Demand and Supply Elasticities and Oil Price Fluctuations: A VAR Approach

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1 Demand and Supply Elasticities and Oil Price Fluctuations: A VAR Approach Dario Caldara Michele Cavallo Matteo Iacoviello February 2, 26 Abstract This paper studies the identification of oil shocks in structural vector autoregressive models (SVAR). First, we show that the cross-equation restrictions of a SVARs impose a nonlinear relation between the short-run price elasticities of oil demand and oil supply. This relation implies that seemingly plausible restrictions on oil supply elasticity may map into implausible values of the oil demand elasticity, and viceversa. The selection of these elasticities is consequential for inference, in particular for the multipliers of oil prices on economic activity. Second, we propose an identification scheme that minimizes the distance between the SVAR-implied elasticities and targets constructed by a meta-analysis of relevant studies. Third, we use the identified SVAR to analyze sources and consequences of movements in oil prices. We find that (i) oil supply shocks and oil-specific demand shocks are, on average, equal drivers of oil price fluctuations, while shocks to global demand play a minor role; (ii) a drop in oil prices driven by oil-market shocks boosts economic activity in advanced economies, while it depresses economic activity in emerging economies, thus accounting for the muted global economic effects of changes in oil prices; (iii) oil supply shocks caused about 7% of the decline in oil prices between 24 and 2. [PRELIMINARY AND INCOMPLETE. DO NOT CITE WITHOUT PERMISSION.] We thank Chris Erceg, Jim Hamilton, and conference participats at the Federal Reserve Board. J Seymour provided outstanding research assistance. All errors and omissions are our own responsibility. The views expressed in this paper are solely the responsibility of the authors and should not be interpreted as reflecting the views of the Board of Governors of the Federal Reserve System or of anyone else associated with the Federal Reserve System. Federal Reserve Board of Governors. dario.caldara@frb.gov Federal Reserve Board of Governors. michele.cavallo@frb.gov Federal Reserve Board of Governors. matteo.iacoviello@frb.gov

2 Introduction [ ] 2 Estimation and Identification 2. Overview The main idea of our estimation strategy is to exploit outside information contained in several studies that have measured the price elasticity of the demand and the supply of oil to discipline the modelling of the oil demand and oil supply curves in a structural VAR model. Specifically, suppose that the true economic structure describing the oil market and its relationship with the world economy is given by p AX t = α j X t j + u t () j= where E [u t u t] = Σ u, a diagonal matrix, X is a vector of macroeconomic variables including oil production and oil prices, u t is a vector describing the structural shocks, and p is the number of lags in the VAR model. Without loss of generality, we normalize the elements on the main diagonal of A to. The reduced form representation for X t is p X t = γ j X t j + ε t (2) where the reduced-form residuals ε t are related to the structural shocks u t by the following: j= ε t = Bu t, (3) Σ ε = BΣ u B, (4) where B = A, so that alternatively u t = Aε t. Estimation of the reduced form VAR allows recovering a consistent estimate of the n (n + ) /2 elements of E [ε t ε t] = Σ ε. However, in order the recover the n 2 unknown elements of B and Σ u, additional identifying assumptions must be made. To implement our identification scheme, we employ Bayesian estimation techniques. The benchmark monthly VAR specification consists of five endogenous variables: () log world crude oil production (q); (2) the log of advanced economies (AE) industrial production (ya); (3) the log of emerging market economies (EE) industrial production (ye); (4) the log oil prices (p); and the log of an index of metal prices (m). We provide details about the VAR specification and data in Section 3 and in the Appendix. 2

3 2.2 Identification For expositional purposes, we divide the discussion of the identification problem in two steps. First, we discuss the structure we impose on the oil market. Second, we discuss the interaction between the oil market and the macroeconomy The Oil Market Our structural VAR model specifies an oil supply and an oil demand equation, corresponding to the equations associated with the oil supply and oil demand shocks. These equations imply a short-run oil supply and oil demand curves. If we consider a simple bivariate system in q and p, these curves are defined as follows: q t = η S p t + u s,t, () q t = η D p t + u d,t, (6) where η S and η D are the short-run price elasticity of oil supply and oil demand, respectively. Given the values for the elements of Σ ε, the covariance structure of the VAR imposes a unique relationship between η S and η D. The red line in Figure 2 plots this relationship computed by setting Σ ε at its OLS estimate. The two elasticities are inversely related: the larger the supply elasticity, the smaller the demand elasticity. The relationship between demand and supply elasticities implied by the VAR works as follows. By construction, the identification exercise in the VAR attempts to to match the volatility of oil prices and quantities p and q as well as their covariance, which in the data is close to zero. These three targets can be hit in principle with four parameters: the variances of the oil demand and oil supply shocks, the oil demand elasticity and the oil supply elasticity. Figure 3 plots all possible combinations of slopes of demand and supply schedules in the oil market The top row of Figure 3 plots two unorthodox cases in which both schedules slope in the same direction, so that either oil supply rises when the price drops (panel A), or oil demand rises when the price rises (panel B). These cases cannot hit the three targets since they are unable to capture the zero covariance between p and q that one finds in the data. This explains why the admissible pairs of supply and demand elasticities characterized in Figure 2 have opposite sign. Among the orthodox cases, there are four possibilities. The middle row of Figure 3 plots an oil market where both demand and supply curvers are elastic (panel C) and an oil market where both curvers are inelastic (panel D). If demand and supply curves were both elastic, a SVAR could match the zero covariance between We extract the elements of Σ ε related to oil prices and quantities from the equation VAR. The estimation of the bivariate system returns a similar covariance matrix of residuals. 3

