Oil Prices and Personal Consumption Expenditures: Does the. Source of the Shock Matter?

Transcription

1 Oil Prices and Personal Consumption Expenditures: Does the Source of the Shock Matter? Zeina N. Alsalman Mohamad B. Karaki Abstract This paper studies the effect of structural oil shocks on personal consumption expenditures (PCE). First, we estimate a nonlinear simultaneous equation model and compute impulse responses by Monte Carlo integration. We find that aggregate PCE responds negatively to positive oil demand shocks, while adverse oil supply shocks are of limited effect. At the industry level, we find important heterogeneity in the magnitude, sign and timing of the PCE responses to oil price innovations caused by different origins. Moreover, unlike other shocks in the crude oil market, we find evidence that households switch to vehicles with higher fuel effi ciency following a positive oil-specific demand shock. Our findings are robust to different nonlinear transformations for the real price of oil. Finally, we use a test of symmetry to evaluate whether PCE responds asymmetrically to positive and negative oil price innovations. Evidence of asymmetry vanishes when we use data mining robust critical values. Keywords: oil prices; oil supply shocks, oil demand shocks, consumption; asymmetry. JEL Classification: E21, Q43. Department of Economics, 332D Elliott Hall, School of Business Administration, Oakland University, Rochester, MI 48309; alsalman@oakland.edu. Department of Economics, School of Business Administration, Lebanese American University, 1515 Adnan Kassar School of Business, Beirut, Lebanon; mkaraki@lau.edu.lb. We are thankful to Ana Maria Herrera and Lutz Kilian for helpful comments and suggestions. The online appendix is available at 1

2 1 Introduction Following the stagflations that occurred in the 1970s, most economists viewed oil price shocks as supply driven. Yet recently, most policymakers and researchers agree that oil price shocks are demand driven. Work by Hamilton (2008), Edelstein and Kilian (2009), Baumeister and Kilian (2016b) and Baumeister, Kilian and Zhou (2017) show that oil prices primarily affect the economy through changes in spending by domestic households and firms due to changes in discretionary income. For instance, Baumeister and Kilian (2016b) underscore that the reduction in oil prices since mid-2014 has increased households purchasing power and consumer spending 1. How responsive is consumption expenditure to oil price shocks? Does the composition of consumption spending change following a change in oil prices? Does the source behind the oil price change matter? Does consumer spending respond asymmetrically to oil price increases and decreases? Given that oil prices primarily operate through changes in consumer spending, and because U.S. consumption expenditure constitutes 70% of U.S. GDP, answers to these questions are urgently important especially given the large recent fluctuations in oil prices. This study investigates the effect of deep structural shocks in the crude oil market on real personal consumption expenditures (PCE). Previous studies by Edelstein and Kilian (2009) and Wang (2013) studied the response of consumption expenditure to an oil price increase that comprises a combination of supply-driven and demand-driven shocks in the crude oil market. This paper separates the effect of deep structural supply shocks and demand shocks in the crude oil market on PCE. In theory, many channels can explain the transmission mechanism of oil price shocks on con- 1 Note that policymakers agree with Baumeister and Kilian (2016b) and Baumeister, Kilian and Zhou (2017) that higher oil prices can damage economic growth and that lower oil prices can stimulate consumption expenditure (see Bernanke, 2006; Yellen 2016). 2

3 sumer expenditure. First, an unexpected increase in the real price of oil reduces discretionary income as gasoline prices rise, which lowers consumption expenditure 2. Second, a sudden increase in oil prices will trigger uncertainty regarding the real price of oil in the future. Consequently, consumers will delay their spending on irreversible purchases such as durable goods. The uncertainty channel associated with oil price shocks can also affect consumption expenditure on nondurable goods, as consumers start to save more as a precaution for future economic turmoil (see Bernanke, 1983; Pindyck, 1991). Third, the operating-cost channel entails that an unexpected increase in the real price of oil will pull consumers away from durable goods that rely on energy such as motor vehicles (see Hamilton, 1988). Finally, the increase in uncertainty associated with an unexpected change in the real price of oil can trigger sectoral reallocative effects that have strong damaging effect on the economy. For instance following an increase in the price of oil, labor and capital will be reallocated away from the auto industry as households reduce their spending on energy intensive goods, such as automobiles. This reallocation effect could create strong changes in the structure of industries in the economy. Furthermore, as oil prices increase, consumers might shift to energy effi cient cars and pull away from large gas guzzling SUVs, which will create important reallocation frictions within the auto industry (see Davis and Haltiwanger, 2001; Herrera and Karaki, 2015). These reallocation effects within and across industries could increase unemployment, which will reduce consumption expenditure further. Note that unlike other channels, this reallocation caused by the uncertainty associated with oil price disruptions can have an asymmetric effect on consumption expenditure. In other words, if oil price shocks mainly affect consumption expenditure through the reallocative channel, then consumption expenditure will respond asymmetrically to positive and negative oil price innovations. 2 see Edelstein and Kilian (2009), Kilian (2014), Baumeister and Kilian (2016a), Hamilton (2009, 2013). 3

