Another Passthrough Bites the Dust? Oil Prices and Inflation

Transcription

1 Another Passthrough Bites the Dust? Oil Prices and Inflation José De Gregorio Banco Central de Chile Christopher Neilson Banco Central de Chile 2th October 26 Oscar Landerretche Universidad de Chile Abstract This paper shows conclusive evidence, as far as the data allows, that there has been a fall in oil price passthrough during the last 3 years. The paper starts by documenting correlations between CPI and oil prices and then uses two estimation strategies to try to properly identify the effect of oil shocks on inflation. The oil adjusted Phillips curve breakpoints methodology shows a clear fall in estimated passthroughs for industrial economies and somewhat for emerging economies. It seems clear that the assumptions required for this estimation to be valid are not present for emerging markets. Moreover, the passthrough estimations for the most recent periods in emerging markets seem to be much more reasonable and consistent with the results from industrial economies. Stable macroeconomic environments seem to favor the proper estimation of Phillips Curves and the usefulness of the breakpoint methodology. To deal with this difficulty we estimate rolling VARs for a subsample of countries for which we find sufficient data. We derive impulse response functions of inflation to oil shocks and interpret the integrals of these impulse responses as estimates of passthroughs. We find that the effect of oil shocks on inflation for a 24 month window have fallen for all of the countries in the sample. Finally, we survey available hypothesis that explain the fall in the oil passthrough. We thank helpful comments from Rómulo Chumacero, Kevin Cowan, Juan Pablo Medina, Rodrigo Krell, Klaus Schmidt-Hebbel, Claudio Soto and the participantes at the Central Bank of Chile SIIP and University of Chile seminars. jdegrego@bcentral.cl,landerretche@econ.uchile.cl,cneilson@bcentral.cl

2 1 Introduction One of the salient features of recent oil hikes has been the lesser impact that they seem to have had on general price levels worldwide when compared to previous oil shocks. The reasons behind this lower impact can be rationalized in several ways, and we will overview them further down in this introduction, and survey them with more detail in the final chapter of the paper. The main objective of this paper is to gather stylized facts using standard macroeconometric models, so that the discussion on the fall of this passthrough is solely on unidentified correlations. There is no identification of particular transmission mechanisms in this paper, nor the measurement of their relative importance. The emphasis of this paper is, hence, to document the fall rather than to speculate on it s causes. It is important to note that the casual observation that inflation is now lower than in the 7s and 8s in the midst of increased oil prices is not a demonstration of a lower passthrough. In the first place, although nominal oil prices are at record heights, real oil prices are not as high. Hence, current oil shocks are much smaller in relation to the price structure of economies. Also, 7s and 8s inflation rates were not only due to the oil shocks, as fiscal imbalances were also a mayor factor, hence, improved macroeconomic policies may have concurred to create the illusion of smaller passthroughs. Finally, oil prices are not entirely exogenous to the general equilibrium of the world economy. Even in the absence of any exciting news from the Middle East, one should expect oil prices to commove with world inflation reflecting the growth of international money base in excess of world growth. The theoretical rationalizations of the change in passthroughs can give ambiguous answers. For example, the direct effect of oil inflation on general inflation is quite obvious. Expansions in transportation and travel, as well as in car ownership and electronic climate control can be observed in all countries. While the world s population doubled between 195 and 2, the number of cars increased tenfold. Hence, as far as the direct effect goes, one should expect passthroughs to inflation to be higher in time, especially in the developing world where fuel consumption has expanded greatly. An almost direct effect of oil prices on inflation is it s effect on the prices of substitute fuels. As oil prices increase, one should expect substitutes to follow suit as agents and firms shift their fuel demand towards them. So, the motion of the passthrough should be a result of the evolution of elasticities of substitution in the demand for different fuels. Here the effect is not so clear, since this depends both on technological developments, prices and availability of other fuels. When compared to the 7s, there does seem to be an increased supply of some of these substitute fuels (mostly natural gas, nuclear energy and alternatives). Moreover, the investments that have increased their supply can be, in many cases, a result of the oil shocks of the XXth century. It is entirely possible that the increased provision of substitutes 1