4 p t and q t and the volatility of p t by mean of large demand and supply shocks. But large supply shocks would overestimate the third target, given by the volatility of quantities. If demand and supply curves were both inelastic, by constrast, a SVAR could match the zero covariance between p t and q t and the volatility of p t by mean of small demand and supply shocks. But small shocks would underestimate the volatility of q t. This explains why η S and η D cannot be jointly small/large, which leads to the inverse relationship between η S and η D depicted in Figure 2. This leaves us with two observationally equivalent possibilities, plotted in the bottom row of Figure 3. Either the demand curve is inelastic and the supply curve is elastic (panel E), or the demand curve is elastic and the supply curve is inelastic (panel F). Both oil market structures can deliver volatilities and comovements between p t and q t consistent with the data. That is, these two pairs of elasticities are part of the loci graphically represented by the curve plotted in Figure 2. Along this curve, the likelihood of the VAR is constant, hence there is no statistical basis for preferring one point over another. To choose the right elasticities, we need to rely on extra model information. Our identification proceeds in two steps. First, we searched the empirical literature on oil price elasticities and compiled a list of studies that estimate short-run demand and supply elasticities. We list the papers and the associated elasticities in Table. The consensus in the literature, as summarized by the median elasticity, is that the demand elasticity is -.6, while the supply elasticity is.9. This pair of elasticities is summarized by the blue circle in Figure 2. Importantly, the blue circle does not lie on the red line, which means they are not admitted by our SVAR. Second, to select a pair of admissible elasticities, our identification strategy selects the VAR restriction that minimizes the Euclidean distance between the VAR-admissible elasticities and the elasticities obtained by the meta-analysis. Without loss of generality, let us assume that we identify the VAR by imposing the restriction on η S. If we denote η D as a function of η S and the covariance matrix of residuals, η D (η S ; Σ ε ), our identification strategy solves the following problem: 2 min η S η S η S η D (η S ; Σ ε ) η D V η S η S η D (η S ; Σ ε ), η D, (7) where η S and η D are the target supply and demand elasticities, respectively, V is a matrix of weights. 3 Our identification scheme selects the green circle in Figure 2 corresponding to the point on the curve that is as close as possible to the blue circle. Such identification yields, for Σ ε evaluated at its OLS estimate, an oil supply elasticity of.7, and an oil demand elasticity of.4. Figure 4 illustrates the significance of these 2 Alternatively, we could have parametrized the problem either by imposing the restriction on η D, in which case η S (η D ; Σ ε), or, following Uhlig (2) and Rubio-Ramírez et al. (2) among many, in terms of an orthonormal matrix Q, so that η S (Ω; Σ ε) and η D (Ω; Σ ε). 3 V is a diagonal matrix whose elements are the variance of the estimated elasticities reported in Table 4

5 elasticities for gauging price movements in the oil market, by plotting the oil demand and oil supply curves implied by our estimated VAR. The elasticities imply that an exogenous decline in world crude oil supply of percent (approximately.8 million barrels per day) should lead, over a -month horizon, to an increase in oil prices of percent and to an offsetting second-round increase in oil supply of about.2 percent (equivalent to.2 million barrels per day) The Oil Market and the Macroeconomy With a modelling of the oil market at hand, the next step is to model its interaction with the macroeconomy. Current and expected developments in the global economy can lead to shifts in the demand for oil, and hence affect the identification of exogenous oil market specific shocks. In turn, such shocks can lead to changes in economic conditions, which is typically what researchers are interested in. The following five equations decribe the joint modelling of the oil market and the global economy: 4 q t = η S p t + u s,t, (8) ya t = ν Q q t + u ya,t (9) ye t = µ Q q t + µ A ya t + u ye,t () q t = η A ya t + η E ye t + η D p t + u d,t () m t = ψ Q q t + ψ A ya t + ψ E ye t + ψ P p t + u m,t. (2) Equations (8)-(2) summarize the parametric restrictions we impose on elements of the matrix A. These parametric restrictions are guided by economic theory. Equation (8) describes the oil supply schedule and we assume that oil production q t responds contemporaneously only to changes in oil prices. Importantly, as discussed below, the exclusion restrictions imposed on the oil supply equation do not rule out the possibility that movements in global activity have a contemporaneous effect on oil production. Global activity can affect production by inducing changes in oil prices. Equation (9) is the behavioural equation for AE IP. We assume that AE IP responds only to oil production within the period. Equation () is the behavioural equation for EE IP. We assume that EE IP responds to AE IP and to oil production within the period. We allow IP to react contemporaneously to changes in oil production because oil is an input in the production of manifacturing goods. EE react contemporaneously to changes in AE activity because exports to AE are an important component of EE demand. Equation () describes an oil demand schedule in which oil demand is allowed to respond to AE and EE IP and to changes in oil prices. Importantly, the demand price elasticity η D is defined as the change in desired 4 For expositional convenience we omit the lagged terms, which instead appear in equation (3).

