Causes of Deforestation and Policies for Reduced Emissions (REDD+):
A Cross-Country Analysis
--Richard J Culas
Deforestation is transformation of forestland for various land uses. Policies aimed at Reducing Emissions from Deforestation and Forest Degradation (REDD+) can provide a way for tackling global warming and climate change. A spatial model of deforestation is considered for designing effective REDD+ policies in view of internalizing the external costs of deforestation. The causes of deforestation can be classified at different levels. In this study, the causes are considered at two levels: the direct (endogenous) and the indirect (exogenous) causes. An econometric model, recursive in nature, is estimated in two stages for analyzing the interactions between the direct and the indirect causes. At the first stage, the direct causes are regressed on the indirect causes by Seemingly Unrelated Regression (SUR) method to account for the correlations between the direct causes. At the second stage, the SUR estimates of the direct causes are used for the regression of a deforestation equation. The results are discussed in relation to Asian, African and Latin American regions to provide an understanding of the mechanism for deforestation process at two levels. The spatial model presented, along with the regression results, can effectively provide guidance for designing REDD+ policies.
© 2014 IUP. All Rights Reserved.
Financial Development as an Instrument of Economic Growth in India:
Evidence from Cointegration and Causality Analysis
--Sachin Kumar
This paper tries to trace the relationship between finance and growth. There are several indicators which represent the degree of financial intermediation such as M3, Real Rate of Interest (RR) and economic growth. In this paper, we have used Time-series methodology such as Unit Root (ADF and Phillips-Perron Tests), Cointegration (developed by Johansen and Juselius), and Granger Pairwise causality. We have checked the presence of unit roots in the data, and all the three variables—Financial Development, RR and Growth Rate—are found to be integrated at first difference. Secondly, Johansen cointegration test results confirm the presence of long-run equilibrium relationship among the variables. Finally, the Granger causality supports the hypothesis of ‘Finance-led Growth’ indicating that the finance is a leading sector in India and is poised for development. This result supports the supply-leading hypothesis for Indian economy for the sample period. These findings have important implications for the conduct of economic policies in India.
© 2014 IUP. All Rights Reserved.
Estimating Volatility Pattern in Stock Markets: The Indian Case
--Saheli Das, Archana Kulkarni and Bandi Kamaiah
This paper examines the volatility pattern in Indian stock markets during the time period January 1, 2011 to March 31, 2014 using the daily closing prices of two stock indices, S&P BSE Sensex and CNX Nifty. This paper uses asymmetric GARCH models like Exponential GARCH (EGARCH) and Threshold GARCH (TGARCH) to explain the volatility. Considering the minimum values of Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), TGARCH model is found to be a superior model for return volatility over EGARCH. The findings suggest that there is no volatility persistence as well as leverage effect in the data during the period under consideration.
© 2014 IUP. All Rights Reserved.
Investors’ Perceptions on Trading Volume and Stock Return Volatility
in Indian Stock Market
--Mahender, Shalini Aggarwal and H L Verma
The present study aims to examine the investor’s perception on trading volume and stock return volatility in Indian stock market using a structured questionnaire. Statistical tools like factor analysis, ANOVA and Cronbach’s alpha are used to analyze data with the help of SPSS. The main findings show that out of the nine dimensions determined, on the basis of age, there is a significant difference in the response of the respondents in the case of tactics. On the basis of education, there is a significant difference in the response of the respondents in the case of cause-effect relationship and risk management. In all demographic profiles, there is no significant difference in trading volume and stock return volatility. The main implication of this study is for the investors and portfolio managers, as a majority of the respondents show strong willingness to use trading volume and stock return volatility as an informational tool. Therefore, this study suggests that a new approach to investment ought to be evolved which should aim at using trading volume and stock return volatility as information indicators.
© 2014 IUP. All Rights Reserved.
Book Review
Travails of Global Economy: A Way Forward to Unravel
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