3 and flexibility has reduced the passthrough of oil to inflation throughout the world. However in the case of the U.S., oil as a percentage of total energy consumption only went down from 46% in 1973 to 4% in 23 1 showing little substitution effect. It is also entirely possible that the world economy has become more intensive in industries that are able to use a greater variety of fuel sources, for example, the service sector that has increased in importance in most advanced economies. On the other hand, more oil intensive manufacturing activities have shifted to developing economies so we would expect passthrough to follow suit. Oil intensity measured as the ratio between oil consumption and economic output fell by half in the U.S. while it doubled in India and more than tripled in Korea from 1965 to 25. The traditional macroeconomic general equilibrium effect of oil shocks is the good old macroeconomic supply shock. In this case wage earners adjust their inflation expectations as a result of the oil shock and hence become more expensive for all sectors of the economy. Margins generally fall throughout the economy and aggregate supply contracts pressuring prices upwards. If rationality prevails, agents rapidly calculate through the general equilibrium of the economy and end up compounding price hikes into a larger passthrough. On the other hand, if wages are inflexible and firms are locked into long term collective labor contracts, most of the macroeconomic adjustment should be unemployment rather than inflation: stagflation is the macroeconomic result of oil shocks with rigid labor markets. However, if anything, labor markets worldwide have become more flexible, hence, we should expect larger passthroughs and shallower recessions. Slightly more modern macroeconomic general equilibrium models would include the reaction functions of micro-founded economic authorities. Here, the choice of exchange rate regime, monetary targets and cyclical fiscal policy become critical. A strict anticyclical monetary policy, for example, would increase the passthrough due to the stagflation resulting from an oil shock. Paradoxically procyclical fiscal policies would reduce it. Hence, the increased anticyclicality of fiscal policies should have contributed to reduce the passthrough. Also, the evolution of worldwide central banking towards inflation targeting or inflation targeting-like regimes as well as increased worldwide credibility of monetary authorities, would help to reduce the passthrough. Current macroeconomic models have stressed how inflation shocks are related to the complete dynamic structure of costs. In an economy with rational agents, inflation should react differently to oil shocks with different expected persistence. Hence, one could argue that the passthrough for recent oil shocks has been lower because they were expected to be only spikes in the price rather than the long lasting hikes that have ensued. A consequence of this rationale, however, is that if agents do any sort of Bayesian updating of their expectations on the shock, as time passes, the passthrough should increase. As we show in section 4, there is some evidence that this is happening for some countries: passthroughs for 25 are slightly higher than for 23. Also, in 1 Data from the US Energy Information Administration (EIA), Monthly Energy Review. 2

4 section 5 we show how futures contracts have reflected the increased belief of markets on the persistence of current oil prices. It can also be argued that globalization and competition (China, Walmart etc) has limited the possibility that producers have of passing on to consumers their higher costs. However it is unclear why persistent and higher costs for all wouldn tt be passed on eventually. 2 As we can see, the theoretical case for reduced passthrough is not clear cut. There are valid arguments that would lead us to expect increases as well as reductions, and, in the end, we have to check empirically which one predominates. What we will attempt in this paper is to identify the passthrough through diffrent alternative econometric methods and to observe it s evolution in time for a variety of countries. This paper is related to the literature on reduced exchange rate passthroughs, not only in a methodological sense, but also because it is entirely possible that the reasons behind reduced exchange rate passthroughs are related to the reasons behind reduced oil passthroughs. Evidence for reduced exchange rate passthroughs for industrial economies can be found in Campa and Goldberg (22), and for developing economies in Borensztein and De Gregorio (1999) and Goldfajn and Werlang (2). In general these papers (just like ours) measure the fall rather than identify its causes. But the general sense behind these authors is that the fall is due to changes in market structure and increasingly elastic demands (due to an increase in the variety of substitutes). As we have argued above, this rationale does not carry through to oil passthroughs completely. Taylor (2), Choudhri and Hakura (21) as well as Gagnon and Ihrig (21) find that a low-inflation environment is an important cause for reduced exchange rate passthroughs in the 199s. Taylor s argument, for example, is that high inflation acts as grease in the wheels for exchange rate shocks. This could also be the case for oil shocks. Most of the aforementioned literature use aggregate data, but recent work in Frankel et al. (25) uses narrowly defined brand commodities to examine passthroughs from exchange rates to inflation and finds, among other things, confirmation that the inflation environment is important in explaining passthroughs. We have learned from the exchange rate passthrough literature that there are microeconomic as well as macroeconomic factors that affect the way changes in exchange rates transfer into changes in the general price level. The effect of oil shocks on inflation has not received much attention although the impact on US inflation and more specifically, US production, has been studied by Mork (1989) and more recently by Hamilton and Herrera (21) and Davis and Hamilton (23). They argue that nonlinearities and asymmetries are the main features behind the observed relationship between oil and prices. Hooker (22), on the other hand, estimates Phillips curves and tests for breakpoints to study changes in the oil passthrough for the U.S.. He finds that it falls after the 8 s and that neither 2 This argument also requirers business to acomodate and take the loss, but corporate profits in the US have been on the rise in 26. 3

5 nonlinearities nor lower dependency of the economy on oil and energy can explain the bulk of this fall. The evidence presented in Hooker (22) also supports the idea that a low inflation environment is important in keeping passthroughs down. The main contribution of this paper is to extend the calculation of passthrough of oil to inflation to a larger set of countries and to verify if the fall in passthrough is limited to the U.S. economy or is generalized to the broader world economy. We find that passthrough has clearly fallen during the last thirty years. In section 2 we report the main stylized facts on which we base the econometric inquiry of the paper. In section 3 we follow methodologically Hooker (22) and estimate the passthrough augmentig a Phillips curve with oil parameters. We then proceed to estimate multiple breaking points for the Phillips curve model for each country. In section 4 we estimate rolling vector autoregressive models and invert to find the impulse responses of oil shocks on the economy for a more selective sample of countries (due to data limitations). In section 5 we review the available hypothesis on the fall of oil passthroughs as well as the main stylized facts that support them. The paper ends with the conclusions and has generous appendices with extended tables and graphic results. 2 Stylized Facts The common wisdom is that large oil shocks have historically been followed by high inflation in many countries, and in some cases have even preceded hyperinflations. The first two panels of Figure 2 below, shows the nominal oil price series plotted with average inflation rates for industrial and emerging economies since The third panel shows the 24 month percentage change of the price of oil. In it we can see the five oil shocks that we usually think of in this literature (1973, 1979, 1991, 1999 and 23). As we can see, the latter four shocks are similar in intensity when measured this way, while the first one seems much stronger. The 1991 oil shock, however, seems quite transitory compared to the others, and the current one seems to be one of the most long lasting. The trajectories of average inflation rates seem quite different in the two sets of countries. Industrial countries followed a secular reduction in inflation rates from the mid 7s to the Asian Crisis in 1998 when they converged to a steady state consisting of an inflation rate that hovers around 2% (notice the different scales in the two first panels of Figure 2). Emerging economies had comparable inflation rates to industrial countries in the early and mid 197s. Since then, inflation rates steadily increased through the 8s only to peak in the late 8s and early 9s. Around the mid 9s average inflation rates for emerging countries entered into a falling trajectory and 3 All three series are from the IFS data base of August 26. The quarterly inflation series is the yoy percentage change in the CPI. 4

