
INTRODUCTION
Quantifying the magnitude and establishing the timing of the passthrough of oil price changes to consumer prices is crucial, particularly so because oil prices tend to undergo wide fluctuations. Consider the plunge of oil prices from July 2014 to February 2016, from about $100 per barrel to $30. What is the effect of such a large swing in oil prices on core inflation? And how long will this effect last? Different answers to this question have very different implications for inflation forecasting, and hence for the stance of monetary policy.
Oil price fluctuations affect consumer inflation through both its energy component and the nonenergy components. However, while there is clear evidence that the passthrough from oil prices to energy prices is relatively fast and complete (Burdette and Zyren, 2003; Meyler, 2009), as well as symmetric (Baumeister and Kilian, 2016), it is unclear to what degree changes in oil prices passthrough into nonenergy prices (Kilian and Lewis, 2011; Kilian, 2014).
In theory, an increase in oil prices might have an inflationary effect in at least three ways. First, because energy prices represent a considerable portion of production costs for a few sectors. Second, because it might lead to higher inflation expectations. Third, because it might lead workers to demand a higher wage to compensate for the increase in energy prices (Bruno and Sachs, 1985; Blanchard and Gali, 2009). By contrast, an increase in oil prices might have a deflationary effect in the same fashion as an adverse demand shock because higher energy prices tend to reduce netdisposable income, and thus consumption (Edelstein and Kilian, 2009; Baumeister and Kilian, 2016; Baumeister et al., 2018) and investments (Edelstein and Kilian, 2007).
Empirically, despite extensive evidence that changes in the oil price contribute to macroeconomic fluctuations (see Hooker, 1996; Barsky and Kilian, 2002; Kilian, 2008b, 2009a,b; Kilian and Vigfusson, 2011, 2017, among others), various authors have shown that the passthrough of oil price changes to core prices has declined since the mideighties (see Hooker, 2002; Chen, 2009; Clark and Terry, 2010, among others) up to the point that it is very limited if not zero (for example Cavallo, 2008; Clark and Terry, 2010).
In this paper we use a different methodological approach to estimate the oil price passthrough into core consumer prices. Our starting point is that fluctuations in disaggregate prices are the result of both macroeconomic shocks, as well as idiosyncratic shocks or measurement error. Hence, in order to estimate correctly the passthrough of a change in the oil price, we have first to disentangle price fluctuations due to macroeconomic shocks, from price fluctuations due to idiosyncratic shocks and measurement error. This is particularly true, given that idiosyncratic shocks can have a nonnegligible effect on core pricesfor example, the plunge of the wireless telephone services index that occurred in March 2017, which shaved of about a tenth of a percentage point to core inflation.
Our empirical strategy consists in using a restricted version of the structural dynamic factor model (Forni et al., 2009) very similar to a FAVAR model (Bernanke et al., 2005). In practice, we first estimate a dynamic factor model on a panel of disaggregate prices, thus allowing us to disentangle common changes in disaggregate prices due to macroeconomic fluctuations from idiosyncratic changes due to sector specific characteristics. And then, we use VAR techniques to estimate the oil price passthrough via the common component, as well as via the idiosyncratic component. Both these passthrough are likely to be important. Indeed, given that they contribute to macroeconomic fluctuations, changes in the oil price might passthrough into core inflation via the common/macroeconomic component. At the same time, changes in the oil price might have a direct effect on some disaggregate pricethose whose production is particularly energy intensiveand therefore they might also passthrough into core inflation via some idiosyncratic component.
Our empirical analysis is carried out on a panel of U.S. personal consumption expenditure (PCE) disaggregate price indexes from 1984 to 2016. We show that an oil price change passesthrough core PCE prices only via its effect on the whole economy, while the direct effect via the cost channel is null. Moreover, the subsample analysis confirms the result in the literature whereby the oil price passthrough into core inflation has decreased over time. However, in contrast with part of this literature (for example Clark and Terry, 2010) we always find a statistically significant passthrough. Finally, we estimate the oil price passthrough on a panel of euro area harmonized index of consumer prices (HICP) at a disaggregate level. This estimate yields a euro area passthrough similar to that of the U.S..
Our results are different from those available in the literature, because it turns out that common and idiosyncratic dynamics in disaggregate prices have different statistical properties: common dynamics are slow moving, idiosyncratic dynamics fast moving and volatile. Therefore, disentangling these two components, which is the novel feature of this paper, is crucial, as in this way the noisy idiosyncratic component does not affect estimation results.