4 In this paper, we use a nonlinear simultaneous equation model to study the effect of oil price innovations on PCE. We decompose oil price innovations into world supply shocks, aggregate demand shocks, and oil-specific demand shocks. Our benchmark model specification is based on the oil price increase measure (see Mork, 1989). This nonlinear transformation in the real price of oil, which censors for the monthly decreases in the oil price change, is closely linked to theoretical models that imply asymmetry in the response of economic activity to oil price increases and decreases. To evaluate whether additional nonlinearities improve the predictive fit between oil prices and PCE, we incorporate an alternative model specification based on the 1-year net oil price increase measure (see Hamilton, 1996). This nonlinear transformation in the real price of oil is purely motivated by behavioral arguments and is not related to theoretical model on oil price transmission to the economy (see Kilian and Vigfusson, 2017). We first use our benchmark model to study the effect of structural shocks in the crude oil market on aggregate PCE. We find that an adverse oil supply shock has no significant effect on aggregate consumption expenditure. This result can possibly be explained by the limited effect that world oil supply shocks have on gasoline prices. We also find that a positive aggregate demand shock leads to a significantly negative effect on aggregate personal consumption expenditure a year after the shock 3. Note that aggregate demand shocks operate through stimulating and decelerating channels. By the stimulating channel, we refer to the increase in U.S. real GDP associated with the increase in global real economic activity. By the decelerating channel, we refer to the increase in oil prices triggered by positive aggregate demand shocks, which negatively affects consumption expenditure. Our results clearly show that the decelerating transmission channel dominates the stimulating channel for aggregate PCE. Moreover, we find that an unexpected positive oil-specific 3 This finding is in line with Kilian (2009), who found that a positive aggregate demand shock has a significantly negative effect on real GDP, 1 year after the shock. 4

5 demand shock has a significantly negative effect on aggregate consumption expenditure, which implies that households reduce their consumption spending as a precaution when they are worried about future oil supply disruptions. Second, we evaluate the effect of oil price innovations on PCE for disaggregated industries. We find important heterogeneity in the responses of industry-level PCE to an adverse supply shock. For instance, we find that the effect is significantly negative for goods and significantly positive for services. In addition, we find that an adverse oil supply shock triggers larger changes in durable goods than nondurable goods. Following a positive aggregate demand shock, we also find ample evidence that the effect is significant for goods but not for services. Yet unlike supply shocks, we find that for most sectors, aggregate demand shocks significantly affect industry-level PCE with a delay of at least 9 months. Interestingly, we find that several sectors respond positively to a positive aggregate demand shock such as motor vehicles parts and accessories, housing and recreation services. This result clearly indicates the limitation of studies that focus on the average response of an economic variable to an oil price shock that constitutes a combination of structural shocks in the crude oil market. In other words, our findings reveal that for many sectors, the response of PCE to an oil price shock due to a disruption in the world oil supply differs from the response of PCE to an oil price shock due to unexpected changes in global real economic activity. We also find that the responses of PCE to an oil-specific demand shock strongly defer vis à vis the responses of PCE to aggregate demand and world oil supply shocks. For instance, we find that the effect is significantly negative for most industries including goods and durable goods. Moreover, compared to other shocks in the crude oil market, our results reveal that PCE for the motor vehicles industry is largely damaged following an oil-specific demand shock 4. A closer look within the motor 4 Note that a positive oil-specific demand shock has a strong positive effect on the real price of oil on impact (see Kilian, 2009). Our findings are therefore in line with Ramey and Vine (2010) who found the motor vehicles industry to be highly sensitive to oil price shocks. 5

