Lutz Kilian

Why is it so Difficult to Beat the Random Walk Forecast of Exchange Rates

We propose an exchange rate model that can explain both the observed volatility and the persistence of real and nominal exchange rate movements and thus in some measure resolves Rogoff’s (1996) purchasing power parity puzzle. Our analysis reconciles the well-known difficulties in beating the random walk forecast model with the statistical evidence of nonlinear mean reversion in deviations from fundamentals. Our analysis also provides a compelling rationale for the long-horizon predictability of exchange rates. We find strong empirical support for long-horizon predictability, and we explain why it is difficult to exploit this predictability in real-time forecasts. Our results not only lend support to economists’ beliefs that the exchange rate is inherently predictable, but they also help us to understand the reluctance of applied forecasters to abandon chartist methods in favor of models based on economic fundamentals.

Small-sample Confidence Intervals for Impulse Response Functions

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.

The Impact of Oil Price Shocks on the U.S. Stock Market

It is shown that the reaction of U.S. real stock returns to an oil price shock differs greatly depending on whether the change in the price of oil is driven by demand or supply shocks in the oil market. The demand and supply shocks driving the global crude oil market jointly account for 22% of the long-run variation in U.S. real stock returns. The responses of industry-specific U.S. stock returns to demand and supply shocks in the crude oil market are consistent with accounts of the transmission of oil price shocks that emphasize the reduction in domestic final demand.

Exogenous Oil Supply Shocks: How Big Are They and How Much Do They Matter for the U.S. Economy?

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.

In-Sample or Out-of-Sample Tests of Predictability: Which One Should We Use?

Abstract It is widely known that significant in-sample evidence of predictability does not guarantee significant out-of-sample predictability. This is often interpreted as an indication that in-sample evidence is likely to be spurious and should be discounted. In this paper, we question this interpretation. Our analysis shows that neither data mining nor dynamic misspecification of the model under the null nor unmodelled structural change under the null are plausible explanations of the observed tendency of in-sample tests to reject the no-predictability null more often than out-of-sample tests. We provide an alternative explanation based on the higher power of in-sample tests of predictability in many situations. We conclude that results of in-sample tests of predictability will typically be more credible than results of out-of-sample tests.

Do We Really Know that Oil Caused the Great Stagflation? A Monetary Alternative

This paper argues that major oil price increases were not nearly as essential a part of the causal mechanism that generated the stagflation of the 1970s as is often thought. There is neither a theoretical presumption that oil supply shocks are stagflationary nor robust empirical evidence for this view. In contrast, we show that monetary expansions and contractions can generate stagflation of realistic magnitude even in the absence of supply shocks. Furthermore, monetary fluctuations help to explain the historical movements of the prices of oil and other commodities, including the surge in the prices of industrial commodities that preceded the 1973-1974 oil price increase. Thus, they can account for the striking coincidence of major oil price increases and worsening stagflation.

Exchange Rates and Monetary Fundamentals: What Do We Learn from Long-Horizon Regressions?

The use of a new bootstrap method for small-sample inference in long-horizon regressions is illustrated by analysing the long-horizon predictability of four major exchange rates, and the findings are reconciled with those of an earlier study by Mark (1995). While there is some evidence of exchange rate predictability, contrary to earlier studies, no evidence is found of higher predictability at longer horizons. Additional evidence is presented that the linear VEC model framework underlying the empirical study is likely to be misspecified, and that the methodology for constructing bootstrap p-values for long-horizon regression tests may be fundamentally flawed.

A Comparison of the Effects of Exogenous Oil Supply Shocks on Output and Inflation in the G7 Countries

A comparison of the effects of exogenous shocks to global crude oil production on seven major industrialized economies suggests a fair degree of similarity in the real growth responses. An exogenous oil supply disruption typically causes a temporary reduction in real GDP growth that is concentrated in the second year after the shock. Inflation responses are more varied. The median CPI inflation response peaks after three to four quarters. Exogenous oil supply disruptions need not generate sustained inflation or stagflation. Typical responses include a fall in the real wage, higher short-term interest rates, and a depreciating currency with respect to the dollar. Despite many qualitative similarities, there is strong statistical evidence that the responses to exogenous oil supply disruptions differ across G7 countries. For suitable subsets of countries, homogeneity cannot be ruled out. A counterfactual historical exercise suggests that the evolution of CPI inflation in the G7 countries would have been similar overall to the actual path even in the absence of exogenous shocks to oil production, consistent with a monetary explanation of the inflation of the 1970s. There is no evidence that the 1973-1974 and 2002-2003 oil supply shocks had a substantial impact on real growth in any G7 country, whereas the 1978-1979, 1980, and 1990-1991 shocks contributed to lower growth in at least some G7 countries. (JEL: E31, E32, Q43) (c) 2008 by the European Economic Association.

Recent developments in bootstrapping time series

In recent years, several new parametric and nonparametric bootstrap methods have been proposed for time series data. Which of these methods should applied researchers use? We provide evidence that for many applications in time series econometrics parametric methods are more accurate, and we identify directions for future research on improving nonparametric methods. We explicitly address the important, but often neglected issue of model selection in bootstrapping. In particular, we emphasize the advantages of the AIC over other lag order selection criteria and the need to account for lag order uncertainty in resampling. We also show that the block size plays an important role in determining the success of the block bootstrap, and we propose a data-based block size selection procedure.(This abstract was borrowed from another version of this item.)

The Role of Speculation in Oil Markets: What Have We Learned So Far?

A popular view is that the surge in the price of oil during 2003-08 cannot be explained by economic fundamentals, but was caused by the increased financialization of oil futures markets, which in turn allowed speculation to become a major determinant of the spot price of oil. This interpretation has been driving policy efforts to regulate oil futures markets. This survey reviews the evidence supporting this view. We identify six strands in the literature corresponding to different empirical methodologies and discuss to what extent each approach sheds light on the role of speculation. We find that the existing evidence is not supportive of an important role of speculation in driving the spot price of oil after 2003. Instead, there is strong evidence that the co-movement between spot and futures prices reflects common economic fundamentals rather than the financialization of oil futures markets.