6 seems to have converged, and are now hovering around 5%, since the turn of the century. Despite these differences, both series share a strong positive reaction to the oil shocks of the 7s (the 1973 oil shock detonated by the War of the Yom Kippur and the 1979 oil shock detonated by the Iranian Revolution). There is also a common positive reaction to the 1991 oil shock (detonated by the Persian Gulf War), although, visually the effect seems to have been further lasting among emerging economies, while among industrial countries it generated only a temporary deviation from the downward trajectory of inflation. Also note that there are historical periods where oil prices and industrial economy inflations commove almost perfectly although there is no exceptional situation in oil markets (e.g. the second half of the 8s and the years immediately following the Asian Crisis). Figure 1: Oil and Inflation over 4 Years Brent Oil Price Industrial Country Inflation and Oil Jan7 Mar75 Jun8 Sep85 Nov9 Feb96 Apr1 Jul6 2 Industrial 24 month Inflation Brent Oil Price Developing Country Inflation and Oil Jan7 Mar75 Jun8 Sep85 Nov9 Feb96 Apr1 Jul Developing Country 24 month Inflation 24 month % change of Oils Price % Change of the Price of Oil -5 Jan7 Mar75 Jun8 Sep85 Nov9 Feb96 Apr1 Jul6 8 quarter % change in Oils Price Q4-73 Q4-78 Q3-9 Q2-99 Q1-4 Selected Oil Shocks Source: International Financial Statistics, September 26 A simple way of describing the relationship between oil shocks and inflation is by constructing the simple passthrough coefficient of oil price inflation and general 5

7 inflation. This coefficient is usually defined as the ratio between the general price inflation and oil price inflation for a given horizon. This is typically presented in the exchange rate passthrough literature as a measure of how much devaluation has passed on to the internal inflation rate. Table 1 shows these passthrough coefficients for four oil shocks. We define an oil shock in this calculation as an event where oil prices rise more than 5% yoy and persist for at least 6 consecutive months 4. Table 1: Oil Shocks, Inflation and Passthroughs Coefficients (PTC) (all % changes are over 8 quarters) Shock Period Start 1973Q4 1979Q1 1999Q2 24Q1 Industrial PTC,2,25,11,6 Emerging PTC,23,33,14,12 Avg. Industrial π 31% 28% 8% 6% Avg. Emerging π 35% 37% 11% 1% Avg. Industrial π 11% 8% 2% -1% Avg. Emerging π 1% 14% -6% -6% Nominal Oil Price % 132% 116% Real Oil Price 99% 92% 17% 88% Note: Coefficients represent the ratio between accumulated inflation and oil price change for a 24 month horizon. in inflation represents the level change from the 24 month period after the oil shock vs the previous 24 month period. Individual episodes of high inflation (π > µ + 3σ 1%) were eliminated to avoid distortions. Under this criteria we get the expected oil shocks in and , but we also find oil shocks in and The Persian Gulf War price spike does not qualify as an oil shock due to its brevity. Interestingly the 1999 and 24 oil shocks were spiked by events in the Middle East but are not considered to be sustained by any geopolitical situation. The 1999 shock was caused by a political regrouping of the OPEC countries that had lost cartel discipline during the years as non-arab member had entered (mainly Mexico, Venezuela and Russia). Although this event triggered the price hike, it is accepted to have been sustained by strong demand financed by fast economic growth of the U.S. and Chinese economies. Again in 23 it was the Iraq War that spiked oil prices and was then followed by the escalation of the Nigerian civil war and the hurricane disasters in the Gulf of Mexico. However, one of the main causes behind the current oil shock is the unrelenting Chinese growth. But, this time, there seems to be an additional source, which is the speculative positions 4 The pass-through coefficient only makes sense when changes remain until at least the end of the horizon for which its being calculated. Otherwise the variation that has been undone is not taken into account by this method. This is why the Persian Gulf War shock does not qualify as an oil shock in Table 1. 5 The last episode start is picked so as to have a full 24 month window. 6