6 vehicles industry reveals that an unexpected positive oil-specific demand shock leads to a significant decrease in PCE for new motor vehicles and new light trucks. Moreover, while Edelstein and Kilian (2009) found that an oil price increase has no significant effect on PCE for new foreign autos, our results reveal that a positive oil-specific demand shock triggers a significant increase in new foreign autos. These differences in the responses of PCE within the motor vehicles industry imply that households shift towards more fuel effi cient cars and pull away from automobiles that have low fuel effi ciency when they are worried about disruptions in the future oil supply. We then use our alternative model specification, where we include the 1-year net oil price increase measure (see Hamilton, 1996) to inquire on the effect of oil price innovations on PCE. We find few differences in the responses compared to what we obtained based from our benchmark model. For instance, using the net oil price increase measure, we find that a world oil supply shock has a significant effect on aggregate personal consumption expenditure. Yet, for most industries our results indicate that the effect of structural shocks in the crude oil market on PCE is less significant compared to that obtained from our benchmark model specification. These findings indicate that additional nonlinearities are not helpful in explaining the changes in personal consumption expenditures to oil price innovations. Finally, we evaluate whether the response of PCE is asymmetric to positive and negative oil price innovations. We follow Kilian and Vigfusson (2011a) and conduct a test of symmetry on the impulse responses. Using the oil price increase measure (see Mork, 1989), we find evidence of asymmetry in the responses of personal consumption expenditure to positive and negative oil price innovations of 1 standard deviation. We find no evidence against the null of symmetry for 1 s.d. shock using the net oil price increase measure (see Hamilton, 1996). For a 2 standard deviation shock, we find more evidence against the null of symmetry regardless of the nonlinear 6

7 transformation of oil prices. Yet, after we account for the repeated application of the test across sectors, by computing data mining robust critical values, evidence against the null of symmetry vanishes 5. The paper is structured as follows. Section 2 presents the data. Our modelling strategy is discussed in section 3. The empirical results for the benchmark model are discussed in section 4. Our findings based on the net oil price increase model are presented in section 5. In section 6 we present a test of symmetry to evaluate whether the responses of personal consumption expenditure to positive and negative oil price shocks are symmetric. Section 7 concludes. 2 Data In this paper, we use data on world oil supply from the Energy Information administration (EIA). We take the log difference of world oil supply to obtain the growth rate. We also obtain the price of oil from the EIA. We use the refiner acquisition cost of imported crude oil deflated by the U.S. CPI to obtain the real oil price. As a measure for global economic activity, we use the index of real economic activity constructed by Kilian (2009). The global economic activity index captures changes in the demand for industrial commodities in global markets using dry cargo single voyage ocean freight rates. The index is computed based on monthly data collected by Drewry Shipping Consultants Ltd. for various bulk dry cargoes including grain, oilseeds, coal, iron ore, fertilizer and scrap metal. We use BEA s National Income and Product Accounts monthly data on real personal consumption expenditures (PCE) at the aggregate and industry levels. Our disaggregated data by product type comprises 46 sectors including durable goods, nondurable goods and services. Our 5 These findings are consistent with Kilian and Vigfusson (2011a), Herrera, Lagalo and Wada (2011), Alsalman and Herrera (2015) and Herrera and Karaki (2015) who found no evidence against the null of symmetry in the response of GDP, unemployment, aggregate industrial production, total stock returns and total manufacturing job flows to positive and negative oil price innovations. 7

8 data is monthly and the sample spans the 1973 M2 to 2015 M12 period. To investigate the asymmetric transmission channels of oil price shocks, we use two different nonlinear transformations for the real price of oil. The first measure is the one month oil price increase. This measure censors any negative growth rate in the real oil price: x 1 t = max {0, ln (o t ) ln (o t 1 )}. (1) The second measure is the net oil price increase over the previous 12-months maximum (see, Hamilton, 1996). This measure is expressed as follows: x 12 t = max {0, ln (o t ) max {0, ln (o t 1 ),..., ln (o t 12 )}}. (2) note that x 12 t refers to a measure that is based on behavioral arguments and is well-known to capture the nonlinear relationship between oil prices and the economy. 3 Modeling strategy We study the effect of shocks in the crude oil market on U.S. consumption expenditure using the following simultaneous equation model: s t = a 10 + d t = a 20 + x t = a 30 + c S,t = a 40 + a 11,i s t i + a 12,i d t i + i=1 a 21,i s t i + i=0 a 31,i s t i + i=0 a 41,i s t i + i=1 a 22,i d t i + i=1 a 32,i d t i + i=0 a 42,i d t i + a 13,i x t i + a 14,i c S,t i + ε 1,t i=1 a 23,i x t i + i=1 a 33,i x t i + i=1 i=0 i=0 i=0 a 43,i x t i + 8 i=1 a 24,i c S,t i + ε 2,t i=1 a 34,i c S,t i + ε 3,t i=1 a 44,i c S,t i + i=1 i=0 k 41,i x # t i + ε 4,t (3a) (3b) (3c) (3d)