6 demand q for a given change in oil prices p, holding IPs constant. Finally, equation (2) describes the behavioral equation for metals prices, which are allowed to respond contemporaneously to all variables in the system. We include metals prices in the VAR because we want to capture the effect of global demand factors on oil prices which are not adequately captured by IP. Base metals such as iron, copper, aluminum are the lifeblood of global industrial activity: as a consequence, they are an ideal bellweather of shifts in cuurent and expected economic activity at the world level, especially in developing economies. The idea is that u m,t is a shock that mainly captures news about global demand, but also current developments that are not adequately accounted for by IP indices. To summarize, in matrix notation, our set of restrictions assumes that the relationship between oil and the macroeconomy is described by the following set of equations: η S ν Q µ Q µ A η A η E η D } ψ Q ψ A ψ E {{ ψ P } A q t y t y t p t m t = p α j X t j + j= u,t u y,t u y,t u d,t u m,t, (3) where ν, ν can be interpreted as the short run elasticity of economic activity to oil production, and e Y, e Y denotes the elasticity of oil demand to economic activity. The restrictions above shown in bold in the matrix A above allow to uniquely identify the structural parameters ( ) ν Q, µ Q, µ A, η A, η E, η D, ψ Q, ψ A, ψ E, ψ P, Σ u given information from Σε, the variance covariance matrix of the reduced form VAR residuals. Comparison with Baumeister and Hamilton (2a). The paper by Baumeister and Hamilton (2a) (BH) offers an alternative approach to filling in the elements of the matrix A in a VAR model with oil prices, world IP, oil production, and oil inventories. BH specify a number of exclusion restrictions for some of the elements of A which are less than the number that is sufficient to achieve exact identification. Next, they specify priors for all the remaining elements of the A matrix in order to help distinguishing between alternative representations of the A matrix which would otherwise all yield the same likelihood. While this approach is tempting, it is not immune from risks. First, by specifying priors for a large number of elements of A, the researcher easily ends up ceding to the temptations of assuming his own conclusions. To give an example, BH impose that economic activity does not depend on oil production (ν =, in our notation), but depend negatively on oil prices, with a coefficient which is bounded between and. If one thinks of the equations behind a VAR as coming from a structural model, there should 6

7 be no reason to assume that economic activity y depends directly on prices but not production. Obviously, to the extent that all shocks move simultaneously oil prices and economic activity, the reduced-form relationship between economic activity and oil prices will involve a non-zero response of activity to prices, but this response cannot form the basis for a prior on the very parameter that one is interested in estimating. Second, coming up with a full set of credible short-run identifying restrictions is difficult. Third, the more priors one specifies, the higher is the likelihood that the VAR is overidentified, and thus incapable of explaining the full variance covariance structure of the data. Comparison with Kilian (29b). The paper by Kilian (29b) is, under some specific assumptions, a special case of our VAR. Kilian works with a three equation VAR where his measure of economic activity y is based on data for dry cargo bulk freight rates. Compared to his approach, we use both IP and metals prices to measure economic activity y. Additionally, we do not restrict ê S to be equal to zero, which is Kilian s working assumption, which implicitly implies a very large oil demand elasticity. 3 Main Results For our purposes, we are particularly interested in understanding how assumptions about η S and η D affect the answer to the following set of questions about oil prices and the macroeconomy:. What Drives Oil Prices? 2. Are All Oil Shocks Alike? 3. Do Oil Price Shocks Matter? To approach these questions, it is useful to rewrite the solved out vector moving average representation of the VAR as follows (we abstract from metal prices as shocks to this variable do not have a contemporaneous effect on the remaining variables in the system): 7

8 q t y t y t p t = B p γ j X t j + η D η S (η A + η E µ A ) η E η S η S η D ν Q η D + η S (η E µ Q ) η E η S ν Q η S ν Q θ η D (µ Q + µ A ν Q ) η D µ A η S (µ A + η A µ Q ) η D + η S (η A ν Q ) η S (µ Q + µ A ν Q ) } η A ν Q η E (µ Q + µ A ν Q ) η A η E µ A {{ η E } B j= u m,t u s,t u y,t u d,t (4) where θ = [η S (η A ν Q + η E (µ A ν Q + µ Q ) ) + η D ]. Figure plots the elasticity of AE IP and EME IP to oil quantity (η A and η E, and the elasticity of oil demand to AE IP and EME IP (ψ A and ψ E ), as function of oil supply elasticity. All elasticities are decresing in η S. Moreover, all elasticities are small, except for ψ E, which takes a value of about. at the restriction selected by our identification strategy. 3. Are All Oil Shocks Alike? Several authors, including Kilian (29b), have claimed that it is important to distinguish among the various shocks that drive changes in oil prices in order to evaluate the response of macroeconomic aggregates to such changes. Our framework allows us to assess such claims analytically. Consider the following experiment. We want to know how much economic activity reacts to a shock that moves oil prices by percent. Obviously, both economic activity y t and oil prices p t are endogenous objects which are driven by the model s fundamental shocks. To calculate the response of activity to oil prices in response to each shock, it suffices to inspect the relavant elements of the impact matrix B. The third row of such matrix lists the impact response of US activity to metal price, oil supply, US activity, and oil demand shocks respectively. The fourth row shows the response of oil price to the same objects. Responses to Oil Supply Shocks. We start by looking at shocks that originate in the oil market. In response to an oil supply shock, the impact elasticity of IP to oil prices is given by y p u s = y p u s = Ainv (2, ) Ainv (4, ) = νe D ( ) νe W + ν e Y Ainv (3, ) Ainv (4, ) = (ν + βν) e D ( ) νe W + ν e Y 8

