DEPARTMENT OF ECONOMICS ISSN DISCUSSION PAPER 03/15. Conditional Convergence in US Disaggregated Petroleum Consumption at the Sector Level

Transcription

1 DEPARTMENT OF ECONOMICS ISSN DISCUSSION PAPER 03/15 Conditional Convergence in US Disaggregated Petroleum Consumption at the Sector Level Hooi Hooi Lean a and Russell Smyth b 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; Pulau Pinang Malaysia; learnmy@gmail.com; hooilean@usm.my; Phone: 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 CRICOS Provider No C

2 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

3 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 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

4 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).

5 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): Y ln PC PC it t it (1) Here PCt denotes total petroleum consumption in the United States at time t and PC 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 Y ln PC PC it t it (2)

6 Here PCt 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) PC it 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): Y ln PC PC it t it (3) Here PCt denotes total petroleum consumption in the sector i at time t and PCit 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, Equation 1 - five series, Equation 2 16 series and Equation 3 24 series Unit root tests To examine the convergence hypothesis for 45 series generated from Equations (1) to (3), we use the conventional ADF test, Narayan and Popp (2010) two breaks unit root test and the Narayan and Liu (2013) GARCH unit root test with two structural breaks in the intercept. We use the Narayan and Popp (2010) test, rather than other popular two-break unit root tests, such as Lumsdaine and Papell (1997) and Lee and Strazicich (2003) for two reasons. First, Narayan and Popp (2013) demonstrate that Narayan and Popp (2010) has better power properties than the other two tests.

7 Second, Narayan and Popp (2010) and Narayan and Liu (2013) use the same approach for detecting the breaks, thus there is a consistency of approach in the two methods, allowing one to get a cleaner picture of the relevance of heteroskedasticity when it comes to examining the Narayan and Liu (2013) test. The advantage of this three-step approach to testing for convergence is that the ADF test serves as a benchmark. The Narayan and Popp (2010) test allows one to see the effect of allowing for structural breaks, but not heteroskedasticity, when testing for conditional convergence. Hence, it is one step up over the ADF test. Finally, for those series for which the data are heteroskedastic, the Narayan and Liu (2013) test allows one to see the effect of both structural breaks and heteroskedasticity on conditional convergence. Thus, it addresses two shortcomings of the ADF test. We do not repeat the method for any of the three tests here, but refer readers to the original papers or previous applications of the tests in the energy literature in which the method is laid out. The ADF test is regularly used as a benchmark unit root test and is widely known. Similarly, the Narayan and Popp (2010) and Narayan and Liu (2013) tests been applied previously in several studies in the energy literature (see eg. Apergis & Payne, 2010; Mishra & Smyth 2014a,b; Narayan & Liu, 2011) Data We examine different types of petroleum consumption by five sectors for the United States; namely, residential, commercial, industrial, transportation and electric power. The series are monthly consumption of different petroleum types measured in 2 Some of these studies apply earlier working paper versions of Narayan and Liu (2013).

8 thousand of barrels per day and are extracted from the Monthly Energy Review of the Energy Information Administration. The sample period is January 1973 to June [Insert Table 1 & Figure 1 here] Table 1 presents descriptive statistics for the sample. Liquefied petroleum gas consumed by the transport sector has the highest mean. Petroleum coke consumed by the electric power sector has the highest standard deviation. Motor gasoline consumed by the transport sector has the lowest mean and standard deviation. Figure 1 presents time series plots for aggregate petroleum consumption in each sector. Residential, commercial and electric power sectors display an increasing trend while the industrial and transportation sectors exhibit a decreasing trend. 3. Results Table 2 presents the results of the ADF test, in order to provide a benchmark. For most of the series, the petroleum consumption rates are integrated of order 1 (I(1)) with both intercept only and intercept and trend. The only series for which the unit root null is rejected with both intercept only and intercept and trend tests are motor gasoline consumed by the industrial sector and petroleum coke consumed by the industrial sector for Equation (3) and the former is only weakly significant in the intercept only model. Thus, there is little evidence of conditional convergence. [Insert Table 2 here] However, not much stake can be put in the results of the ADF test. The results suffer from the dual bias that power to reject the unit root null is low in the presence of structural breaks (Perron, 1989) and conditional heteroskedasticity (Kim & Schmidt, 1993). The monthly petroleum consumption data is likely to suffer from both biases. [Insert Table 3 here]