8 taken in the oil market by international financial intermediaries hunting high yields. Rows 2 and 3 of Table 1 show average oil passthrough coefficients for a 24 month window following each oil shock. Note that they were very similar in the mid 7s, and then diverged as they both increased towards the late 7s. Also note that by the late 9s both passthrough coefficients had fallen, and by the Iraq War they had fallen further, especially among industrial economies. Finally bare in mind that individual episodes of inflation over 1% were eliminated, 6 most of which fell into the 7 s, so that we do not excessively overweigh hyperinflationary countries in our sample of emerging economies. Hence, the fall in the passthrough coefficients is underestimated in Table 1. Table 1 also shows average inflation levels during each episode on rows four and five. High inflation rates seem to be correlated with high passthrough coefficients, just like in the exchange rate passthrough literature. Rows six and seven show the difference in average inflation in the two years before and after the oil price rise. It seems that a central difference between the oil shocks is that the first two correlate with significant increases in inflation while the latest two oil shocks are correlated either with stationary (in the case of industrial economies) or falling (in the case of emerging) inflation. Finally, the last two rows in Table 1 show the relative size of the oil price rise over the 24 month horizon. The current oil shock is comparable to previous ones both in real and nominal terms, but slightly smaller; and, that it has happened in an environment of substantially smaller inflation (although this was also true in the 1999 shock). Obviously Table 1 has all the problems and limitations that unidentified correlations have. First, when oil prices and inflation commove there is the possibility that we are not identifying properly the effect but rather observing the consequences of shocks on other markets or parameters. The traditional statistical way of identifying the effect of oil shocks on inflation is to use econometrics methods to control for everything else that is going on in the economy. This is what we do in the next two sections of this paper. 3 Oil Pass-through and Structural Breaks in the Traditional Phillips Curve In this section we follow Hooker (22) in estimating the effect of oil prices on reduced form Phillips curves. In doing this we follow a long tradition of applied macroeconomists that continue to use this econometric estimation as a first glance tool. 7 6 This limit corresponds to approximately three standard deviations over the average of average inflation. 7 See Rudd and Whelan (25) and Gali et al. (25) for a review and a discussion on alternative forward-looking Phillips curves and there empirical relevance. 7

9 In this section we estimate a traditional Phillips curve equation with several lags of inflation, production gap and the percentage change of oil prices. Our efforts in this section are concentrated on extending this estimation to the broadest set of countries possible. In fact we estimate oil adjusted Phillips curves for 37 countries of which 23 are industrial and 14 turn out to be emerging. Although the lag structure of the Phillips Curve varies from country to country, we take uniformity as far as we can go. Surveying through the literature one finds that preferred Phillips curves for different countries vary substantially in specification. In a sense, we sacrifice fitness of our estimations on the altar of comparability. Finally, country Phillips curves frequently include dummies reflecting the common knowledge of local economists about structural breaks o other anomalies. For example, for the U.S., dummies for the Nixon price controls usually improve these estimations substantially and in emerging economies, dummies for particularly violent social and economic events usually prove useful. These dummies are usually quite noncontroversial and also quite critical, especially in emerging markets (more so in those with periods of hyperinflation). However, in this paper we will not include dummies for any country, since we do not have the specific information required and, even if we had, it would hinder comparability. Finally, to enlarge the sample of countries as much as possible, we use industrial production or real GDP indices as proxies of economic activity depending on their availability. 8 The details of the data used are presented in the Appendix A-1. The evidence above suggests there has been a fall in the correlation between oil price shocks and inflation so the regressions are tested for multiple structural breaks on all parameters 9 following Bai & Perron (1998,23). This is the main methodological feature of this section. 8 We are a aware that this could lead to substantial defects in the quality of our measure of output gaps. For example, some emerging economies, in the last few decades, have de-industrialized as a result of their comercial liberalization processes and, instead have specialized in activities in which they have clear cut comparative advantages (e.g. Chile in commodities, India in services). Although it is true that these processes usually happen at a longer frequency than economic fluctuations, it is, nevertheless, true that these indices could be quite non representative. In any case, and considering the scarcity of quarterly production data, we still think that industrial production data is an acceptable proxy for economic activity. 9 Interesting surveys of the structural break literature can be found in Hansen (21) and Perron (25). 8

10 3.1 Methodology Generally, we estimate 1 : π t = α + 4 β i π t i + i=1 4 γ i (y t i y t i ) + i= 4 θ i oil t i (1) i= Where π is the quarterly yoy percentage variation of general CPI index, y is the quarterly yoy percentage variation of the industrial production index, y is the Hodrick- Prescott filtered trend of y and oil is the quarterly yoy percentage change of the barrel of brent oils U.S. dollar price. The full passthrough from an oil price shock to inflation, (φ) would then be 4 i= θ i φ = 1 4 i=1 β i We test this specification for multiple structural breaks as suggested in?. Hansen (21) and Perron (25) provide an interesting explanation and review of the literature on structural breaks and we will only briefly discuss the main intuition that sustains the method that we apply. The way that this methodology works is to assume that the date (T i ) and the amount of structural breaks (m) can be jointly estimated with the parameters using the least squares principle. An example for the case of one structural break can be quite illuminating. In this case the parameters {β i, γ i, θ i } = δ are estimated for each possible break date T. In this way {β i, γ i, θ i } are a function of the break date and so the date that minimizes the SS ( δ{t } ) is chosen as the estimate for ˆT and ˆδ{ ˆT }. In the case of one break (m = 1), equation (1) can be written as the following linear regression: [ ][ ] z1 δ1 y t = + u z t = z tδ j + u t (3) 2 Where the number of rows of z 1 and z 2 depend on the break date T 1 but the number of columns is simply k. This way z t is n km matrix with the partitioned data along the diagonal. In the case of two or more breaks (m breaks), the same is repeated for all possible combinations of T 1, T 2...T m. As one can imagine, this requires extensive grid searching. Fortunately Bai and Perron (23) have developed an efficient algorithm and that allows very short calculation times and shared their GAUSS code which we use. 1 Estimation for multiple breaks was carried out using Matlab code based on the GAUSS code provided by Pierre Perron at his web page. Right hand variable lag lengths were selected by Hannan- Quin information criterion. 9 δ 2 (2)