9 where s t refers to the percentage change in the world oil supply, d t refers to the aggregate economic activity index developed by Kilian (2009), x t refers to the percentage change in the real price of oil, x # t denotes one of the nonlinear measures in oil prices discussed in the section 2 (x 1 t, x 12 t ), and c S,t refers to the growth rate in consumer expenditure in industry S. The model is estimated using twelve monthly lags 6. In terms of identification restrictions, we follow Kilian (2009) and assume that the supply curve in the crude oil market is vertical. This restriction stems from the fact that world oil supply does not respond to aggregate demand shocks and oil specific demand shocks in the same month because of the high adjustment costs for oil producing countries. We also assume that aggregate demand does not respond contemporaneously to the percentage change in the real price of oil and consumption expenditure. This restriction is plausible given the fact that aggregate economic activity does not respond within a month to changes in oil prices. The third equation in our model refers to the changes in the growth rate in oil prices that are neither explained by supply shocks nor by aggregate demand shocks. Specifically, equation 3c represents the changes in oil demand as a precaution of future oil supply disruptions. We assume that x t does not respond contemporaneously to changes in U.S. consumption expenditure. The model is effi ciently estimated by OLS equation by equation. Note that, our identification restrictions are in line with Kilian (2009). Note also that our empirical model differs from the symmetric structural VAR used in Edelstein and Kilian (2009). Precisely, the presence of a nonlinear transformation in the real price of oil in our model, allows us to nest both symmetric and asymmetric responses in consumer expenditure to oil price innovations that are driven by oil supply disruptions, fluctuations in aggregate economic activity and oil specific demand shocks. Given that our model is nonlinear in x t we compute impulse response functions by Monte Carlo integration, which account for the 6 We follow Herrera, Lagalo and Wada (2011), Alsalman and Herrera (2015), Herrera and Karaki (2015) and Karaki (2017), who estimate a nonlinear model using 12 monthly lags to study the macroeconomic effects of oil price shocks. 9

10 history and the size of the shock. 4 The response of U.S. Real PCE to Structural Crude Oil Shocks Figure 1 illustrates the cumulative impulse responses for aggregate PCE and selected industry-level PCE to an oil supply shock, aggregate demand shock and oil-specific demand shock of 1 standard deviation 7. Our results are based on the nonlinear transformation of Mork s (1989) modified version of oil price increase, x 1 t. Squares and diamonds denote statistical significance at the 5% and 10% levels, respectively. 4.1 The response of aggregate PCE The first panel of figure 1 reveals that an adverse oil supply shock has no significant effect on aggregate PCE across all horizons. This finding can possibly be explained by the fact that shortfalls in oil production are of limited significance in explaining changes in real oil prices and gasoline prices (see Kilian, 2008, 2009 and Kilian and Park, 2009). Work by Kilian (2009) and Kilian and Park (2009) shows that aggregate demand shocks have both stimulating and decelerating effects on the U.S. economy. The stimulating effect mainly comes from the increase in real GDP caused by the increase in world economic activity, whereas the decelerating effect refers to the slowdown in U.S. real GDP due to the increase in oil prices caused by a boom in global economic activity. The first row-second column of figure 1 reveals that an unanticipated increase in aggregate demand generates a persistent and statistically significant decrease in cumulative aggregate PCE for h > 11. This decline in aggregate PCE occurs because the indirect decelerating effect associated with aggregate demand shocks dominates the direct stimulating 7 Figure A.1 of the online appendix displays the responses of PCE for all disaggregated industries. 10

11 effect. The first row of figure 1 shows that a positive oil specific demand shock significantly drops aggregate PCE five months after the shock. This reduction in PCE becomes persistently significant for h > 11. This result is in line with Kilian (2008) and Baumeister and Kilian (2016b) who found that shocks to precautionary demand in the crude oil market lead to an instant increase in both crude oil and gasoline prices, which negatively affect consumers discretionary income and lower consumption expenditure. 4.2 The response of industry-level PCE supply shocks While supply shocks have largely insignificant effects on aggregate PCE, the response of PCE to an adverse supply shock greatly differs across industries. For instance, while the cumulative response of PCE on goods shows a statistically significant decline shortly after a negative oil supply shock, the cumulative response of PCE on services shows a significant increase immediately after the same shock. We also find that the effect of an adverse supply shock on PCE importantly differs within the durable goods sector. For instance, the 1-year cumulative response of PCE is percentage points for household appliances, and percentage points for pleasure aircraft. Moreover, we find that oil supply shocks have a very limited effect on the nondurable goods sector. The response of PCE for nondurables tends to be short-lived and largely insignificant. Note also that the 1-year cumulative response for nondurables is only percentage points. Digging into disaggregated industries within the nondurables sector, we find that the response for food and beverages is largely insignificant whereas the responses of clothing and footwear and gas and other energy goods are short-lived and negative. For instance, the 1-year cumulative response for gasoline and other energy goods is -0.1 percentage points. 11