9 Table 3 presents the results of the Narayan and Popp (2010) Model 1 (M1) and Model 2 (M2). M1 allows for two breaks in the intercept and M2 allows for two breaks in the intercept and trend. Distillate fuel oil consumed by the commercial sector and motor gasoline consumed by the commercial sector are significant in both M1 and M2 cases for Equation (3), rejecting the unit root null, although only weakly so in M2. Three further series are significant in M1, while one further series is significant in M2 for Equation (3), rejecting the unit root null. Overall, the Narayan and Popp (2010) test provides little evidence of conditional convergence. With M1, 11 per cent of the series exhibit conditional convergence, while with M2 the comparable figure is 9 per cent. Moreover, for several of the series, the unit root null is only weakly rejected. Applying a 5 per cent cut-off, there is even less evidence of conditional convergence. A limitation of the Narayan and Popp (2010) test is that it is constructed on a linear model that is not suitable for modelling heteroskedasticity. Narayan and Liu (2013) relax the assumption of identically independent distributed errors, in proposing a GARCH (1,1) framework that can accommodate heteroskedasticity. Thus, the Narayan and Liu (2013) test is more suitable in the presence of heteroskedasticity, which is likely to be more likely in high frequency data. The results of an ARCH(12) LM test are reported in the final column of Table 1. We selected a lag of 12 given that we employ monthly data (see also Mishra & Smyth, 2014b). The null of no arch effect is rejected 27 of the 45 series (60 per cent of the sample). [Insert Table 4 here] Table 4 presents the Narayan and Liu (2013) test for the 27 series for which there is an arch effect. For these 27 series there is much more evidence of conditional convergence. The unit root null is rejected for all but four of the series at the 5 per

10 cent level or better. Thus, for 85 per cent of the 27 series for which there is an arch effect, the Narayan and Liu (2013) test finds evidence of conditional convergence. 4. Discussion We now turn to an overall evaluation of the level of support for conditional convergence in United States disaggregated petroleum consumption at the sector level. The tests give different results for some series. Thus, we need a way to decide between them. Given that Narayan and Popp (2010) and Narayan and Liu (2013) adopt the same approach to choosing the break dates, the key point for distinguishing between these tests is whether the ARCH LM test finds heteroskedasticity in the data. For those series for which there is heteroskedasticity, we prefer the results of the Narayan and Liu (2013) test. For the other series, we prefer the results of the Narayan and Popp (2013) test. Using this approach as a rule of thumb, we can put together a hybrid set of findings between Narayan and Popp M1 (intercept only) and Narayan and Liu (2013). The unit root null is rejected for 23 series by Narayan and Liu (2013) and a further one series (motor gasoline consumed by the commercial sector for Equation (3)) by Narayan and Popp (2010). Thus for 24 of the 45 series or 53 per cent of the total, the unit root null is rejected, suggesting conditional convergence in just over one half of the (disaggregated) petroleum consumption series at the sector level. Some important insights, with policy implications, can be gleaned by considering which series exhibit conditional convergence. The first point to note is that total petroleum consumption exhibits conditional convergence in each of the five sectors. The second point is that when we turn to disaggregated petroleum, there are differences in the extent of conditional convergence across sectors. In transportation eight of 11 (73 per cent) of disaggregated petroleum series exhibit convergence. In the other sectors, the comparable figures are industrial (four out of 12 or 33 per cent);