11 Thus, for the general case, equation (1) can be written following the notation in Perron (25), can be as a linear regression with m breaks (or m + 1 regimes): y t = z t δ j + u t (4) for j = 1,...,m + 1. Where z contains inflation, gap and oil lags on its diagonal in partitioned matrices determined by the break dates. Allowing all coefficients to change across regimes lets the inflation process adjust in each regime. This is of central importance since we are interested in the long term passthrough (over a year) which depends on the persistence of inflation generated by second round effects. 3.2 Results Before estimating the oil adjusted Phillips curves for each of the countries in our sample we estimate it for industrial country wide aggregates. Interestingly we find a structural break in 198, just after the Iranian Oil Shock. The estimated passthrough falls from.18 to.5. The economic interpretation is the following: pre 198, a 1% increase in Brent passed through as a.18% increase in inflation so that, lets, say, the 98% dollar increase in Brent that we had in the 1973 Yom Kippur oil shock explains a 18% increase in inflation over 24 months (which is roughly 9% a year). Post 198, a similar shock would increase inflation in industrial countries by 5% over 24 months (2% per year). In the remainder of this subsection we extend this estimation the largest possible sample of countries that available data can allow. Complete regression outputs and tests are presented in Appendices?? and A-2 respectively, and table A-1.2 lists the estimated break points for the sample of countries for which we have been able to obtain sufficient data to run the tests. We have sorted breakpoints into four rough time categories: the mid 7s, the early 8s (which probably reflect the first two oil shocks) the transition from the 8s to the 9s and the late 9s. Of the 49 countries we subjected to this method, 1 did not reveal evidence of any structural breaks. In the interest of brevity we summarize the results in Figure 2. There we take averages on the countries in our sample. It shows averages over the full 49 countries, but also, to increase comparability in time, over the 2 countries that are present throughout the whole period. Break points are indicated in the graph by the discontinuous jumps in the two series. Also, both series are calculated by taking average passthrough over available countries, but eliminating the top and bottom two observations to insulate from outliers. The Figure shows a very clear trend of falling passthroughs. Conservatively (on the basis of the 2 country average) we could say that passthroughs have fallen by two thirds, from leveles that hovered slightly above 1% to approximately 3%. Unfortunately, the 2 country group, although consistent and comparable in time, underestimates what has happened in emerging economies. Some indication of the effect 1

12 Figure 2: Estimated Average Oil Passthrough Through Time Oil Passthrough Q1-65 Q1-7 Q1-75 Q1-8 Q1-85 Q1-9 Q1-95 Q1- Q1-5 Average over constant group (2 countries) Average over all available countries (49 countries) of including these economies can be glimpsed at by seeing the evolution of the 49 country average. According to this measure, passthroughs have fallen by more than six sevenths from the neighborhood of 14% to below 2%. A few things are worth highlighting from the extended tables of the Appendix. The first is that most industrial countries display significant falls in their passthroughs, just as the industrial country aggregates do. Interestingly, the trajectory that we estimate for the U.S. has a similarity to the one in Hooker (22). That is, passthroughs fall, and we find a break in the early 8s, which is remarkable since Hooker uses unemployment and multiple dummies in his Phillips Curve while we use industrial production and no dummies at all. However, we are fortunate to be able to extend the time frame of his estimation, so that we find an additional break in Also notice that we obtain the exact same breakpoints for Canada as in Khalaf and Kichian (23) from the Bank of Canada. Another exceptional country is, of course, Israel which has been in center stage of many of the oil shocks. In their case, passthroughs have fluctuated dramatically but have ended up falling to negative levels. It is very likely that, due to the volatility of Israeli macroeconomics (and politics for that matter) this Phillips curve methodology is not well suited to estimate passthroughs for them. Also exceptional are the mildly negative passthroughs of the Japanese and Korean economies, again, it is probably safe to say that passthroughs have fallen in these two countries during the last three 11