12 Overall our results reveal that the decline in durable goods consumption is larger than that of non-durables. In other words, the one-year cumulative decline in durable goods is 0.17 percentage points whereas that of non-durable goods is only 0.01 percentage points (see table 1). These findings imply that consumers postpone their expenditures on big-ticket items shortly after an increase in oil prices arising from a negative supply shock. Last but not least, the first column of figure 1 reveals that the response of PCE for services is positive for h < 3 but the effect becomes insignificant for the remaining horizons. Interestingly, we also find that the effect on transportation services is largely insignificant; however, a closer look within this sector reveals that oil supply shocks have a significantly negative effect on both ground transportation and air transportation. For instance, the 1-year cumulative response of PCE for ground transportation is percentage points. While the response of aggregate PCE to oil supply shocks is insignificant, our results reflect that an analysis that solely focus on the aggregate level can mask economically significant effects that oil supply shocks have on industry-level PCE aggregate demand shocks The second column of figure 1 portrays the cumulative responses of PCE to a positive aggregate demand shock. The impulse response functions depict important heterogeneity in the responses of PCE across sectors. For instance, the response of PCE for durable goods is larger than the response of PCE for nondurables and services. Table 1 shows that the one-year cumulative response for durable goods is percentage point, while that for nondurables is only -0.1 percentage points. Moreover, goods and services vary in their responses to positive aggregate demand shocks. While a positive aggregate demand shock triggers a significant and persistent decrease of 0.22 percentage points on the cumulative response of goods a year after the shock, the response of services is largely insignificant. Consequently, these findings indicate that the delayed and persistent decline in the 12

13 cumulative aggregate PCE is mainly driven by the cumulative decline in goods and not in services. Interestingly, we find that for most sectors the response of PCE to a positive aggregate demand shock is significantly negative for h > 9. This delay in the response of PCE to aggregate demand shocks is consistent with Kilian (2009) who finds that an unanticipated increase in global demand for industrial commodities would gradually increase the real price of oil and negatively affect the economy. It is important to note also that even though most of the goods sectors respond negatively to an aggregate demand shock mainly with a delay, some sectors such as motor vehicle parts and accessories, other nondurable goods respond positively, within a year after the shock. Moreover, while the impulse response functions in figure 1 reveal that a positive aggregate demand shock has no effect on PCE for services, we find that disaggregated industries within the services sector such as housing and recreation services experience an increase in PCE. Our results differ from previous work by Edelstein and Kilian (2009) who do not disentangle deep structural shocks within the crude oil market, but instead, focus on the average response of consumption to an oil price shock. Our findings reveal that the sign and the magnitude for the response of aggregate and disaggregated PCE to oil price innovations heavily depend on the source of the oil price shock oil-specific demand shocks The effect of oil-specific demand shock on PCE varies greatly across sectors (see the third column of figure 1). The impulse response functions reveal that for some sectors the effect is significantly negative for most horizons such as goods, new light trucks and gas and other energy goods. For other sectors, we find that the effect of oil-specific demand shocks is significantly positive for only few months after the shock such as household appliances and food and beverages. Note however 13

14 that for more than half of the sectors, the effect is significantly negative for h > 10. Similar to oil supply shocks and aggregate demand shocks, an oil specific demand shock triggers a larger 1-year cumulative change in durables (0.46 percentage points) than nondurables (-0.16 percentage points). Note that higher uncertainty about the price of oil may cause consumers to delay their purchases of energy-intensive durables such as motor vehicles (see Edelstein and Kilian, 2009; Baumeister and Kilian, 2016b). Overall, our results clearly reveal that disentangling supply and demand shocks is key for adequately studying the effect of oil price innovations on PCE. While our paper uses a different modeling strategy than Edelstein and Kilian (2009), it is interesting to compare our results with theirs. Edelstein and Kilian (2009), using linear models, quantified the response of real consumption to unanticipated purchasing power losses driven by unexpected changes in the real price of oil. While their results reveal that consumption is negatively affected following an unexpected increase in oil prices, our results point that the response of PCE to an oil price shock heavily depends on the source of the oil price fluctuation. Specifically, we find that the effect of oil supply shocks on aggregate and disaggregated PCE is largely insignificant. In addition, we find that aggregate demand shocks affect PCE with a delay of around 9 months. Moreover, our results reveal that a positive aggregate demand shock reduces PCE for some sectors, and increases PCE for others. We also found that an oil specific-demand shock has a significantly negative effect on aggregate PCE. Unlike aggregate demand shocks, oil-specific demand shocks trigger faster changes in PCE. 14

