Sajal Ghosh
Management Development Institute
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Featured researches published by Sajal Ghosh.
Energy Policy | 2002
Sajal Ghosh
Abstract This paper tries to examine the Granger causality between electricity consumption per capita and Gross Domestic Product (GDP) per capita for India using annual data covering the period 1950–51 to 1996–97. Phillips–Perron tests reveal that both the series, after logarithmic transformation, are non-stationary and individually integrated of order one. This study finds the absence of long-run equilibrium relationship among the variables but there exists unidirectional Granger causality running from economic growth to electricity consumption without any feedback effect. So, electricity conservation policies can be initiated without deteriorating economic side effects.
International Journal of Indian Culture and Business Management | 2008
Sajal Ghosh
This study forecasts the monthly peak demand of electricity in the northern region of India using univariate time-series techniques namely Multiplicative Seasonal Autoregressive Integrated Moving Average (MSARIMA) and Holt-Winters Multiplicative Exponential Smoothing (ES) for seasonally unadjusted monthly data spanning from April 2000 to February 2007. In-sample forecasting reveals that the MSARIMA model outperforms the ES model in terms of lower root mean square error, mean absolute error and mean absolute percent error criteria. It has been found that ARIMA (2, 0, 0) (0, 1, 1)12 is the best fitted model to explain the monthly peak demand of electricity, which has been used to forecast the monthly peak demand of electricity in northern India, 15 months ahead from February 2007. This will help Northern Regional Load Dispatch Centre to make necessary arrangements a priori to meet the future peak demand.
International Journal of Indian Culture and Business Management | 2011
Sajal Ghosh
The impact of international tourism on a country’s economic growth has attracted a great deal of attention among economists and policy makers. This study probes tourism-led growth (TLG) hypothesis for India employing bounds test and Johansen approaches of cointegration using annual data for the time span from 1980 to 2006 in a multivariate framework. Empirical results reveal the absence of a long-term equilibrium relationship between international tourist arrivals and economic activity in India. It also fails to establish any short-run relationship between international tourist arrivals and economic growth in an unrestricted vector autoregression framework. Thus, this study rejects TLG hypothesis for India.
Applied Economics | 2002
Sajal Ghosh; Anjana Das
This paper, has tried to forecast monthly maximum electricity demand for the state Maharashtra, India, using Multiplicative Seasonal Autoregressive Integrated Moving Average (MSARIMA) method for seasonally unadjusted monthly data spanning from April 1980 to June 1999. The forecasted period is 18 months ahead from June 1999. This studys basic findings are that the series does not reveal any drastic change for the forecasted period. It continues to follow the same trend along with the seasonal variation.
Macroeconomics and Finance in Emerging Market Economies | 2014
Sajal Ghosh; Kakali Kanjilal
The study investigates the dynamic impact of linear and non-linear specifications of oil price shocks on macroeconomic fundamentals for an oil-importing emerging economy – India – during the period March 1991 to January 2009. The paper deploys extended vector autoregressive (VAR) model of possibly integrated processes proposed by Toda and Yamamoto, which has its advantage of application irrespective of the variables being stationary or cointegrated. The study further estimates two-state Markov regime-switch VAR model to examine regime shift behaviour of the underlying variables and its relationship. The study finds that inflation and foreign exchange reserve are greatly impacted by oil price shocks. The study also confirms that the movement in oil price is exogenous with respect to the movement of India’s macroeconomic variables and the impact of oil price shocks are asymmetric in nature with negative price shocks having more pronounced effect than positive shocks.
International Journal of Indian Culture and Business Management | 2007
Sajal Ghosh
The Indian energy sector has been characterised by low per capita commercial energy consumption, skewed distribution of primary commercial energy sources, ballooning demand – supply gap, inadequate energy infrastructure, and high import dependence mainly in the form of crude oil. Energy consumption in India is expected to more than double by 2020 to propel the development aspirations. Under such circumstances, India needs to explore all the supply side and demand side energy options along with the requisite infrastructure to bridge the demand–supply gap. This requires huge investment. The paper outlines investment opportunities and potentials in the sectors such as coal, power, oil and gas, renewables, energy efficiency and through Clean Development Mechanism route along with government policy initiatives to foster investment. Finally, the paper identifies some bottlenecks for private investment and suggests remedies to attract private investment.
Applied Economics | 2016
Anshul Jain; Pratap Chandra Biswal; Sajal Ghosh
ABSTRACT This study examines the causal relationships between volatility and volume across spot and futures market for the 50 constituent stocks of the CNX NIFTY Index. Granger non-causality tests implemented using vector autoregression (VAR) and asymmetric VAR models indicate the presence of significant causal relations from both the spot and futures volume to both the spot and futures volatility. Bidirectional causal relationships between spot and futures volume were observed for almost all stocks but few stocks displayed a similar relationship between volatilities. The results highlight the importance of volume in absorbing information and its behaviour as the conduit of information.
International Journal of Green Energy | 2016
Sunil Kumar Sharma; Sajal Ghosh
ABSTRACT This study forecasts day-ahead wind speed at 15 minute intervals at the site of a wind turbine located in Maharashtra, India. Wind speed exhibits non-stationarity, seasonality and time-varying volatility clustering. Univariate linear and non-linear time series techniques namely MSARIMA, MSARIMA-GARCH and MSARIMA-EGARCH have been employed for forecasting wind speed using data span ranging from 3 days to 15 days. Study suggests that mean absolute percentage error (MAPE) values first decrease with the increase in data span, reaches its minima and then start increasing. All models provide superior forecasting performances with 5 days data span. It is further evident that ARIMA-GARCH model generates lowest MAPE with 5 days data span. All these models provide superior forecasts with respect to current industry practices. This study establishes that employing various linear and non-linear time series techniques for forecasting day-ahead wind speed can benefit the industry in terms of better operational management of wind turbines and better integration of wind energy into the power system, which have huge financial implications for wind power generators in India.
Journal of Resources, Energy, and Development | 2006
Sajal Ghosh; Sujay Basu
This paper examines ‘coal consumption—GDP (gross domestic product)’ and ‘gas consumption—GDP’ Granger causality techniques. Augmented Dickey-Fuller tests reveal that all series viz. per capita GDP, per capita coal consumption, and per capita gas consumption, after logarithmic transformation are non-stationary and individually integrated of the order one. This study reveals the absence of co-integration but finds the existence of unidirectional Granger causality running from coal consumption to economic growth and from economic growth to gas consumption in bi-variate vector autoregression frameworks using annual data, covering the period 1970/71 to 2001/02. Thus, lowering the share of coal in the fuel mix would adversely affect India’s economic growth. On the other hand, a growth in the income is found to be responsible for the gas consumption being clean and efficient in nature.
International Journal of Indian Culture and Business Management | 2014
Sajal Ghosh; Kakali Kanjilal
Electricity prices often exhibit extreme volatility due to its non-storable nature coupled with significant seasonal and diurnal variations of demand, supply constraints at peak hours and transmission bottlenecks. This study tries to forecast day-ahead hourly electricity price of Indian energy exchange (IEX) with an additional objective of modelling the volatility using MSARIMA and MSARIMA-EGARCH models. It has been found that MSARIMA-EGARCH model slightly outperform MSARIMA model in terms of in-sample forecasting performances. The study reveals that seasonality and time-varying volatility are present and past shocks to the variance are asymmetric with negative shocks give rise to higher volatility of price than positive shocks. The study also establishes that shocks to electricity price volatility die out almost instantaneously. The above information can help to build up cost effective risk management plans for the participating companies in IEX.