13 decades, but the negative passthroughs show a certain inadequacy of this methodology. We also present oil shock breakpoints for a set of hyperinflation economies in the Appendix. Usually these countries display very strange and volatile passthrough changes. Our emerging market sample has several problems: it is small (as a result of data limitations), it is loaded with oil producers (which probably do not get into stagflations with oil shocks) and it has several countries that passed through sever hyperinflation episodes (notice early passthroughs for Argentina, Brazil and Chile). The results for our estimations are disastrous and even nonsensical in these cases. We cannot interpret these parameters and we just display them to illustrate the serious limitations that this methodology has when applied to emerging markets. However, note that the final passthroughs estimated for emerging markets reflect the global trend of falling passthroughs and more stable macroeconomic environments (suitable for estimating Phillip Curves). It is quite obvious that the estimated passthroughs are reflecting different things than oil shocks (credit crunches, balance of payment crises... etc.) that may have coincided with oil shocks but were only triggered by the ensuing global recessions. In the next section we attempt a more rigorous estimation that involves a clearer identification of the interaction of different economic variables. Unfortunately, the methodology requires higher frequency data and more variables and we therefore sacrifice sample size dramatically. 4 Oil Shocks and VARs In this section, VAR models are estimated for rolling windows of data starting between depending on data availability. This section draws heavily on Wong (2) where rolling VARs are used to argue that the effectiveness of U.S. monetary policy has fallen in the U.S.. However, our object of interest for is the orthogonalized impulse response of the consumer price index from an oil price shock. Our hypothesis will be that the the impact of oil shocks has fallen as the rolling windows move closer to the present. In this case, the measure of the passthrough will be the integral of the impulse response function for the VARs run on rolling windows. We will try to inquire if the integrals of the impulse response functions have fallen through time. The main advantage of the rolling VAR methodology is that it is a very unstructured way of analyzing parameter changes and instability in time. This methodology is descriptive in nature and probably allows us to estimate the effect of oil shocks on inflation forecasts, since the changing structure of the VARs should reflect the changing optimal inflation models used by agents to forecast inflation. The main defect of the methodology is that it requires more data (higher frequency for lags and more variables for the structure of the model). Given these data limitations, we try to approximate the best benchmark model, baring in mind that our aim is not 12

14 to investigate VAR modeling or inflation modeling but to use a method to see the changing effects of oil shocks given the model. Again we will sacrifice fitness of our estimation on the altar of comparability. 4.1 Methodology Since oil shocks have changed in time, using the triangular decomposition in the VARs is particularly critical. For example, passthroughs may be constant through time, but the intensity of oil shocks may change. A Cholesky decomposition mixes these two elements of the effect of an orthogonal shock, sinceit generates relative responses of endogenous variables to orthogonalized shocks. This is why when Cholesky impulse response functions are reported, they are measured in standard deviations of the shock. We know if the shock has statistically significant dynamic effects, however, we do not know if it has economic importance and, moreover, we cannot properly compare impulse responses of different windows in time, since it is perfectly plausible that the intensity of orthogonalized shocks has varied over time. In section 2 we reported some differences in the intensity and length of the fluctuations of oil prices during the five potential shocks that we have analyzed in the post war period. The 1973 Yom Kippur shock seems to have been the most intense, but the following have varied in length. When we estimate using the triangular decomposition, we can compare properly, and observe both the statistical significance and economic importance of shocks. Data limitations substantially limit our sample of countries. We have only nine industrial economies (Korea, Japan, Denmark, France, Germany, Italy, the UK, the U.S. and Canada). We only have three emerging economies: Colombia and Chile which are the countries with longest comparable series in Latin America, and Israel, which is always difficult to interpret, more so when analyzing oil shocks Impulse response functions and rolling windows. We estimate a VAR model by OLS and due to the use of rolling windows, a slightly different approach must be taken in the calculation of the impulse response functions if we want to compare them through time 11. The general system is the following: y t = c + p Φ i y t i + ǫ t (5) i=1 With ǫ N(, Ω). Estimating ˆΦ, ˆΩ by OLS, the MA representation can be written as y t = µ + ǫ t + Ψ t 1 ǫ t (6) 11 See Hamilton (1994) 11.4 for details. 13

15 The object of interest is the amount by which we must revise our forecast of CPI given new information for oil: E(y CPI,t+s y t ) y oil,t s = 1, 2,...horizon (7) With ǫ oil,t >, we can revise our estimate for the other ǫ j,t s behavior by using the information contained in Ω = T 1 T i=1 ˆǫ tˆǫ t. Concretely we want to find how the unit change in y oil,t = ǫ oil,t leads to changes in the vector of innovations ǫ and use this information together with the Ψ t+s for each s for the relevant horizon to find by how much we should revise our forecast for CPI given y t, ǫ t. We use the triangular decomposition Ω = ADA where A is lower triangle and D is a diagonal matrix giving the variance of u t = A 1 ǫ t, where u jt is the residual projection of ǫ j,t on u j,t and so has the interpretation of new information of y j,t beyond that contained in y j,t. The effect of ǫ j,t on ǫ t is given by the the column j of matrix A denoted by a j and in our specific case correspondes to the column A oil. This way the ortogonalizad impulse response function is given by the following expresión: E(y CPI,t+s y t ) y oil,t = Ψ s A oil s = 1...horizon (8) The triangular decomposition is done instead of the more popular Choleski decomposition so as to isolate the estimated variance of the variable being shocked. The Choleski decomposition is the following: Ω = ADA = AD 1/2 D 1/2 A = PP (9) In this case the impulse response function is given by E(y CPI,t+s y t ) y oil,t = Ψ s A oil doil s = 1...horizon (1) Where d oil is the element along the diagonal of D corresponding to oils ortogonalizad variance. Both Φ and Ω are functions of the data of each particular window so that the simulated Ψ and Â, D will be changing when the estimated window changes. The impulse response functions will be changing due to changes in Φ, Â and D in this case and simply comparing them could be misleading since a fall in level could be due to a fall in the size of the shock d oil. By using the triangular decomposition, we can interpret equation (8) as the consequence on CPI of a one unit rise in the log of the price of oil. 14