15 4.3 Motor vehicle consumption and the switch to energy-effi cient vehicles Since the 70s, a large body of literature has examined the impact of innovations in oil and/or retail gasoline prices on motor vehicles consumption 8. Ramey and Vine (2010), for instance, show that shocks in oil prices have a direct impact on consumer expenditures of motor vehicles. In addition, Davis and Haltiwanger (2001) and Wei (2009) claim that an increase in oil prices will induce households to switch from large SUVs to fuel effi cient cars. Figure 1 reveals that following an adverse supply shock, spending on motor vehicles significantly falls at low horizons and then the effect become insignificant. Following an aggregate demand shock, motor vehicle consumption significantly falls for (h > 9). This delay is possibly attributed to the decelerating effect associated with aggregate demand shocks (see Kilian, 2009). Similarly, we find that a positive oil-specific demand shock leads to a significant decrease in PCE for new motor vehicles. Note though that the motor vehicles industries tend to respond to an oil-specific demand shock faster than to an aggregate demand shock. The reason behind the discrepancy in the response of PCE to different demand shocks in the crude oil market is because an oil specific demand shock leads a persistent and immediate increase in the real price of oil. Compared to an oil-specific demand shock, however, an aggregate demand shock triggers a smaller increase in the real price of oil that occurs after a delay (see Kilian, 2009; Kilian and Park, 2009). Let us now look at the disaggregated industries within the motor vehicle industry. Following an oil supply shock, our results indicate that new autos, domestic autos and foreign autos are negatively affected whereas new light trucks and motor vehicles parts and accessories are largely unaffected. On one hand we find that an aggregate demand shock triggers a significantly negative effect on new autos, new domestic autos and new light trucks. For instance, the 1-year cumulative 8 (see, among others, Baumeister and Kilian 2016b; Ramey and Vine 2010; Edelstein and Kilian 2009, Wei 2009, Hughes, Knittel and Sperling 2008; Bresnahan and Ramey 1993; Dahl 1979). 15

16 response of PCE for new light trucks is percentage points (see table 1). On another hand, figure 1 reveals that a positive aggregate demand shock significantly increases PCE for motor vehicles parts and accessories. Interestingly, we find that spending on cars is highly affected with oil-specific demand shocks. For instance, we find that a positive oil-specific demand shock leads to a significant increase in PCE for new foreign autos at low horizons, then the responses of PCE for these industries significantly fall 10 months after the shock. Moreover, we find that an oil-specific demand shock leads to a significantly negative effect on PCE for new domestic autos and light trucks that last for several years. These results reveal that households switch from light trucks to fuel effi cient foreign cars when they are uncertain about future oil supply disruptions. These findings are in line with Baumeister and Kilian (2016b), who claim that expectations about future gasoline prices play a crucial role in households decision to buy new vehicles. Our results are in line with media analysts who claimed that the surge in oil prices throughout the first seven months of 2008 lead to an increase in subcompact cars sales such as GM s Cheverolet Aveo by 39% whereas the overall auto industry sales fell by 11% (see Mitchell, 2008). Our results imply that the response of PCE within the motor vehicles industry to structural shocks in the crude oil market depends on vehicles fuel effi ciency level Empirical evidence from Hamilton s 1-year net oil price increase measure In this section we use an alternative model specification that is based on the 1-year net oil price increase nonlinear transformation for the real price of oil (x 12 t ) proposed by Hamilton (1996). Figure A.2 of the online appendix displays the impulse responses of aggregate and disaggregated PCE to 9 See Busse, Knittel and Zettelmeyer (2013) for a discussion on the role of fuel effi ciency in affecting spending on different types of motor vehicles. 16