11 residential (two out of four or 50 per cent); commercial (4 out of eight or 50 per cent) and electric power (one out of five or 20 per cent). Thus, at the disaggregated level, there is a lot of evidence of convergence in the transport sector, which is important from a policy perspective given the dominance of petroleum consumption as an energy source in that sector. The other sector in which petroleum is important representing in excess of one third of energy consumption is the industrial sector. There is less evidence of conditional convergence at the disaggregated level there. The location of the breaks in both the Narayan and Popp (2010) and Narayan and Liu (2013) tests are associated with a mix of world and domestic events influencing petroleum markets. The major world events are the 1973 oil crisis, the 1979 oil crisis, the 1990 oil price shock and the 2000 energy crisis. Domestic initiatives that are potential causes of some of the breaks in the 1990s are the Solar, Wind, Waste and Geothermal Act (1990) and Energy policy Act (1992). Initiatives in the 2000s included the Energy Policy Acts and Federal Energy Independence and Security Act, enacted in 2002, 2005 and 2007 respectively. Each of these pieces of legislation promoted renewable energy at the expense of petroleum products. 5. Conclusion and Policy Implications We find that just over half of the 45 series considered exhibit conditional convergence. Importantly, though, we find evidence of conditional convergence for total petroleum consumption in each of the five sectors and approximately three quarters of disaggregated petroleum consumption in the transport sector. This result suggests that for total petroleum consumption at the sector level and for several types of disaggregated petroleum consumption in transport at least, policies to generate efficiency savings in petroleum consumption and promote alternatives, such as

12 renewable energy, can be expected to generate further convergence and, thus, be effective. All in all, the findings paint an optimistic picture for generating savings in total petroleum consumption at the sector level. The results are more mixed at the disaggregated level and vary across sectors. Future research could examine conditional convergence in alternative forms of energy consumption at the sector level in the United States or at the sector level for other countries.

13 References Anorou, E. and DiPietro, W.R. (2014) Convergence in per capita energy consumption among African countries: Evidence from a sequential panel selection method, International Journal of Energy Economics and Policy, 4(4). Apergis N and Payne J.E. (2010) Structural breaks and petroleum consumption in US states: are shocks transitory or permanent? Energy Policy, 38, Energy Information Administration (EIA) (2014) Annual Energy Outlook United States Department of Energy, Washington DC. Kim, K. and Schmidt, P. (1993). Unit root tests with conditional heteroskedasticity. Journal of Econometrics, 59, Knittel, C.R. (2012) Reducing petroleum consumption from transportation. Journal of Economic Perspectives, 26(1), Lee, J. and Strazicich, M. C. (2003) Minimum Lagrange multiplier unit root test with two structural breaks, Review of Economics and Statistics, 85, Lumsdaine, R. and Papell, D. (1997) Multiple trend breaks and the unit root hypothesis, Review of Economics and Statistics, 79, Meng, M., Payne, J.E. and Lee, J. (2013) Convergence in per capita energy use among OECD countries, Energy Economics, 36, Mishra, V. and Smyth, R. (2014a) Convergence in energy consumption per capita among ASEAN countries, Energy Policy, 73, Mishra, V. and Smyth, R. (2014b) Is monthly natural gas consumption stationary? New evidence from a GARCH unit root test with structural breaks, Energy Policy, 69, Narayan, PK. and Liu, R. (2011) Are shocks to commodity prices persistent? Applied Energy, 88, Narayan, PK. and Liu, R. (2013). New evidence on the weak-form efficient market hypothesis. Working Paper, Centre for Financial Econometrics, Deakin University Narayan, PK. and Popp, S. (2010). A new unit root test with two structural breaks in level and slope at unknown time. Journal of Applied Statistics, 37, Narayan, PK. and Popp, S. (2013). Size and power properties of structural break unit root tests. Applied Economics, 45, Narayan PK. and Smyth R. (2007) Are shocks to energy consumption permanent or temporary? Evidence from 182 countries, Energy Policy, 35, Perron, P. (1989) The great crash, the oil price shock and the unit root hypothesis, Econometrica, 57, Smyth, R. (2013) Are fluctuations in energy variables permanent or transitory? A survey of the literature on the integration properties of energy consumption and production, Applied Energy, 104,