16 4.1.2 The model and variables The general case for non-u.s. countries, the variables used following Kim and Roubini (2) were the log of oils (Brent) price, the fed fund rate, log of industrial production index, log of the consumer price index, log of M1, short term interest rates, and the log of the exchange rate to the dollar. A trend was also added. The model for the U.S. economy is characterized following Wong (2) and Bernanke and Mihov (1995) and includes the log of oils (Brent) price, log of the industrial production index, log of consumer price index, federal funds rate, log of total reserves and the log of non-borrowed reserved. 12. We are purposefully not imposing any structure on the VARs and using a set of variables and an ordering that is as consensual as possible. This we do so that the focus of our result is intertemporal and international comparability, rather than the specifics of VAR estimation for each country. We also must caution the interpretation of the regressions and impulse response functions of Germany, Italy and France, since these countries transited, within the sample, to the monetary union. 4.2 Results Figure 3 shows the 24 month impulse response functions for selected windows of 2 month time spans 13. Figure 4, on the other hand, shows the integrals of these impulse functions for all the VARs estimated for each country. Figure 4 seems to give additional evidence supporting the reducing passthrough hypothesis. All countries in the sample show reductions in passthrough up to the turn of the century. Interestingly, the U.S. displays the same increase in passthroughs up to the 8s that it showed with the Phillips curve methodology. Chile and Colombia now show very clear reductions in the passthrough, and Korea and Japan show low but positive effects of oil shocks on inflation in the most recent VARs. It is interesting to note, nonetheless, that there seems to have been a slow recovery of passthroughs in some countries. In particular, Canadian passthroughs have recovered almost to their old levels and U.S. passthroughs are well on their way. Still countries like Japan, Korea, Denmark, Israel and Chile seem to be stationed in a low passthrough steady state. As we have argued in the Introduction, if the recent oil shocks were expected to be transitory, passthroughs should be low in an economy with fully rational forward looking agents. If this rationalization of passthrough fluctuations has any merit we should expect passthroughs to increase as the oil shock lasts and we include more data in future versions of the paper. Interestingly it seems to be already happening in the U.S. and Canada. Another interesting feature of these estimations is that they seem to indicate larger 12 Three lags were used in all specifications for comparability. 13 These models were estimated with different window sizes and we find similar results. 15

17 falls in passthroughs than those estimates in the previous section. Unfortunately, due to data constraints, not all countries are available for both procedures, nor are they necessarily for the same time frames. For example, comparisons for France and Germany are nonsensical. However, it is interesting to notice that Denmark was a country that is included in the data base used in section 3 but ended up not included in any of the tables, because we could not fina a significant breakpoint. On the other hand, in this chapter there seems to be very clear evidence of a fall in the Danish passthrough, specifically when the data of the early seventies falls out of the estimation window. For the countries that are included in both exercises it is probably best to compare the relative changes in the passthrough that both methodologies generate rather than the specific parameters estimated. This although in section 3 the estimated passthrough coefficient was the permanent effect of a 1% increase in oil price growth on inflation, and in this case we are estimating one unit of the log of U.S.$ oil prices, so the shocks are approximately comparable. If we are to compare the results of this chapter with the previous one it is probable best to look at the end of the 24 month impulse response functions as an estimation of the permanent effect of the shock on inflation, and remember that the units of the impulse response functions are the logs of monthly CPI, so that, to make them comparable we would have to, approximately, multiply by twelve. Hence, if the previous chapter shows the U.S. passthrough falling from circa 16% to circa 6%, in this case it seems to be falling from circa 6% to circa 6%, a much stronger fall. For Canada, our previous calculations show a fall from circa 5% to circa 2%, our new calculations show a fall from circa 3.5% to circa.5%, which is still a stronger fall. In general, our VARs indicate a much stronger fall in passthroughs. Finally, it is important to point out that we are able to find a reasonable estimate for the fall in the Chilean passthrough, that was not possible in the previous section due to the difficulties of the methodologies in adapting to a country with so many structural transformations. In this case we find clear cut evidence as we do for Colombia that the passthrough has fallen. This highlights the advantages of the methodology of this section. 16

18 Figure 3: Selected IRF from a unit shock to oil. (shock measured as 1 unit of the log of oils U.S.$ brent price ) 2 x 1 3 Germany Italy.2 France Denmark Chile.8 Colombia Japan Korea Israel Canada.4 UK USA Jan72 Aug88 Jan78 Aug94 Jan84 Aug Jan89 Aug5 *Window widths of 2 months. Periods selected arbitrarily and are meant to be compared with others of the same country and not between countries. 17

