Oil Prices, Gasoline Prices and Inflation Expectations: A New Model and New Facts

Abstract

The conventional wisdom that inflation expectations respond to the level of the price of oil (or the price of gasoline) is based on testing the null hypothesis of a zero slope coefficient in a static single-equation regression model fit to aggregate data. Given that the regressor in this model is not stationary, the null distribution of the t-test statistic is nonstandard, invalidating the use of the normal approximation. Once the critical values are adjusted, these regressions provide no support for the conventional wisdom. Using a new structural vector regression model, however, we demonstrate that gasoline price shocks may indeed drive one-year household inflation expectations. The model shows that there have been several such episodes since 1990. In particular, the rise in household inflation expectations between 2009 and 2013 is almost entirely explained by a large increase in gasoline prices. However, on average, gasoline price shocks account for only 39% of the variation in household inflation expectations since 1981.

The Impact of Rising Oil Prices on U.S. Inflation and Inflation Expectations in 2020-23

“…The use of a partially identified model not only dispenses with the need for potentially controversial additional identifying assumptions, but also facilitates the construction of scenarios for the identified shock. Models ( 1) and ( 2) are an extension of a lower-dimensional model examined and validated in Kilian and Zhou (2021) who show that in their setting very similar results are obtained under a range of alternative, more restrictive identifying assumptions.…”

“…This model recognizes that it is gasoline prices rather than the price of crude oil that enter the consumer basket and that the gasoline price is what matters for the determination of household inflation expectations because of its salience (see, e.g., Coibion and Gorodnichenko 2015;Binder 2018). The identification of the model builds on insights in Kilian and Zhou (2021), who show that a structural VAR model is a more natural framework to estimate the effect of gasoline price shocks on inflation and inflation expectations than reduced-form correlations. In this paper, we take the analysis a step further by discussing how scenarios about the future evolution of the price of crude oil (such as the scenario entertained by the Bank of America) may be econometrically evaluated within our structural VAR model, along with the impact of past gasoline price shocks on inflation and inflation expectations.…”

“…Our work relates to a large literature on how household survey inflation expectations respond to oil and gasoline price shocks (see, e.g., Coibion and Gorodnichenko 2015;Wong 2015;Binder 2018;Conflitti and Cristadoro 2018;Binder and Makridis 2021;Kilian and Zhou 2021). In addition, our analysis is related to earlier empirical work on how oil and gasoline price shocks are transmitted to inflation (see, e.g., Kilian 2009;Clark and Terry 2010;Kilian and Lewis 2011;Wong 2015;Conflitti and Luciani 2019;Kilian and Zhou 2021). Our analysis differs from this earlier literature not only in that we are the first to examine the major fluctuations in inflation and inflation expectations during the COVID-19 pandemic.…”

Energy Price Fluctuation and Inflation in Nigeria: A Granger Causality Analysis

“…The analysis showed that gasoline price volatility accounts for approximately 39% of the variation in household inflation. Kilian and Zhou (2020) propose that more examination is needed to determine the causes of inflation in America as gas prices alone cannot give reasonable information on inflation rate volatility.…”

The Role of the Prior in Estimating VAR Models with Sign Restrictions

“…the rationale of these restrictions and of the VAR model speci…cation can be found inKilian and Zhou (2020a) whose analysis in turn is motivated byCoibion and Gorodnichenko (2015). The model is estimated based on a conventional uniform-Gaussian-inverse Wishart prior with the prior means of all slope parameters set to zero.…”