14 Smyth, R. and Narayan, PK. (2014) Applied econometrics and implications for energy economics research, Energy Economics (in press) Yang, H.Y. (2000). A note on the causal relationship between energy and GDP in Taiwan, Energy Economics, 22,

15 Table 1 Descriptive statistics Series Mean Std. Dev. Skewness Kurtosis ARCH (12) LM Test Five series analysed using Equation (1) Total Petroleum Consumed by Residential Sector *** Total Petroleum Consumed by Commercial Sector *** Total Petroleum Consumed by Industrial Sector Total Petroleum Consumed by Transportation Sector *** Total Petroleum Consumed by Electric Power Sector *** Sixteen series analysed using Equation (2) Distillate Fuel Oil Consumed by Residential Sector *** Distillate Fuel Oil Consumed by Commercial Sector *** Distillate Fuel Oil Consumed by Industrial Sector Distillate Fuel Oil Consumed by Transportation Sector *** Distillate Fuel Oil Consumed by Electric Power Sector Liquefied Petroleum Gases Consumed by Residential Sector Liquefied Petroleum Gases Consumed by Commercial Sector Liquefied Petroleum Gases Consumed by Industrial Sector Liquefied Petroleum Gases Consumed by Transportation Sector Motor Gasoline Consumed by Commercial Sector Motor Gasoline Consumed by Industrial Sector Motor Gasoline Consumed by Transportation Sector Residual Fuel Oil Consumed by Commercial Sector *** Residual Fuel Oil Consumed by Industrial Sector *** Residual Fuel Oil Consumed by Transportation Sector *** Residual Fuel Oil Consumed by Electric Power Sector *** Twenty-four series analysed using Equation (3) Distillate Fuel Oil Consumed by Residential Sector *** Liquefied Petroleum Gases Consumed by Residential Sector *** Distillate Fuel Oil Consumed by Commercial Sector *** Liquefied Petroleum Gases Consumed by Commercial Sector *** Motor Gasoline Consumed by Commercial Sector Residual Fuel Oil Consumed by Commercial Sector Asphalt and Road Oil Consumed by Industrial Sector ***

16 Distillate Fuel Oil Consumed by Industrial Sector Liquefied Petroleum Gases Consumed by Industrial Sector * Lubricants Consumed by Industrial Sector Motor Gasoline Consumed by Industrial Sector Petroleum Coke Consumed by Industrial Sector Residual Fuel Oil Consumed by Industrial Sector *** Other Petroleum Products Consumed by Industrial Sector *** Aviation Gasoline Consumed by Transportation Sector *** Distillate Fuel Oil Consumed by Transportation Sector *** Jet Fuel Consumed by Transportation Sector *** Liquefied Petroleum Gases Consumed by Transportation Sector Lubricants Consumed by Transportation Sector *** Motor Gasoline Consumed by Transportation Sector ** Residual Fuel Oil Consumed by Transportation Sector *** Distillate Fuel Oil Consumed by Electric Power Sector Petroleum Coke Consumed by Electric Power Sector *** Residual Fuel Oil Consumed by Electric Power Sector *** Note: *,** and *** denotes statistical significance at the10%, 5% and 1% levels respectively.

