Large fluctuations in energy prices have been a distinguishing characteristic of the U.S. economy since the 1970s. Turmoil in the Middle East, rising energy prices in the United States, and evidence of global warming recently have reignited interest in the link between energy prices and economic performance. This paper addresses a number of the key issues in this debate: What are energy price shocks and where do they come from? How responsive is energy demand to changes in energy prices? How do consumer's expenditure patterns evolve in response to energy price shocks? How do energy price shocks affect U.S. real output, inflation, and stock prices? Why do energy price increases seem to cause recessions but energy price decreases do not seem to cause expansions? Why has there been a surge in the price of oil in recent years? Why has this new energy price shock not caused a recession so far? Have the effects of energy price shocks waned since the 1980s and, if so, why? As the paper demonstrates, it is critical to account for the endogeneity of energy prices and to differentiate between the effects of demand and supply shocks in energy markets when answering these questions.

The paper proposes a new measure of exogenous oil supply shocks. The timing, the magnitude, and the sign of this measure may differ greatly from current state-of-the-art estimates. It is shown that only a small fraction of the observed oil price increases during oil crisis periods can be attributed to exogenous oil production disruptions. Exogenous oil supply shocks cause a sharp drop of U.S. real GDP growth after five quarters rather than an immediate and sustained reduction in economic growth and a spike in CPI inflation after three quarters. Overall, exogenous oil supply shocks made remarkably little difference for the evolution of the U.S. economy since the 1970s, although they did matter for some historical episodes.

A Monte Carlo analysis of the coverage accuracy and average length of alternative bootstrap confidence intervals for impulse-response estimators shows that the accuracy of equal-tailed and symmetric percentile-t intervals can be poor and erratic in small samples (both in models with large roots and in models without roots near the unit circle). In contrast, some percentile bootstrap intervals may be both shorter and more accurate. The accuracy of percentile-t intervals improves with sample size, but the sample size required for reliable inference can be very large. Moreover, for such large sample sizes, virtually all bootstrap intervals tend to have excellent coverage accuracy.

Bias-corrected bootstrap confidence intervals explicitly account for the bias and skewness of the small-sample distribution of the impulse response estimator, while retaining asymptotic validity in stationary autoregressions. Monte Carlo simulations for a wide range of bivariate models show that in small samples bias-corrected bootstrap intervals tend to be more accurate than delta method intervals, standard bootstrap intervals, and Monte Carlo integration intervals. This conclusion holds for VAR models estimated in levels, as deviations from a linear time trend, and in first differences. It also holds for random walk processes and cointegrated processes estimated in levels. An empirical example shows that bias-corrected bootstrap intervals may imply economic interpretations of the data that are substantively different from standard methods.

Shocks to the real price of oil may reflect oil supply shocks, shocks to the global demand for all industrial commodities, or demand shocks that are specific to the crude oil market. Each shock has different effects on the real price of oil and on US macroeconomic aggregates. Changes in the composition of shocks help explain why regressions of macroeconomic aggregates on oil prices tend to be unstable. Evidence that the recent surge in oil prices was driven primarily by global demand shocks helps explain why this shock so far has failed to cause a major recession in the United States.

We propose a formal test of the hypothesis that energy prices are predetermined with respect to U.S. macroeconomic aggregates. The test is based on regressing changes in daily energy prices on daily news from U.S. macroeconomic data releases. Using a wide range of macroeconomic news, we find no compelling evidence of feedback at daily or monthly horizons, contradicting the view that energy prices respond instantaneously to macroeconomic news and consistent with the commonly used identifying assumption that there is no feedback from U.S. macroeconomic aggregates to monthly innovations in energy prices.

We compare the finite-sample performance of impulse response confidence intervals based on local projections (LPs) and vector autoregressive (VAR) models in linear stationary settings. We find that in small samples, the asymptotic LP interval often is less accurate than the bias-adjusted bootstrap VAR interval, notwithstanding its excessive average length. Although the asymptotic LP interval has adequate coverage in sufficiently large samples, its average length still far exceeds that of bias-adjusted bootstrap VAR intervals with comparable accuracy. Bootstrap LP intervals (with or without bias correction) and asymptotic VAR intervals are shorter on average, but they often lack coverage accuracy in finite samples.

A direct consequence of restricting the price of a good for which secondary markets do not exist is that, in the presence of excess demand, the good will not be allocated to the buyers who value it the most. We demonstrate the empirical importance of this allocative cost for the U.S. residential market for natural gas, which was subject to price ceilings during 1954–89. Using a household-level, discrete-continuous model of natural gas demand, we estimate that the allocative cost in this market averaged $3.6 billion annually, nearly tripling previous estimates of the net welfare loss to U.S. consumers.