17 different structural shocks in the crude oil market 10. Following an oil supply shock, we find that the responses of aggregate and disaggregated PCE based on our alternative model specification are highly consistent with the responses based on our benchmark model. In other words, regardless of the model specification, oil supply shocks have a limited effect on PCE. The responses of PCE to aggregate demand shocks are also highly consistent across both model specifications. Thus both model specifications imply that when faced with an aggregate demand shock, consumers will reduce their spending on energy intensive durable goods and increase their spending for few nondurables goods and services industries. An important difference across both model specifications is in the responses of PCE to oilspecific demand shocks. At the aggregate level, results based on the x 12 t measure reveals that oil-specific demand shocks have no significant effect on aggregate PCE. This result is at odds with what we found based on our benchmark model where aggregate PCE significantly falls following a positive oil-specific demand shock. At the disaggregated level, we find the responses of PCE are very similar for both models. For instance, regardless of the nonlinear measure for the real price of oil, we find that consumers switch to fuel-effi cient vehicles when hit with a positive precautionary oil demand shock. Moreover, in line with the results based on our benchmark model, we find that oil-specific demand shocks have a limited effect on services using x 12 t. In brief, our results indicate that the responses of PCE to supply shocks, aggregate demand shocks and oil-specific demand shocks are robust for both model specifications. Yet, one main difference is the effect of oil-specific demand shock on aggregate PCE, where the results based on the x 12 t (x 1 t ) indicate that the effect is largely insignificant (significant). These findings contradict Mehra and Petersen (2005) who found that the negative effect of an oil price increase on consumer 10 Table A.1 displays the 1-cumulative responses of aggregate and disaggregated PCE to structural shocks in the crude oil market using the x 12 t nonlinear transformation of the real price of oil. 17

18 spending is more important when the oil price increase is measured as net increases The responses of U.S. PCE to positive and negative oil price innovations Do positive innovations in oil prices have larger effect than negative innovations on the US PCE? To answer this question, we focus on our benchmark model specification, which is more tightly linked to theoretical channels asymmetry than our alternative model specification. Our results in Table 2 reports the 1-year and 2-year cumulative difference in absolute terms of the impact of 1 s.d. positive and negative oil price innovations on PCE. At the aggregate level, we find little evidence for asymmetry, where positive and negative innovations in the real price of oil trigger a 1-year (2-year) cumulative difference of 0.04 (0.04) percentage points. Compared to aggregate PCE, differences in the cumulative responses of PCE to positive and negative oil price innovations are larger (smaller) for durable (nondurable) goods. Table 2 reveals that the 1-year (2-year) cumulative difference in PCE to positive and negative oil price innovations is 0.12 (0.13) percentage points for durables and 0.03 (0.03) percentage points for services. The asymmetry in the responses of PCE for durables to positive and negative oil price innovations could be attributed to the costly resource reallocation channel (see Davis and Haltiwanger, 2001; Herrera and Karaki, 2015; Baumeister and Kilian 2016b). For many other industries, such as food and beverages purchased for off-premises consumption, housing, ground transportation, and financial services and insurance, the effect of 11 Note that Mehra and Paterson (2005) use a single equation model that includes a nonlinear measure for the nominal price of oil to study the predictive relationship between oil prices and consumer spending. They use data that spans the 1962 to 2004 period. In contrast with our results, they find that, compared to the oil price increase measure, the effect of oil prices on consumer spending is more important when the 1-year net oil price increase measure is used (see Hamilton, 1996). We argue that their results differ from ours for the following reasons. First, their model does not account for structural supply and demand shocks in the crude oil market. Second, as explained by Kilian and Vigfusson (2011b), it s inappropriate to use nominal oil price data from the pre-1973 period where the data is discrete and combine it with nominal oil price data from the post-1973 period where the data is continuous. 18

19 positive and negative oil price innovations on PCE appears to be symmetric. To evaluate whether aggregate and disaggregated PCE respond symmetrically to positive and negative oil price innovations, we follow the recent literature and compute an impulse response function based test of symmetry (see Kilian and Vigfusson, 2011a; Herrera, Lagolo and Wada, 2011, 2015; Alsalman and Herrera, 2015; Herrera and Karaki, 2015). The null hypothesis can be written as follows: H o : I P CE (h, δ) = I P CE (h, δ) for h = 0, 1, 2,..., H. where δ represents the size of the shock and I P CE refers to the unconditional response of PCE to an oil price shock. We focus on the one-year horizon (h = 1, 2..., 12) to reduce the data mining problem associated with repeating the test across different horizons and because the largest effect of oil price shocks on the economy appear to happen 1 year after the shock (see Davis and Haltiwanger, 2001; Herrera and Karaki, 2015). The left panel of Table 3 presents the p-values of the test of symmetry for the one-year response of PCE to positive and negative oil price innovations of 1 s.d. We are unable to reject the null of symmetry at the 5% level for aggregate PCE. Our results are in line with Edelstein and Kilian (2009) who found no evidence of asymmetry in aggregate consumer expenditures. At the industry level, we find evidence of asymmetry for goods, durable, and motor vehicle parts and accessories at the 10% level using Mork s specification. We find more evidence of asymmetry when using a large 2 s.d. shock where the null of symmetry is rejected for aggregate and several disaggregated PCE (see table 3). Given that the p values reported in table 3 are based on conventional critical values, which do not account for the repeated application of the test across 47 different sectors. To account for 19

