Abstract
This study presents an analysis of the time-varying GDP, FDI, exports and energy price elasticities of electric power consumption in India over the period 1978–2015, using the Kalman filter approach. The results show that GDP and the energy price are two of the main drivers of electric power consumption in India, while FDI and exports are found to play a less significant role since they are monopoly-driven and relatively low when compared to international standards. These findings imply that increases in energy prices in India might have a significant impact on electric power consumption in the short- and long-run. Furthermore, several changes in FDI and exports seem to have affected the sensitivity of electric power consumption during the period prior to regulations, which made individuals, businesses, and agencies more sensitive to energy costs. On the other hand, the period after regulation has been characterized by more stable and declining sensitivity of electric power consumption. Therefore, factors such as regulations and changes in the country’s economic activities appear to have affected GDP, FDI, exports and energy price elasticities of electric power consumption in India.
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World Development Indicators (World Bank 2016).
References
Aayog NITI (2017) Energizing India: a joint project report of NITI Aayog and IEEJ. National Institution for Transforming India, New Delhi
Alberini A, Filippini M (2011) Response of residential electricity demand to price: the effect of measurement error. Energy Econ 33:889–895
Al-Faris ARF (2002) The demand for electricity in the GCC countries. Energy Policy 30:117–124
Andrews DWK (1993) Tests for parameter instability and structural change with unknown change point. Econom 61:821–856
Andrews DWK, Ploberger W (1994) Optimal tests when a nuisance parameter is present only under the alternative. Econom 62:1383–1414
Balabanoff S (1994) The dynamics of energy demand in Latin America. OPEC Energy Rev 18:467–488
Bekhet HA, bt Othman NS (2011) Causality analysis among electricity consumption, consumer expenditure, gross domestic product (GDP) and foreign direct investment (FDI): Case study of Malaysia. J Econ Int Financ 3:228–235
Bento JP (2011) Energy savings via foreign direct investment: empirical evidence from Portugal. Working Paper No. 2011/24, Maastricht School of Management
Bianco V, Manca O, Nardini S (2009) Electricity consumption forecasting in Italy using linear regression models. Energy 34:1413–1421
Blundell R, Bond S (2000) GMM estimation with persistent panel data: an application to production functions. Econom Rev 19:321–340
BP (2017) BP statistical review of world energy 2017
Cabinet Committee on Economic Affairs (2017) Cabinet approves enhancement of capacity from 20,000 MW to 40,000 MW of the scheme for development of solar parks and ultra mega solar power projects. Press Information Bureau, Government of India
Cho Y, Lee J, Kim TY (2007) The impact of ICT investment and energy price on industrial electricity demand: Dynamic growth model approach. Energy Policy 35:4730–4738
Chu C-SJ (1989) New tests for parameter constancy in stationary and non-stationary regression models. University of California at San Diego, Mimeo
Cole MA (2006) Does trade liberalization increase national energy use? Econ Lett 92:108–112
Cuthbertson K (1988) Expectations, learning and the Kalman filter. Manch Sch 56:223–246
Cuthbertson K, Hall SG, Taylor MP (1993) Applied econometric techniques. University of Michigan Press, USA
De Vita G, Endresen K, Hunt LC (2006) An empirical analysis of energy demand in Namibia. Energy Policy 34:3447–3463
Dickey DA, Fuller WA (1979) Distribution of the estimators for autoregressive time series with unit root. J Am Stat Assoc 74:427–431
Dilaver Z, Hunt LC (2011) Industrial electricity demand for Turkey: A structural time series analysis. Energy Econ 33:426–436
Engle RF, Granger CWJ (1987) Co-integration and error correction: representation, estimation, and testing. Econom 55:251–276
Filippini M, Pachauri S (2004) Elasticities of electricity demand in urban Indian households. Energy Policy 32:429–436
Halicioglu F (2007) Residential electricity demand dynamics in Turkey. Energy Econ 29:199–210
Hansen BE (1992) Tests for parameter instability in regressions with I(1) processes. J Bus Econ Stat 10:321–335
Hansen BE (1997) Approximate asymptotic p values for structural-change tests. J Bus Econ Stat 15:60–67
Harvey AC (1987) Applications of the Kalman filter in econometrics. In: Bewley T (ed) Advances in econometrics. I. Cambridge University Press, Fifth World Congress, Vol, pp 285–313
Harvey A (1997) Trends, cycles and autoregressions. Econ J 107:192–201
Hendry DF, Juselius K (2000) Explaining cointegration analysis: part 1. Energy J 21:1–42
Hendry DF, Juselius K (2001) Explaining cointegration analysis: part II. Energy J 22:75–120
