Abstract:
The present paper seeks to cast scepticism on the validity and value
of the results of all earlier studies in India on energy demand analysis
and forecasting based on time series regression, on three grounds. (i) As
these studies did not care for model adequacy diagnostic checking,
indispensably required to verify the empirical validity of the residual
whiteness assumptions underlying the very model, their results might
be misleading. This criticism in fact applies to all regression analysis in
general. (ii) As the time series regression approach of these studies did
not account for possible non-stationarity (i.e., unit root integratedness)
in the series, their significant results might be just the misleading result
of spurious regression. They also failed to benefit from an analytical
framework for a meaningful long-run equilibrium and short-run
‘causality’ in a cointegrating space of error correction. (iii) These studies,
by adopting a methodology suitable to a developed power system in
advanced economies, sought to correlate the less correlatables in the
context of an underdeveloped power system in a less developed economy.
All explanations of association of electricity consumption in a hopeless
situation of chronic shortage and unreliability with its generally accepted
‘causatives’ (as in the developed systems) of population, per capita
income, average revenue, etc., all in their aggregate time series, might
not hold much water here.
Our empirical results prove our secepticism at least in the context
of Kerala power system. We find that the cost of dispensing with model
adequacy diagnosis before accepting and interpreting the seemingly
significant results is very high. We find that all the variables generally
recognised for electricity demand analysis are non-stationary, I(1). We
find that all the possible combinations of these I(1) variables fail to be
explained in a cointegrating space and even their stationary growth rates
remain unrelated in the Granger-‘causality’ sense.
