study recommends focusing on increasing the inflows of resources in the form of aid and foreign direct investment (FDI) from the developed world to achieve higher economic growth.     

Cross-hedging foreign currency risk: Empirical evidence from an error correction model

In this article, the traditional price change hedge ratio estimation method is extended by applying the theory of cointegration in the case of cross-hedging of spot exchange risk of the Belgian franc (BF), the Italian lira (IL), and the Dutch guilder (NG) with U.S. Dollar Index futures contracts. Previous studies ignore the last period's equilibrium error and short-run deviations. The findings of this study indicate that the hedge ratio estimated by the error correction method is superior to that obtained from the traditional method, as evidenced by the likelihood ratio test and out-of-sample forecasts. Hedgers will be able to control the risk of their portfolios more effectively at a lower cost.     

A fuzzy goal programming approach to analyse sustainable development goals of India

Energy policy, environmental planning and economic development play a key role in sustainable development. Sustainable development requires suitable and strategic policies satisfying multiple and conflicting objectives. Fuzzy goal programming (FGP) is a well-known approach in multi-criteria decision-making for its practical application. In this article, a FGP approach is proposed to analyse environmental, energy and sustainability goals of India by the year 2030 with reference to the key economic sectors of India. The presented model analyses the improvement opportunities, requirement of efforts and implementation of the sustainable development plans. Numerical illustration is also provided for validation and application of the proposed model.     

Lessons for Development from the Japanese Experience

Economic class, defined in relation to its actual control over the economy’s productive assets, is the most useful lens for examining power distribution in the post-Soviet transition and clarifying the neoliberal nature of its social provisioning processes. Using Ukraine as an illustration, I argue that only ownership empowerment of economically powerless classes can democratize the oligarchic transition economies. As an exit strategy from oligarchic capitalism, I recommend that state capitalism be implemented, as a transitory condition only, to divest oligarchs of unlawfully acquired economic power. Then, progressive restructuring of oligarchic companies must be conducted through broadening property ownership to include shared ownership and worker participation in economic decisions. I conclude that only by nurturing the democratic fundamentals of the economy and promoting a social democratic welfare state could a government in post-Euromaidan Ukraine initiate its own social control and create a genuine political and economic democracy.     

Choice and the Internet: From Clickstream to Research Stream

The authors discuss research progress and future opportunities for modeling consumer choice on the Internet using clickstream data (the electronic records of Internet usage recorded by company web servers and syndicated data services). The authors compare the nature of Internet choice (as captured by clickstream data) with supermarket choice (as captured by UPC scanner panel data), highlighting the differences relevant to choice modelers. Though the application of choice models to clickstream data is relatively new, the authors review existing early work and provide a two-by-two categorization of the applications studied to date (delineating search versus purchase on the one hand and within-site versus across-site choices on the other). The paper offers directions for further research in these areas and discusses additional opportunities afforded by clickstream information, including personalization, data mining, automation, and customer valuation. Notwithstanding the numerous challenges associated with clickstream data research, the authors conclude that the detailed nature of the information tracked about Internet usage and e-commerce transactions presents an enormous opportunity for empirical modelers to enhance the understanding and prediction of choice behavior.     

Leveraging Information Across Categories

Companies are collecting increasing amounts of information about their customers. This effort is based on the assumption that more information is better and that this information can be leveraged to predict customers' behavior in a variety of situations and product categories. For example, information about a customer's purchase behavior in one category can be helpful in predicting his potential behavior in a related category, which in turn could help a firm in its cross-selling efforts. In this paper, we present a model to better understand and predict a consumer's purchases and preferences when we may have limited or no information about him in one or more product categories. Conceptually this involves leveraging information from purchases of other consumers in multiple categories as well as partial information (e.g., purchase in one of the categories) of the target consumer. Our approach builds on the pioneering work of (Rossi et al. (1996)) who demonstrate the value of purchase information in the context of a single product category. We present results from an extensive simulation as well as an application on scanner panel data. Our simulation shows many interesting and somewhat surprising results. Specifically, we find that compared to a single-category analysis, a cross-category analysis does not lead to any significant improvement in data likelihood in most cases. Therefore, the single-category analysis of (Rossi et al. (1996)) is even more powerful than previously thought. However, we also find that a cross-category analysis does improve parameter recovery in many situations as compared to a single-category analysis. It is in these conditions that retailers can use cross-category information to better implement micro marketing programs. We demonstrate the transfer of information across categories in an application of two grocery products—Breakfast Foods and Table Syrup. In spite of a reasonable correlation (0.21) in the price parameter across these two categories, our simulation guidelines predict very little benefit of cross-category analysis over single-category analysis. Our empirical results confirm this prediction.     

HEDGING WITH INTERNATIONAL STOCK INDEX FUTURES: AN INTERTEMPORAL ERROR CORRECTION MODEL

In this paper we extend the traditional price change hedge ratio estimation method by applying the theory of cointegration to hedging with stock index futures contracts for France (CAC 40), the United Kingdom (FTSE 100), Germany (DAX), and Japan (NIKKEI). Previous studies ignore the last period's equilibrium error and short-run deviations. The findings of this study indicate that the hedge ratios obtained from the error correction method are superior to those obtained from the traditional method as evidenced by the likelihood ratio test and out-of-sample forecasts. Using the procedures developed in this paper, hedgers can control the risk of their portfolios more effectively at a lower cost.