Holtedahl P, Joutz FL (2004) Residential electricity demand in Taiwan. Energy Econ 26:201–224
Hunt LC, Salgado C, Thorpe A (1999) The policy of power and the power of policy: energy policy in Honduras. J Energy Dev 25:1–36
Hunt LC, Judge G, Ninomiya Y (2003) Underlying trends and seasonality in UK energy demand: a sectoral analysis. Energy Econ 25:93–118
Ibrahim IB, Hurst C (1990) Estimating energy and oil demand functions: a study of thirteen developing countries. Energy Econ 12:93–102
Inglesi R (2010) Aggregate electricity demand in South Africa: conditional forecasts to 2030. Appl Energy 87:197–204
Inglesi-Lotz R (2011) The evolution of price elasticity of electricity demand in South Africa: a Kalman filter application. Energy Policy 39:3690–3696
Johansen S (1991) Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. Econom 59:1551–1580
Jumbe CB (2004) Cointegration and causality between electricity consumption and GDP: empirical evidence from Malawi. Energy Econ 26:61–68
Kalman RE (1960) A new approach to linear filtering and prediction problems. J Basic Eng 82:35–45
Kalman RE (1963) New methods in Wiener filtering theory. In: Bogdanoff JL, Kozin F (eds) Proceeding of the first symposium on engineering applications of random function theory and probability. John Wiley and Sons, New York, pp 270–388
Keho Y (2016) What drives energy consumption in developing countries? The experience of selected African countries. Energy Policy 91:233–246
Kiviet JF (1995) On bias, inconsistency, and efficiency of various estimators in dynamic panel data models. J Econom 68:53–78
Lawson T (1980) Adaptive expectations and uncertainty. Rev Econ Stud 47:305–320
Masreliez C, Martin R (1977) Robust Bayesian estimation for the linear model and robustifying the Kalman filter. IEEE Trans Automat Contr 22:361–371
Milani F (2007) Expectations, learning and macroeconomic persistence. J Monet Econ 54:2065–2082
Ministry of New and Renewable Energy (2012) Guidelines for tariff based competitive bidding process for grid connected power projects based on renewable energy sources. Ministry of New and Renewable Energy, Government of India
Ministry of New and Renewable Energy (2015) Green energy corridor project. Press Information Bureau, Government of India
Morisson GW, Pike DH (1977) Kalman filter applied to statistical forecasting. Manag Sci 23:768–774
Nakajima T, Hamori S (2010) Change in consumer sensitivity to electricity prices in response to retail deregulation: a panel empirical analysis of the residential demand for electricity in the United States. Energy Policy 38:2470–2476
Phillips PCB, Perron P (1988) Testing for a unit root in time series regression. Biom 75:335–346
Pouris A (1987) The price elasticity of electricity demand in South Africa. Appl Econ 19:1269–1277
Sbia R, Shahbaz M, Hamdi H (2014) A contribution of foreign direct investment, clean energy, trade openness, carbon emissions and economic growth to energy demand in UAE. Econ Model 36:191–197
Shahbaz M, Tang CF, Shabbir MS (2011) Electricity consumption and economic growth nexus in Portugal using cointegration and causality approaches. Energy Policy 39:3529–3536
Sinha A, Bhattacharya J (2014) Is economic liberalization causing environmental degradation in India? An analysis of interventions. J Appl Bus Econ 16:121–136
Sinha A, Shahbaz M (2018) Estimation of environmental Kuznets curve for CO2 emission: role of renewable energy generation in India. Renew Energy 119:703–711
Slade ME (1989) Modelling stochastic and cyclical components of technical change: an application of the Kalman filter. J Econom 41:363–383
TERI (2017) Transitions in Indian electricity sector: 2017–2030. The Energy and Resources Institute
Thamae RI, Thamae LZ, Thamae TM (2015) Dynamics of electricity demand in Lesotho: A Kalman filter approach. Stud Bus Econ 10:130–139
United Nations (2015) Sustainable development goals. Department of Public Information, United Nations
Wiener N (1949) Extrapolation, interpolation and smoothing of stationary time series with engineering applications. MIT Press, Cambridge, MA
World Bank (2016) World bank indicators. Retrieved from http://data.worldbank.org/indicator
Zaman K, Khan MM, Ahmad M, Rustam R (2012) Determinants of electricity consumption function in Pakistan: old wine in a new bottle. Energy Policy 50:623–634
Zivot E, Andrews D (1992) Further evidence of the great crash, the oil-price shock, and the unit-root hypothesis. J Bus Econ Stat 10:251–270
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Farhani, S. (2021). The Impact of Regulations, Energy Prices and Economic Activity on the India’s Electric Power Consumption: A Kalman Filter Application. In: Goutte, S., Guesmi, K., Boroumand, R.H., Porcher, T. (eds) Advances in Managing Energy and Climate Risks. Lecture Notes in Energy, vol 82. Springer, Cham. https://doi.org/10.1007/978-3-030-71403-1_4
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