9 where e W = e Y + βe Y. In words, the impact response of production to a supply-driven oil price increase will be negative if ν, the elasticity of IP to oil production, is positive and if oil is a normal good (so that both e Y is positive and e D is negative), provided that neither ν nor e Y are too large. Additionally, a large negative response will occur when oil demand is very elastic. With an elastic demand, it takes a larger reduction in oil production to cause prices to rise percent. In turn, the larger reduction in oil production feeds back on the real side through the elasticity of IP to oil quantity, and through the elasticity of oil demand to income, both of which magnify the required drop in oil production and activity. [Discuss Figure 7 here] Responses to Oil Demand Shocks. Consider now an oil demand shock. [ADD ANALYTICAL EXPRESSIONS HERE] On impact, a rise in oil demand will cause oil production to rise (by a factor given by e S ), which in turn will cause output to rise (by ν Q ). The combined effect on IP is initially positive, is larger the larger the output elasticity to oil production, and becomes smaller the smaller the oil supply elasticity. [Discuss Figure 8 here] Responses to Activity Shocks. To complete the discussion, we need to discuss activity shocks. It is no surprise that the response of IP to oil prices conditioning on activity shocks may be overwhelmingly different from the responses to direct oil market shocks. Such response is given by: p y u y = Ainv (4, 2) Ainv (2, 2) = p y u Ainv (4, 3) y = Ainv (3, 3) = e W e D + e S ν e S e Y e Y e D + e S νe S e Y Intutively, a % increase in economic activity will shift the oil demand schedule to the right by η A percent. If either oil demand or oil supply are very steep, the increase in oil demand will result is a large increase in prices, and thus in a smaller total derivative of activity to changes in prices. While counterintuitive, the result is only a by-product of the reverse causation fallacy. In fact, these estimates are best interpreted as saying that in response to an activity shock prices drop by a factor that is proportional to the income elasticity of oil demand, η A divided by the sum of demand and supply elasticities (both in absolute value). [Discuss Figure 9 here] 9

10 [Discuss Figure here] Responses to Metal Prices Shocks [Discuss Figure 2 here] 3.2 What Drives Oil Prices To understand what drives oil prices, we want to know how much of the change in p is driven by the various shocks driving economic activity. One way to formalize this question is to consider how much of the volatility in oil prices is driven by oil specific shocks. 3.3 Do Oil Price Shocks Matter? 4 The Role of Oil Prices in U.S. History. Historical Decompositions This section presents the historical decompositions of the actual paths of the VAR variables, as implied by the model estimates, and it relates our findings to those of earlier studies. We use the estimated decompositions to measure the contributions of each structural shock to the evolution of the endogenous variables. In particular, our interest is in assessing quantitatively the relative importance of the shocks in driving realized movements in the real price of oil. Figure 3 shows the decompositions across the whole sample for the real price of oil, global oil production, industrial production in both advanced foreign economies and emerging market economies, and the metals price index. The solid black lines depict the actual paths of the VAR variables, whereas the areas coloured in green, blue, cyan, red, and orange represent the contributions to the actual paths made by metals prices, industrial production in AFEs, industrial production in EMEs, oil supply, and oil demand shocks, respectively. The five upper panels display the decompositions obtained using our identification featuring relatively small elasticities for both oil supply and oil demand, whereas the four lower panels show the decompositions obtained with a Kilian-like identification combining a low short-run supply elasticity and a high short-run demand elasticity. Overall, our results imply that, across the whole sample period, oil price fluctuations were mostly determined by a combination of four shocks, that is, metals prices, EME-activity, oil supply, and oil-specific demand shocks, with their relative contributions varying across sub-periods, and that AFE-activity shocks played a fairly smaller role. Notably, this interpretation of oil price movements differs from that of Kilian (29b), whose findings support the view that oil supply shocks played a comparatively small role in shaping historical oil price movements, and that these, instead, were primarily driven by a combination of global activity and oil