17 Table 2 ADF unit root tests Intercept Intercept and trend Series level first diff. level first diff. Five series analysed using Equation (1) Total Petroleum Consumed by Residential Sector *** *** Total Petroleum Consumed by Commercial Sector *** *** Total Petroleum Consumed by Industrial Sector *** *** Total Petroleum Consumed by Transportation Sector *** *** Total Petroleum Consumed by Electric Power Sector *** *** Sixteen series analysed using Equation (2) Distillate Fuel Oil Consumed by Residential Sector *** *** Distillate Fuel Oil Consumed by Commercial Sector *** *** Distillate Fuel Oil Consumed by Industrial Sector *** *** Distillate Fuel Oil Consumed by Transportation Sector *** *** Distillate Fuel Oil Consumed by Electric Power Sector *** *** Liquefied Petroleum Gases Consumed by Residential Sector *** *** Liquefied Petroleum Gases Consumed by Commercial Sector *** *** Liquefied Petroleum Gases Consumed by Industrial Sector ** *** *** Liquefied Petroleum Gases Consumed by Transportation Sector *** *** Motor Gasoline Consumed by Commercial Sector *** *** Motor Gasoline Consumed by Industrial Sector ** *** *** Motor Gasoline Consumed by Transportation Sector *** *** Residual Fuel Oil Consumed by Commercial Sector *** *** Residual Fuel Oil Consumed by Industrial Sector *** *** Residual Fuel Oil Consumed by Transportation Sector *** *** Residual Fuel Oil Consumed by Electric Power Sector *** *** Twenty-four series analysed using Equation (3) Distillate Fuel Oil Consumed by Residential Sector *** *** Liquefied Petroleum Gases Consumed by Residential Sector *** *** Distillate Fuel Oil Consumed by Commercial Sector ) *** *** Liquefied Petroleum Gases Consumed by Commercial Sector *** *** Motor Gasoline Consumed by Commercial Sector *** *** Residual Fuel Oil Consumed by Commercial Sector *** ***

18 Asphalt and Road Oil Consumed by Industrial Sector *** *** Distillate Fuel Oil Consumed by Industrial Sector *** *** Liquefied Petroleum Gases Consumed by Industrial Sector *** *** Lubricants Consumed by Industrial Sector *** *** Motor Gasoline Consumed by Industrial Sector * *** ** *** Petroleum Coke Consumed by Industrial Sector *** *** *** *** Residual Fuel Oil Consumed by Industrial Sector *** *** *** Other Petroleum Products Consumed by Industrial Sector ** *** *** Aviation Gasoline Consumed by Transportation Sector *** ** *** Distillate Fuel Oil Consumed by Transportation Sector *** *** Jet Fuel Consumed by Transportation Sector *** *** Liquefied Petroleum Gases Consumed by Transportation Sector *** *** Lubricants Consumed by Transportation Sector *** *** Motor Gasoline Consumed by Transportation Sector *** *** Residual Fuel Oil Consumed by Transportation Sector *** *** Distillate Fuel Oil Consumed by Electric Power Sector *** ** *** Petroleum Coke Consumed by Electric Power Sector *** ** *** Residual Fuel Oil Consumed by Electric Power Sector *** *** Note: *,** and *** denotes statistical significance at the 10%, 5% and 1% levels respectively

19 Table 3 Narayan-Popp (2010) two break unit root test M1 M2 Series t-stat TB1 TB2 k t-stat TB1 TB2 K Five series analysed using Equation (1) Total Petroleum Consumed by Residential Sector Total Petroleum Consumed by Commercial Sector Total Petroleum Consumed by Industrial Sector Total Petroleum Consumed by Transportation Sector Total Petroleum Consumed by Electric Power Sector Sixteen series analysed using Equation (2) Distillate Fuel Oil Consumed by Residential Sector Distillate Fuel Oil Consumed by Commercial Sector Distillate Fuel Oil Consumed by Industrial Sector Distillate Fuel Oil Consumed by Transportation Sector Distillate Fuel Oil Consumed by Electric Power Sector * Liquefied Petroleum Gases Consumed by Residential Sector Liquefied Petroleum Gases Consumed by Commercial Sector Liquefied Petroleum Gases Consumed by Industrial Sector Liquefied Petroleum Gases Consumed by Transportation Sector Motor Gasoline Consumed by Commercial Sector Motor Gasoline Consumed by Industrial Sector Motor Gasoline Consumed by Transportation Sector Residual Fuel Oil Consumed by Commercial Sector Residual Fuel Oil Consumed by Industrial Sector Residual Fuel Oil Consumed by Transportation Sector Residual Fuel Oil Consumed by Electric Power Sector Twenty-four series analysed using Equation (3) Distillate Fuel Oil Consumed by Residential Sector Liquefied Petroleum Gases Consumed by Residential Sector Distillate Fuel Oil Consumed by Commercial Sector ** * Liquefied Petroleum Gases Consumed by Commercial Sector Motor Gasoline Consumed by Commercial Sector ** * Residual Fuel Oil Consumed by Commercial Sector Asphalt and Road Oil Consumed by Industrial Sector

