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Department of Economics Issn 1441-5429 Discussion DEPARTMENT OF ECONOMICS ISSN 1441-5429 DISCUSSION PAPER 03/15 Conditional Convergence in US Disaggregated Petroleum Consumption at the Sector Level Hooi Hooi Leana and Russell Smythb Abstract: We test for convergence in disaggregated petroleum consumption at the sector level for the United States using the recently proposed GARCH unit root test, suitable for high frequency data. We find evidence of convergence for just over half of the series, including total petroleum consumption in each sector and approximately three quarters of the disaggregated petroleum consumption series in transportation. Keywords: Convergence; Petroleum consumption, Unit root, United States a Hooi Hooi Lean, Economics Program, School of Social Sciences, Unversiti Sains Malaysia; 11800 Pulau Pinang Malaysia; email: [email protected]; [email protected]; Phone: 04 653 2663 b Department of Economics, Monash University, Australia © 2015 Hooi Hooi Lean and Russell Smyth All rights reserved. No part of this paper may be reproduced in any form, or stored in a retrieval system, without the prior written permission of the author monash.edu/ business-economics ABN 12 377 614 012 CRICOS Provider No. 00008C 1. Introduction Beginning with Narayan and Smyth (2007) a large literature exists that tests for a unit root in energy consumption. This literature is reviewed in Smyth (2013). More recently, a smaller literature has developed which applies unit root tests to test for conditional convergence in energy consumption. Evidence on the existence, or otherwise, of conditional convergence in energy consumption can prove insightful for determining whether policies designed to reduce the intensity of energy consumption are effective. In the case of policies designed to reduce the intensity of fossil fuel consumption, this also has further implications for the efficacy of efforts aimed at reducing greenhouse gas emissions and curtailing global warming. In short, if there is evidence of energy convergence, and growth rates are relatively modest, this suggests that policies designed to curtail energy consumption are being effective. The seminal article on conditional convergence in energy consumption is Meng et al. (2013). These authors applied unit root tests to examine convergence in energy consumption per capita in Organisation for Economic Cooperation and Development (OECD) countries and found support for the hypothesis that energy consumption is converging in these high-income countries. Subsequent studies have applied unit root tests to examine convergence in energy consumption among other groups of countries in Africa and Asia (Anoruo & DiPietro, 2014; Mishra & Smyth 2014a). These studies have generally found evidence of convergence in energy consumption. At the conclusion of their article, Meng et al. (2013, p.545) propose: “Future research can extend the methodological approach taken in this study [to] …. sector analysis of energy use convergence within a specific country as well as across countries”. More recently, Mishra and Smyth (2014a, p. 184), who test for conditional energy convergence across Association of Southeast Asian (ASEAN) countries, propose: “Future research could consider convergence in disaggregated energy across sectors”. In this paper we extend the literature on energy convergence in two directions. First, we take up the suggestion in Meng et al. (2013) and Mishra and Smyth (2014a) to apply unit root tests to study conditional convergence in disaggregated energy at the sector level. Specifically, we apply a series of unit root tests to examine conditional convergence of disaggregated petroleum consumption across five sectors in the United States using monthly data over the period January 1973 to June 2014. Second, we make a methodological contribution in that in addition to the Augmented Dickey-Fuller (ADF) and Narayan and Popp (2010) unit root tests we apply the Narayan and Liu (2013) generalized autoregressive conditional heteroskedasticity (GARCH) unit root test. The latter has the advantage that it not only allows for structural breaks, but accommodates heteroskedasticity, which is likely to be present in high frequency energy consumption data. The Narayan and Liu (2013) test has recently been applied when testing for a unit root in monthly energy consumption data (see Mishra & Smyth, 2014b), but has not been applied in the conditional energy convergence literature. In this respect, we take up, and marry, two suggestions for future research in a recent article that surveys the state-of-the-art in econometric modelling of energy variables (Smyth & Narayan, 2014). That article suggested that future research should address heteroskedasticity in high frequency energy data and further examine conditional