11 market-specific demand shocks. The latter, also labeled as precautionary demand shocks, reflect, as argued by Kilian, shifts in market participants expectations about future availability of oil supplies. His findings also imply that the large effects that adverse geopolitical events have had on the price of oil appear to reflect increases in precautionary demand driven by intensified market concerns about future oil supply shortfalls, rather than actual disruptions to oil supply. Thus, our sizable estimate for the contribution of supply shocks to oil price fluctuations represent a significant departure of our results from those of Kilian. In contrast, our interpretation of oil price fluctuations is more in line with studies that emphasize the role of supply conditions, while debunking at the same time the role of global activity factors, as for example in Hamilton (29), whose analysis provides support for the more traditional notion that most oil price shocks of earlier decades were primarily driven by physical disruption to oil supply caused by exogenous geopolitical events. Our findings are also in line with the overall conclusions in Baumeister and Hamilton (2b) that oil supply shocks are important in accounting for oil price changes and that global activity shocks are not a big factor in oil price movements. 6 Using a Bayesian estimating framework, Baumeister and Hamilton find that the posterior medians for the the short-run supply and demand price elasticities are, respectively,.6 and -.3, two values that are fairly close to those we imposed based on our meta-analysis of the estimated elasticities in the related literature. 7 Interestingly, they also find that placing less weight on prior information about supply and demand elasticities would lead to infer a smaller short-run supply elasticity and a more elastic demand, thus ultimately implying a smaller role for supply shocks and a larger role for demand shocks. The next focus of our analysis consists of four important episodes in the sample involving large changes in the price of oil. We are interested in characterizing the nature of these episodes by measuring the corresponding relative contribution of each structural shock, with the ultimate goal of gaining a better insight about the sources of oil price fluctuations. The first episode is centered around the first Iraq war and lasts from July 99 until April 99. The second episodes focuses on the period of the global financial crisis ranging from August 28 until the beginning of 2, while the third captures the period of the civil war in Libya from February 2 through September 2. Finally, the fourth episode corresponds with the most recent period that saw the dramatic fall in the price of oil from July 24 through March 2. Similarly to Figure 3, the estimated contributions of the structural shocks to the actual paths of the VAR variables during the four key episodes are plotted in Figures 4 through. The impulse response analysis in Kilian (29b), based on the identifying assumption of a zero short-run oil supply elasticity, shows that unanticipated oil supply disruptions have had only a small positive effect on the real price of oil. This finding is also consistent with related evidence in Kilian (28) that oil supply shocks have displayed little explanatory power for oil price fluctuations. 6 In addition, Baumeister and Hamilton (2b) also find that oil consumption demand shocks are also an important factor in oil price fluctuations and that speculative demand shocks are not. 7 Their data also indicate that, for both parameters, values near zero can likely be ruled out.

12 4. The First Iraq War (99 99) Figure 4 shows the historical decompositions of realized movements in the endogenous variables of our model for the period starting from the Kuwait invasion (July 99) until a few months after the end of the First Gulf War (August 99). The upper left panel shows that the real price of oil experienced a sharp rise, jumping about $8 above the level registered in July 99, right before the invasion of Kuwait by the Iraq army. Our results offer a balanced interpretation of the price rise during The salient feature of our decomposition is that the rise was driven by both negative supply shocks and positive oil-specific demand shocks, together accounting for almost 8 percent of the overall increase. These results stand in contrast to those obtained using the identification strategy as in Kilian (29b), which, as shown in the lower panels in Figure 4, attributes the sharp spike in the real price of oil in 99 after the invasion of Kuwait almost entirely to an increase in oil-specific precautionary demand, with a small role played by oil supply disruptions. It is also worth emphasizing that the results in the lower panels obtained by assuming a low short-run elasticity of oil supply are consistent with those from studies who employed a similar identifying assumption. Among these, Kilian (29b) found that oil supply disruptions in Iraq and Kuwait played a small role in the 99 9 oil price shock. Similarly, Kilian and Lee (24) found no support for the idea that oil supply was a key determinant of the real price of oil during the same period. Kilian and Murphy (22) argue that the reason why oil production disruptions in Iraq and Kuwait had a small effect on the price of oil is because they were partially offset through supply increases by other producers. 8 Furthermore, both Kilian (29b) and Kilian and Murphy (22) make the case that the sharp rise in the price of oil in after the invasion of Kuwait was mostly driven by higher precautionary demand. Relatedly, Kilian and Lee (24) found evidence that storage demand for oil pushed up the real price of oil in 99 near the time of the invasion of Kuwait. According to the narrative described in Baumeister and Kilian (2a), the sharp increase in the price of oil following the invasion of Kuwait importantly reflected higher demand for oil inventories driven by concerns of possible attacks on Saudi oil fields, as shown also in Kilian and Murphy (24). Subsequently, as these concerns receded, the oil price fell back along with inventory demand. The importance of precautionary and inventory demand in accounting for the price rise in has been disputed by Hamilton (29). More specifically, to assess the contribution of precautionary demand to the sharp price rise, he examined monthly changes in U.S. oil inventories during the episode and observed that inventories were decreasing, rather than accumulating, thus casting doubts on the explanation emphasizing the role of precautionary oil-specific demand shocks. Hamilton s interpretation of the oil price increase experienced around the time of the First Gulf War focuses, instead, on the role of supply shocks. He stressed how the invasion of Kuwait by the Iraqi army was followed by a disruption of the flow of oil from Iraq and Kuwait 8 The impulse responses in Kilian (29b) show that a negative oil supply shock leads to a sharp but short-lived decline in global oil production and to a small and transitory increase in the real price of oil. As argued by Kilian, this pattern could be partly explained by the endogenous expansions of supply from some producers to offset the initial decrease of production elsewhere. 2