20 Distillate Fuel Oil Consumed by Industrial Sector Liquefied Petroleum Gases Consumed by Industrial Sector Lubricants Consumed by Industrial Sector Motor Gasoline Consumed by Industrial Sector Petroleum Coke Consumed by Industrial Sector Residual Fuel Oil Consumed by Industrial Sector Other Petroleum Products Consumed by Industrial Sector Aviation Gasoline Consumed by Transportation Sector Distillate Fuel Oil Consumed by Transportation Sector Jet Fuel Consumed by Transportation Sector * Liquefied Petroleum Gases Consumed by Transportation Sector Lubricants Consumed by Transportation Sector * Motor Gasoline Consumed by Transportation Sector Residual Fuel Oil Consumed by Transportation Sector Distillate Fuel Oil Consumed by Electric Power Sector Petroleum Coke Consumed by Electric Power Sector * Residual Fuel Oil Consumed by Electric Power Sector Notes: M1 is Narayan and Popp s Model 1. M2 is Narayan and Popp s Model 2. TB is the date of the structural break; K is the lag length; * denotes statistical significance at the 10% level respectively.

21 Table 4 Narayan and Liu (2013) GARCH unit root test with two structural breaks in intercept Series Test Statistics TB1 TB2 Series analysed using Equation (1) Total Petroleum Consumed by Residential Sector ** Total Petroleum Consumed by Commercial Sector ** Total Petroleum Consumed by Transportation Sector ** Total Petroleum Consumed by Electric Power Sector ** Series analysed using Equation (2) Distillate Fuel Oil Consumed by Residential Sector ** Distillate Fuel Oil Consumed by Commercial Sector ** Distillate Fuel Oil Consumed by Transportation Sector ** Residual Fuel Oil Consumed by Commercial Sector ** Residual Fuel Oil Consumed by Industrial Sector ** Residual Fuel Oil Consumed by Transportation Sector ** Residual Fuel Oil Consumed by Electric Power Sector ** Series analysed using Equation (3) Distillate Fuel Oil Consumed by Residential Sector ** Liquefied Petroleum Gases Consumed by Residential Sector ** Distillate Fuel Oil Consumed by Commercial Sector ** Liquefied Petroleum Gases Consumed by Commercial Sector Asphalt and Road Oil Consumed by Industrial Sector ** Liquefied Petroleum Gases Consumed by Industrial Sector ** Residual Fuel Oil Consumed by Industrial Sector Other Petroleum Products Consumed by Industrial Sector ** Aviation Gasoline Consumed by Transportation Sector ** Distillate Fuel Oil Consumed by Transportation Sector ** Jet Fuel Consumed by Transportation Sector ** Lubricants Consumed by Transportation Sector ** Motor Gasoline Consumed by Transportation Sector ** Residual Fuel Oil Consumed by Transportation Sector ** Petroleum Coke Consumed by Electric Power Sector Residual Fuel Oil Consumed by Electric Power Sector Notes: The test was performed under the assumption of two breaks in intercept and slope of the series. The 5% critical for the unit root test statistics are obtained from Narayan and Liu (2013). Narayan and Liu (2013) provide critical values for 5% level of significance only. ** indicates rejection of null of unit root at 5% level of significance.

22 Figure 1 Time series plots for total petroleum consumption in each sector 4.8 COM 5.5 ELP IND 4.0 RES