convergence in energy consumption. Our focus is on petroleum consumption because of its importance as an energy source in the United States and the extensive ongoing debates about effectiveness of policies to curtail its use on environmental grounds. In 2012, petroleum and other liquids was the single largest type of energy consumption in the United States, being responsible for 37.8 per cent of total energy consumption (EIA, 2014, Table A1). To see this in global terms, this figure represents one quarter of the world’s total petroleum consumption. In per capita terms, United States consumption is about 30 per cent higher than the next largest consumer of petroleum, which is Canada (Knittel, 2012). It is important to consider petroleum consumption at the sector level, given its relative importance as an energy source varies considerably across sectors. In 2012 in the United States, petroleum and other liquids was responsible for 97 per cent of energy consumption in the transport sector, 34.1 per cent of energy consumption in the industrial sector, 9.8 per cent of energy consumption in the residential sector and 7.6 per cent of energy consumption in the commercial sector (EIA, 2014, Table A2).1 The need to improve efficiency of petroleum consumption is one of the most pressing issues in United States energy policy. Policies to reduce petroleum consumption include policies to promote renewable energy alternatives. Examples are the Energy Policy Acts, passed in 2002 and 2005, and the Federal Energy Independence and Security Act, passed in 2007, each of which contains financial incentives and tax measures to promote renewable energy at the expense of petroleum based products. The need to improve efficiency of petroleum consumption is particularly pressing in high use sectors, such as transport, which are responsible for 30 per cent of United States greenhouse gas emissions (Knittel, 2012). Initiatives in this area include policies such as the Corporate Average Fuel Economy (CAFE) standards that set minimum fuel economy requirements for new cars. Testing for conditional convergence speaks directly to the efficacy of such policies. 1 Figures are as a proportion of delivered energy (excluding electricity-related losses). We focus on disaggregated petroleum consumption at the sector level because intensity in petroleum consumption can be expected to differ not only across sectors, but also across different petroleum products (Yang, 2000). The interaction of petroleum product and sector is also likely to be important. The extent to which (disaggregated) petroleum consumption occurs in particular sectors is likely to effect the level of persistence following a shock and, hence, the degree of conditional convergence. As such, if one were to focus on aggregate petroleum consumption, potentially much information would be lost. At the very least, aggregate results would mask differences across different petroleum products and sectors. 2. Method 2.1. Petroleum consumption ratios There are three categories of petroleum consumption ratio based on the disaggregated types and sectors. First, we examine whether or not the natural log of the difference between aggregate petroleum consumption from each specific sector i and total petroleum consumption in the United States is stationary as per Equation (1): Yit ln PC t PC it (1) Here PC denotes total petroleum consumption in the United States at time t and PC t it denotes aggregate petroleum consumption by sector i at time t. Equation (1) denotes the natural log of the petroleum consumption ratio – i.e. total petroleum consumption in the United States divided by aggregate petroleum consumption by sector i. Second, there are also different petroleum types (Distillate Fuel Oil, Liquefied Petroleum Gases, Motor Gasoline and Residual Fuel Oil). Hence, we compute (2) Here denotes total type j petroleum consumption in the United States at time t and denotes type j petroleum consumption by sector i at time t. Equation (2) denotes the natural log of the type j petroleum consumption ratio – i.e. total type j petroleum consumption in the United States divided by type j petroleum consumption by sector i. Third, we also examine total petroleum consumption in a sector itself using equation (3): (3) Here denotes total petroleum consumption in the sector i at time t and denotes type j petroleum consumption by sector i at time t. Thus Equation (3) denotes the natural log of the sector i petroleum consumption ratio – i.e. total petroleum consumption in the sector i divided by type j petroleum consumption by sector i. Based on these three equations, we test conditional convergence for 45 series in total; namely,Yit ln PCEquation t PC it 1 - five series, Equation 2 – 16 series and Equation 3 – 24 series. 2.2. Unit root tests PCt
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