13 equivalent to about 6 percent of global supply. Four months after the onset of the episode, however, global production returned at about its pre-shock level, as Saudi Arabia raised production to offset lower supply from Iraq and Kuwait and thus stabilize prices. 9 Baumeister and Hamilton (2b), another study supporting the view that supply shocks were the biggest factor in the oil price spike in 99, also noted the rapid increases in Saudi production intended to offset part of the lost production associated with the Gulf War. 4.2 Global financial crisis In Figure, we plot the estimated historical decompositions of the model variables for the period of the Global Financial Crisis and the subsequent recovery ranging from August 28 to December 2. As indicated by the solid black line in the upper left panel, the real price of oil experienced a dramatic plunge of more than $ from the start of the crisis until January 29. Both our identification with a low demand elasticity and a Kiliantype identification with a high demand elasticity attribute much of the decline during the Global Financial Crisis to negative oil-specific demand shocks and a more modest role to AFE and EME adverse activity shocks. Considering that the estimated contributions of oil-specific demand disturbances to the observed decline are about the same regardless of the value assigned to the elasticity of demand, it is also not surprising to find that adverse AFE and EME activity shocks played a comparatively smaller role in pushing down the price of oil. The difference in the results across the two identification schemes involves the decomposition of the decrease in global oil production relative to its trend. While a framework as in Kilian (29b), with a low supply elasticity and high demand elasticity, attributes a substantial portion of the observed decrease to supply shocks, as would normally would be expected, our framework with a low demand elasticity assigns a more prominent role to oil-specific demand shocks in explaining the large fall in prices and the concurrent decrease in the quantity of oil produced. Our finding, different from common belief, that the fall in global activity (both AFE and EME) did not play an important role in the plunge of the price of oil in echoes the analyses in the studies of Hamilton (29) and others. Hamilton argued that the collapse in the price of oil was too large to be explained by the observed decline in global economic activity so that, even as the global downturn was acute, it was not severe enough to account for the magnitude of the oil price drop. Instead, Hamilton formulated the conjecture that part of the decline was due the delayed negative effects on demand of the earlier price rises, with the lag reflecting an intermediate-run elasticity of demand higher than the corresponding short-run elasticity. In his comment to Hamilton (29), Kilian (29a) agrees with the view that the drop in the real price of oil after mid-28 reflected only in part an unexpected reduction in global real activity, thereby alluding that other factors, likely associated with the deterioration in financial conditions, also played some role. Along a similar 9 In this case, Hamilton (29) s explanation for the short duration of the price rise in concurs with that of Kilian, as it emphasizes the role of the reaction by other key producers to adjust their supply and thus mitigate the consequences on prices of disruptions popping up in one region. 3

14 trace, the results of Baumeister and Hamilton (2b) indicate that the oil price drop in the second half 28 and the subsequent rebound in 29 reflect the coincidence in time of shocks of different nature, with all the four structural shocks of their framework making a contribution. 4.3 Libyan civil war - Arab Spring Figure 6 presents the decompositions of the actual paths of the VAR variables for the period of the civil war in Libya, from November 2 through January 22. As shown by the solid black in the upper left panel, the real price of oil rose by more than $3 a few months after the start of the episode. Our identification implies that, even as the initial upward-sloping segment of the price path was somewhat shored up by higher oil-specific demand, presumably reflecting heightened uncertainty, a substantial portion of the subsequent higher price level was supported by the reduced flow of oil produced in the region. This result stands in contrast with the decomposition for the real price of oil implied by the identification with a low elasticity of supply and a high elasticity of demand in the spirit of Kilian (29b). The upper left panel of the lower quintet shows, in fact, that the price rise experienced during the Libyan civil war primarily reflected increases in oil-specific demand rather than disruptions in supply, thus pointing to the role of market fears about potential supply disruptions, rather than actual supply disruptions This second set of findings was to be expected, as it confirms the results of Kilian and Lee (24). In fact, using a similar framework and identification as in Kilian (29b), Kilian and Murphy (22), and Kilian and Murphy (24), they provide evidence endorsing the significant role played by speculative demand in the aftermath of the Libyan uprising in early 2 and during the subsequent civil war. More specifically, they found that the Libyan Revolution brought about a shift in expectations in oil markets, thus leading to an increase in speculative demand that lifted the price of oil by as much as $3 per barrel. Their results also indicate that the effect on the real price of oil of the coincident flow supply disruption caused by the events in Libya appear small at a global level slump Finally, Figure 7 displays the estimated historical decompositions for the June 24 December 2 period, characterized by a major slump in the real price of oil. The solid black line in the upper panel shows that the real price of oil lost more than $ per barrel during this episode. Notably, the upper panel also reveals that the decomposition based on our identification with low elasticities for both supply and demand attributes the drop mainly to supply shocks. Around the end of 24 and the beginning of 2, the decline reflected downward pressure stemming from both positive supply shocks and negative oil-specific demand shocks. In particular, on one hand, positive shocks to global supply, as also detected by the decomposition of global production plotted in the upper right panel, likely resulted from the enduring expansion in unconventional shale oil production, as 4

