Short–run electricity demand forecasts in Maharashtra

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 study's 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.     

Decomposition of Technological Change and Factor Bias in Indian Power Sector: An Unbalanced Panel Data Approach

Technological change and factor bias in the Indian power sector are analyzed using a translog cost function. Various components of technological progress and factor bias are identified and estimated, using a 21 year unbalanced panel data of Indian states and union territories. Heterogeneity across states is incorporated in the model using a variance component model. Appropriate corrections are made for unbalanced panel data. Empirical results show that the annual average rate of technological progress has been 2.4% for the country as a whole. Accumulation of knowledge and increasing scale are found to be the major factors contributing to technological progress. In contrast, the effects of factor price changes and fixed capital accumulation on technological progress have been unfavorable. Pure factor bias measure indicate saving in the use of fuel and labor, and increased use of materials. Tests are performed to check the curvature properties of the underlying technology.     

Ownership structure and cost efficiency: A study of publicly owned passenger-bus transportation companies in India

This paper estimates the determinants of cost inefficiency of several publicly operated passenger-bus transportation companies in India in terms of their ownership structure as well as other firm-specific characteristics. A panel data on publicly operated passenger-bus transportation companies is used to estimate a translog cost system with inefficiency. Inefficiency is specified in such a way that both its mean and variance are firm- and time-specific. For the estimation of production technology and cost inefficiency we have used a multi-step estimation procedure instead of the single-step maximum likelihood (ML) method. In the first step we estimate the translog cost system with heteroskedastic cost function without using any distributional assumptions on the error terms. The second stage uses the ML method to estimate the parameters associated with inefficiency, conditional on the parameter estimates obtained from the first stage. Finally, the residual of the cost function is decomposed to obtain firm-and time-specific measures of cost inefficiency, with ownership type and other firm-specific characteristics as explanatory variables.Financial support of the Nevada Agricultural Experiment Station is gratefully acknowledged.     

Government Interventions, Market Imperfections, and Technical Inefficiency in a Mixed Economy: A Case Study of Indian Agriculture

This paper uses an output-maximizing framework in the presence of expenditure constraint to measure output loss and input misallocation resulting from market distortions and technical inefficiency. A generalized indirect production function accommodating allocative distortions and technical inefficiency is used. Allocative distortions are captured in terms of effective (shadow) prices in which distortion parameters are both farm- and input-specific. The stochastic frontier approach is used to model technical inefficiency. Using farm-level data on 105 jute growers from West Bengal, India, we find that average output losses due to allocative distortions and technical inefficiency are 6.3% and 14%, respectively.