19 Figure 4: Accumulated Effect of a unit Oil Shock on Inflation (shock measured as 1 U.S.$) Germany Italy France Dec86 Jan9 Mar93May96Aug99 Window End Date 1.5 Oct91 Feb94 Jul96 Dec98 Window End Date Oct94 Sep96 Jul98 Window End Date 1 Denmark 2 Chile 1.5 Colombia Nov9 May93 Dec95 Jul98 Window End Date 1 Nov91 Jan94 Apr96 Jul98 Window End Date.5 Nov96 Aug99 Jun2 Window End Date Japan Korea Israel Nov73 May79 Sep84 Jan9 Window End Date Nov86Dec89 Feb93 Apr96 Jul99 Window End Date Jan99 Feb Apr1 Jun2 Aug3 Window End Date Canada 1.5 Dec73 May79 Sep84 Jan9 Window End Date UK Nov88 Aug91 Jun94 Apr97 Feb Window End Date USA Nov85 Jan94 Mar2 Window End Date *The accumulated effect is calculated as the integral of the impulse response function for a 24 month horizon. Window Width is 2 months. 18

20 5 Why the Passthrough has Declined? Up to this point in this paper we have documented a generalized decline in the oil passthrough for a large number of economies. To do this we have used two alternative statistical methodologies that seem to show consistently that there has been a significant reduction in the effects of oil on inflation in industrial as well as emerging economies. In principle there are many possible explanations behind this stylized fact, and, since the decline in the oil passthrough is a salient feature of current macroeconomic developments in the global economy, a number of these hypotheses are worth to examining. Our empirical approach of previous sections, does not allow us to discriminate between these hypotheses, and identify specific the passthrough channels. So, in this section we survey the available hypothesis on the fall of oil passthrough and circumstantially address the evidence that supports each argument. 5.1 Has the World Economy Become Less Oil Intensive? The first argument, used not only to explain the low passthroughfrom increases in oil prices to inflation, but also, and more frequently, to explain the low recessionary effects of recent oil price surge, is that the world economy has changed structurally since the oil shocks of the seventies. This change has been a reduced importance of oil in the economy. Industrial economies have become service oriented and the difficulties of previous oil shocks have driven to the acquisition of more energy efficient technologies as well as improve the efficiency of energy consumption. Hence, as economies become less dependent on energy (and represent a smaller proportion of total cost), arithmetically, the effect of an oil shock is smaller. There is plenty of circumstantial evidence to support the energy efficiency thesis for the U.S.. Peterson (26), in a Congressional Budget Office report, shows that both petroleum and natural gas consumption measured in BTU (British Thermal Units) per unit of real GDP has fallen by half in the U.S.. Simple calculations from British Petroleum official data sets on worldwide oil consumption show that in 1965 U.S. citizens consumed an average of 2.69 barrels of crude per year, in 25 this number had increased to So, oil consumption per capita has grown. However, since GDP per capita has grown even more, U.S. GDP has become less oil intensive. In fact, in 1965 it took 1338 barrels of crude to produce a billion of U.S. GDP (in 2 U.S.$), in 25 this fell to 753 which is 44% less. Moreover, the Energy Information Administration (2) shows that oil intensity in the U.S. has fallen 42% since the 197 s. This fact for the U.S. does not necessarily hold for all countries, nor has all the world befallen to the same process. Figure 5 plots the ratios of oil consumption to real GDP for a set of 45 countries for the years 1965 and 24. The figure plots two lines, a 45 degree line to discriminate countries that increase oil dependency from 19

21 those who reduce it and a 22.5 degree line that separates countries that reduced oil dependency by half or more (like the U.S.) from the rest. As the figure clearly shows, the U.S. does not represent the average reduction in oil intensity. On the contrary, it is one of the extreme cases. World oil intensity has fallen only 27% despite the fact that the U.S. represents almost 3 percent of world GDP. The figure also shows that at least 2 out of 45 countries maintain or increase their oil intensity in the last four decades. Only 12 countries show falls in oil dependency that are comparable to the U.S Barrils of Oil/Real GDP (USD 2) ECU THA KWT IDN EGY MYS IND VEN CHN KOR DZA PHL PAK ZAF MEX GRC BEL PRT BRA CHL CAN NLD NZL PER ESP World HUN COL ISL AUS JPN ITA FIN USA IRL AUT FRA ARG CHE NOR DNK SWE GBR HKG Barrils of Oil/Real GDP (USD 2) Figure 5: Oil Importance in the Economy Hence, although the oil efficiency hypothesis is probably part of the explanation for the reduction in the oil passthrough, this is only the case for a subset of countries. It could be a factor behind the decline in the passthrough in the U.S. and some other countries in our sample (Canada, Argentina and some European countries). However, even for these countries, the fall in oil dependency cannot account for the magnitude of the decline in the passthrough. The fall in oil intensity is at most 5%, while in section 2 we showed that oil passthroughs have fallen by in the 66%-85% range (explanation of Figure 2), and in section 3 we have shown evidence that there can be even greater falls. Hence, even for these countries, we are probably missing part of the story. It is important to note that of the sample of countries in our VAR exercise of section 4, a majority of countries are in the category of falling oil intensity. On the 2