15 also acknowledged by Kilian (24). On the other hand, the negative shocks to oil-specific demand were likely due to waning concerns about future availability of oil supplies and thus heightened expectations of future excess supply in global oil markets. These expectations, in turn, presumably reflected a few main factors, that is, the return to production of oil fields in Iraq and Libya following the end of military threats from extremists, greater market confidence that the expansion in shale oil production would not suddenly lose momentum following the price slump, and OPEC s unwillingness to cut production and thus follow through with a policy of stabilizing prices, as typically done in earlier decades. Later on, the price decline continued to reflect positive supply shock, driven by the resilience of U.S. crude production. In addition, the decline also began to reflect negative EME-IP shocks, directly related to flagging activity in developing economies. In contrast, the decomposition of the 24-2 oil price slump based on the identification as in Kilian (29b), consistent with the results for the whole sample, attributes a sizable portion of the decline to oil-specific demand shocks, with a modest contribution assigned to supply shocks. The study by Baumeister and Kilian (2b) paints an analogous picture. In their quantitative assessment of the drop in the price of oil between June and December 24, they find that more than percent of the decline was actually predictable, as it reflected the cumulative effects of earlier shocks, that is, adverse demand shocks stemming from a slowdown of the global economy, and positive shocks to actual and expected oil production, primarily reflecting the unexpected strong growth of unconventional oil production in the U.S, and Canada. However, Baumeister and Kilian also find that the unpredictable portion of the price decline is not related to supply disturbances, but it instead reflects both a negative shock to oil price expectations in July 24 that lowered the demand for oil inventories, seemingly consistent with a more positive outlook on oil production, and a shock to the demand for oil in December 24 reflecting an unexpected weakening of the global economy. This interpretation is thus similar to what we obtain through a Kilian-like identification, as the shock to oil price expectations in Baumeister and Kilian (2b) maps failry closely into the oil-specific demand shock in our framework, and as the unexpected weakening of the global economy at the end of 24 primarily originated from emerging-market economies. Another study that has assessed the role of supply and demand shocks in the price slump of the second half of 24 is that of Baumeister and Hamilton (2b). In their baseline specification with values for the supply and demand elasticities that are close to those we imposed in our identification, they find that, even though demand shocks come out a little more important than supply shocks during the price collapse, the latter account for a meaningful one third of the overall drop, therefore with this result similar in spirit to the decomposition we obtain through our identification with low price elasticities for both supply and demand. Even though many commentaries remarked that the decision of OPEC to keep its production target unchanged was largely anticipated, between the end of November and the beginning of December in 24, that is, in the days immediately following OPEC s decision, the Brent price of oil lost a remarkable amount of about $ per barrel.

16 Robustness [ ] 6 Conclusions [ ] 6

17 References Baumeister, C. and J. D. Hamilton (2a). Structural interpretation of vector autoregressions with incomplete identification: Revisiting the role of oil supply and demand shocks. Unpublished. Baumeister, C. and J. D. Hamilton (2b). Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks. University of Notre Dame and University of Califoria, San Diego. Baumeister, C. and L. Kilian (2a). 4 Years of Oil Price Fluctuations: Why the Price of Oil May Still Surprise Us. Journal of Economic Perspectives forthcoming. Baumeister, C. and L. Kilian (2b). Understanding the Decline in the Price of Oil since June 24. Journal of the Association of Environmental and Resource Economists forthcoming. Baumeister, C. and G. Peersman (23). The role of time-varying price elasticities in accounting for volatility changes in the crude oil market. Journal of Applied Econometrics 28 (7), Brons, M., P. Nijkamp, E. Pels, and P. Rietveld (28). A meta-analysis of the price elasticity of gasoline demand. a sur approach. Energy Economics 3 (), Cooper, J. C. (23). Price elasticity of demand for crude oil: estimates for 23 countries. OPEC Review 27 (), 8. Dahl, C. (993). A survey of oil demand elasticities for developing countries. OPEC Review 7 (4), Erceg, C., L. Guerrieri, and S. B. Kamin (2). Did easy money in the dollar bloc fuel the oil price run-up? International Journal of Central Banking 7 (), 3 6. Espey, M. (998). Gasoline demand revisited: an international meta-analysis of elasticities. Energy Economics 2 (3), Gately, D. and H. G. Huntington (22). The asymmetric effects of changes in price and income on energy and oil demand. The Energy Journal, 9. Gately, D. and P. Rappoport (988). The adjustment of us oil demand to the price increases of the 97s. The Energy Journal, Goodwin, P., J. Dargay, and M. Hanly (24). Elasticities of road traffic and fuel consumption with respect to price and income: a review. Transport Reviews 24 (3),

18 Goodwin, P. B. (992). A review of new demand elasticities with special reference to short and long run effects of price changes. Journal of Transport Economics and Policy, 69. Graham, D. J. and S. Glaister (24). Road traffic demand elasticity estimates: a review. Transport Reviews 24 (3), Greene, D. L. and P. N. Leiby (26). The oil security metrics model. ORNL/TM-26/, Oak Ridge National Laboratory, Oak Ridge, Tennessee. Hamilton, J. D. (29). Causes and Consequences of the Oil Shock of Brookings Papers on Economic Activity 4 ( (Spring), IMF (2). World economic outlook. Kilian, L. (28, May). Exogenous Oil Supply Shocks: How Big Are They and How Much Do They Matter for the U.S. Economy? Review of Economics and Statistics 9 (2), Kilian, L. (29a). Comment on Causes and Consequences of the Oil Shock of Brookings Papers on Economic Activity 4 ( (Spring), Kilian, L. (29b, June). Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market. American Economic Review 99 (3), Kilian, L. (24, December). The impact of the shale oil revolution on u.s. oil and gas prices. Discussion Paper 34, CEPR. Kilian, L. and T. K. Lee (24). Quantifying the speculative component in the real price of oil: The role of global oil inventories. Journal of International Money and Finance 42 (C), Kilian, L. and D. P. Murphy (22, October). Why agnostic sign restrictions are not enough: Understanding the dynamics of oil market var models. Journal of the European Economic Association (), Kilian, L. and D. P. Murphy (24). The role of inventories and speculative trading in the global market for crude oil. Journal of Applied Econometrics 29 (3), Krichene, N. (22). World crude oil and natural gas: a demand and supply model. Energy Economics 24 (6), Liu, W. (24). Modeling gasoline demand in the united states: A flexible semiparametric approach. Energy Economics 4,