20 this data mining problem, we follow Herrera, Lagalo and Wada (2011) and compute data mining robust critical values by simulating the null distribution of the supremum of the bootstrap tests statistic across all the sectors. Our results based on data mining robust critical values indicate that the null of symmetry cannot be rejected for any sector Conclusion This paper studied the effect of oil price innovations on personal consumption expenditure. We used a nonlinear structural equation model that nests both symmetric and asymmetric responses to oil price innovations. Unlike previous studies that used a nonlinear model to study the effect of positive and negative oil price innovations on economic activity, our study used a model that decompose structural shocks in the crude oil market. Such decomposition is important because work by Kilian (2009) and Kilian and Park (2009) found that supply shocks in the crude oil market differ from demand shocks in their effects on GDP, inflation and the stock market. Our benchmark model is based on the oil price increase measure, which censors the decreases in the monthly changes in the real price of crude oil. We estimated the model by OLS and computed impulse response functions by Monte Carlo integration for aggregate and disaggregated personal consumption expenditures. Focusing first on aggregate consumption, we found that an adverse shock in world oil supply has no significant effect. Both aggregate demand shocks and oil-specific demand shocks trigger a significantly negative effect on aggregate PCE. Note though that aggregate PCE responds more quickly to oil-specific demand shocks than to aggregate demand shocks. 12 Note that our results are consistent with the previous literature. For instance, Kilian and Vigfusson (2011a), Herrera et al (2011, 2015), Alsalman and Herrera (2015) and Herrera and Karaki (2015) found no evidence of asymmetry, at the aggregate level, in the response of GDP, industrial production, stock returns and job flows, respectively. Note also that our results are robust for different nonlinear transformations of the real price of oil. The second panel of table (where x # t = x 12 t ) reveals that the null of symmetry cannot be rejected after accounting for data mining. 20

21 At the disaggregated level, we found important differences in the responses of industry-level PCE to structural shocks in the crude oil market. On one hand, an adverse world supply shock has a negative effect on goods and a positive effect on services. On another hand, a positive aggregate demand shock triggers an important increase in PCE for goods but has no significant effect on services. In addition, the peak effect of aggregate demand shocks on disaggregated industries occurs 9 months after the shock for most industries. This delay in the response of PCE can possibly be attributed to the fact that the stimulating effect of aggregate demand shocks on PCE is weak and within a year, the decelerating effect dominates, which leads to a significant reduction in PCE. Interestingly, we found that some industries respond positively to positive aggregate demand shocks. Among these sectors is the motor vehicles and parts industry. The effect of oil-specific demand shocks on industry level PCE also differs importantly from the two other shocks. Our results reveal that the effect is significantly negative for more than half of the industries. For few industries, such as household and appliances, we find that the effect of oil-specific demand shock is significantly positive. Overall, we find that an oil-specific demand shock has a stronger effect on disaggregated PCE than supply shocks and aggregate demand shocks. We evaluate the robustness of our results by using an alternative model specification where we incorporate the 1-year net oil price increase measure (see Hamilton,1996). Our results indicate that the responses of PCE to structural shocks in the crude oil market are less significant and smaller in magnitude. In contrast with studies that do not disentangle the effect of supply shocks and demand shocks in the crude oil markets, our results indicate that the net oil price increase measure has a lower explanatory power than the oil price increase measure. Then we evaluate whether the response of PCE to positive and negative oil price innovations is asymmetric. We followed Kilian and Vigfusson (2011a) and implemented a formal test of symmetry 21

22 on the impulse response functions. We find very little evidence of asymmetry for a typical positive oil price shock of 1 s.d. We find more evidence against the null of symmetry for a large 2 s.d. shock. Yet, evidence of asymmetry vanishes when we use data mining robust critical values. 22

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