Other papers have used dynamic factor models to study the effects of oil price fluctuations on the economy, but none have focused on the passthrough into consumer prices. For example, Aastveit (2014), Aastveit et al. (2015), Juvenal and Petrella (2015), and Stock and Watson (2016) study the effects of different structural oil price shocks on the economy, while An et al. (2014) study whether oil price shocks have asymmetric effects on the economy. Moreover, other papers have used dynamic factor models to analyze disaggregate prices (Cristadoro et al., 2005; Altissimo et al., 2009; Boivin et al., 2009; Reis and Watson, 2010; De Graeve and Walentin, 2015, among others), but none have used these models to study the oil price passthrough. Finally, Gao et al. (2014) study the effect of oil price shocks on a number of disaggregate U.S. consumer prices using VAR techniques; they find a significant effect only on the price of energyintensive goods but do not distinguish between macroeconomic and idiosyncratic effects.
The rest of the paper proceeds as follows. Section 2 presents the methodology. Section 3 presents the empirical analysis on the U.S., namely: Section 3.1 describes the data used, and Section 3.2 discusses common and idiosyncratic dynamics in U.S. PCE prices. Then, Section 3.3 presents estimates of the oil price passthrough, and Section 3.4 presents subsample analysis. Finally, Section 4 presents the empirical analysis on the euro area, and Section 5 briefly summarizes the results.

THE ECONOMETRIC FRAMEWORK
The goal of this paper is to quantify the effect of oil price changes on core price inflation. More precisely, we aim to disentangle the specific (idiosyncratic) effect that an oil price change might have on each disaggregate price, from its macroeconomic (common) effect that an oil price change has on all prices. To do so, we first estimate a dynamic factor model on a panel of price indicators to separate common from idiosyncratic price changes, and then use VAR techniques to estimate the passthrough.
Factor models are based on the idea that fluctuations in disaggregate prices are due to a few common (macroeconomic) shocks ([u.sub.t]) that affect all prices, and to several idiosyncratic shocks ([e.sub.t]), resulting from sectorspecific dynamics or from sampling error, which influence one or a few of them. Accordingly, each price component in the dataset can be decomposed into a common part [[chi].sub.it] which is a linear combination of a small number r of common factors [f.sub.t] that are driven by the common shocks, and an idiosyncratic part [[xi].sub.it] that is driven by idiosyncratic shocks. Let [mathematical expression not reproducible] be the annualized monthonmonth logchange in the z'th price component at time t, where i = 1,...., n and t = l,....,T, we then have
[mathematical expression not reproducible] (1)
where [[lambda].sub.t] is a r*1 vector containing the factor loadings of the ith variable, and [mathematical expression not reproducible] Model (1) is the approximate dynamic factor model proposed by Stock and Watson (2002a,b), which is a particular case of the generalized dynamic factor model studied by Forni et al. (2000) and Forni and Lippi(2001).
It is well documented that changes in the oil price contribute to macroeconomic fluctuations (see Hooker, 1996; Barsky and Kilian, 2002; Kilian, 2008b, 2009b, among others), thus they are likely to have a macroeconomic effect on all prices. To incorporate this feature in our model, we assume that the common factors and the oil price evolve over time according to a VAR model. Let [mathematical expression not reproducible] be the monthly real oil price growth rate, then we have
[mathematical expression not reproducible] (2)
where [v.sub.t] is "the oil price shock".
At the same time, given that sectors are more or less energy intensive so that energy costs represent a larger or smaller share of total costs, a change in the oil price might have a direct effect to those disaggregate prices which production is particularly energy intensive. This points at the possibility that a change in the oil price passesthrough into core inflation also via some idiosyncratic component, and therefore we allow for the possibility that the oil price and each idiosyncratic component evolve over time according to a bivariate VAR:
[mathematical expression not reproducible] (3)
Under the assumption that all the components of [[pi].sut.t] = ([[pi].sut.1t] [[pi].sut.2t]... [[pi].sut.nt])' are stationary, the common factors, the...
Oil Price Passthrough into Core Inflation.
Author:  Conflitti, Cristina 
FREE EXCERPT
To continue reading
FREE SIGN UP
COPYRIGHT TV Trade Media, Inc.
COPYRIGHT GALE, Cengage Learning. All rights reserved.
COPYRIGHT GALE, Cengage Learning. All rights reserved.