19 Ramcharran, H. (22). Oil production responses to price changes: an empirical application of the competitive model to opec and non-opec countries. Energy Economics 24 (2), Rubio-Ramírez, J. F., D. F. Waggoner, and T. Zha (2, 4). Structural vector autoregressions: Theory of identification and algorithms for inference. Review of Economic Studies 77 (2), Uhlig, H. (2, March). What are the effects of monetary policy on output? results from an agnostic identification procedure. Journal of Monetary Economics 2 (2), Van Robays, I. (22). Macroeconomic uncertainty and the impact of oil shocks. 9

20 A The Data [ ] 2

21 Table : Oil Demand and Oil Supply Elasticities across Studies Short-run Oil Elasticity Author Method Demand Supply Google Cites Baumeister and Peersman (23) VAR Brons et al. (28) Meta-analysis Cooper (23) time-series model Dahl (993) literature survey Erceg et al. (2) DSGE Model -. 9 Espey (998) Meta-analysis Gately and Huntington (22) time-series (6 specs) 28 Gately and Rappoport (988) log-linear demand 37 Goodwin (992) Meta-analysis Goodwin et al. (24) Meta-analysis -.2 Graham and Glaister (24) Meta-analysis Greene and Leiby (26) Oil Security Model Hamilton (29) Meta-analysis IMF (2) Panel, 4 countries -.2 Kilian and Murphy (24) VAR Krichene (22) least squares, ECM Liu (24) Semiparametric, U.S Ramcharran (22) log-log with trend. 9 Van Robays (22) Threshold VAR MEDIAN AVERAGE

22 Oil Production Figure : The Data Oil Production Percent from trend index (98=) AFE IP AFE IP Percent from trend index (98=) EME IP EME IP Percent from trend index (98=) Real Price of Oil Real Price of Oil Percent from trend Real 26 dollars Metal Prices Metal Prices Percent from trend 7 2 index (98=) Note: Sample period: monthly data from 98:M to 2:M2. Data linearly detrended (left column) an undetrended, in log levels (right column). The shaded vertical bars denote the NBER-dated U.S. recessions. 22

23 Impact Oil Demand Elasticity Figure 2: Oil Demand and Supply Elasticities Implied by the SVAR Constraint Estimates from Literature Selected Restrictions Impact Oil Supply Elasticity Note: The red solid line shows the relationship between the oil demand and supply elasticities imposed by the 4-equation VAR. The green square corresponds to the selected identification scheme, given the VAR (supply elasticity:.7, demand elasticity: -.4). The blue circle corresponds to elasticities suggested by the meta-analysis of the literature (supply elasticity:.9, demand elasticity: -.6). 23

24 Price Price Price Price Price Price Figure 3: Alternative Oil Market Structures (A) Downward sloping supply curve B) Upward sloping demand curve Quantity Quantity (C) Both schedules elastic (D) Both schedules inelastic Quantity Quantity E Inelastic demand, elastic supply F Elastic demand, inelastic supply Quantity Quantity Note: The solid lines are oil demand curves and the oil supply curves. The blue dots represent simulated data implied by a given oil market structure. 24

25 Figure 4: Global Oil Market Implied by the SVAR Oil Price (% change)... Oil Production (% change) Note: The solid lines are the oil demand curve and the oil supply curve pinned down by our VAR, in percent deviation from mean. The red dashed line shifts oil supply to the left by percent. See text for details about data construction. 2

26 Figure : How VAR Parameters Depend on Oil Price Elasticity. Elasticity of AFE IP to Oil Quantity.3 Elasticity of Oil Demand to AFE IP..2 % % Impact Oil Supply Elasticity Impact Oil Supply Elasticity. Elasticity of EME IP to Oil Quantity 2 Elasticity of Oil Demand to EME IP % % Impact Oil Supply Elasticity....2 Impact Oil Supply Elasticity Note:. 26

27 Figure 6: How Activity Responds to Oil Price Changes. (AFEIP :24 )/ (P :24 )... Selected Restriction Oil Demand Shock Oil Supply Shock....2 Impact Oil Supply Elasticity (EMEIP :24 )/ (P :24 ).. Selected Restriction Oil Demand Shock Oil Supply Shock....2 Impact Oil Supply Elasticity Note: The lines plot the two-year elasticity of AFE and EME IP to oil prices (average response of activity in the first two years divided by average response of prices in the first two years) implied by the estimated VAR model for the identified shocks. 27

28 Figure 7: Impulse Responses, Oil Supply Shock Oil Supply Oil Production AFE IP EME IP Real Price of Oil Metal Prices Note: The red solid line in each panel depicts the median impulse response of the specified variable to a standard deviation oil supply shock. 28