This article provides guidance on how to evaluate and improve the forecasting ability of models in the presence of instabilities, which are widespread in economic time series. Empirically relevant examples include predicting the financial crisis of 2007–08, as well as, more broadly, fluctuations in asset prices, exchange rates, output growth, and inflation. In the context of unstable environments, I discuss how to assess models’ forecasting ability; how to robustify models’ estimation; and how to correctly report measures of forecast uncertainty. Importantly, and perhaps surprisingly, breaks in models’ parameters are neither necessary nor sufficient to generate time variation in models’ forecasting performance: thus, one should not test for breaks in models’ parameters, but rather evaluate their forecasting ability in a robust way. In addition, local measures of models’ forecasting performance are more appropriate than traditional, average measures.

The main goal of this article is to provide an answer to the question: does anything forecast exchange rates, and if so, which variables? It is well known that exchange rate fluctuations are very difficult to predict using economic models, and that a random walk forecasts exchange rates better than any economic model (the Meese and Rogoff puzzle). However, the recent literature has identified a series of fundamentals/methodologies that claim to have resolved the puzzle. This article provides a critical review of the recent literature on exchange rate forecasting and illustrates the new methodologies and fundamentals that have been recently proposed in an up-to-date, thorough empirical analysis. Overall, our analysis of the literature and the data suggests that the answer to the question: “Are exchange rates predictable?” is, “It depends”—on the choice of predictor, forecast horizon, sample period, model, and forecast evaluation method. Predictability is most apparent when one or more of the following hold: the predictors are Taylor rule or net foreign assets, the model is linear, and a small number of parameters are estimated. The toughest benchmark is the random walk without drift.

This paper investigates the sources of the substantial decrease in output growth volatility in the mid-1980s by identifying which of the structural parameters in a representative New Keynesian and structural VAR models changed. Overturning conventional wisdom, we show that the Great Moderation was due not only to changes in shock volatilities but also to changes in monetary policy parameters, as well as in the private sector's parameters. The Great Moderation was previously attributed to good luck because the alternative sources of instabilities appear to have offsetting effects on output volatility and therefore were impossible to detect using existing techniques.

We propose new indices to measure macroeconomic uncertainty. The indices measure how unexpected a realization of a representative macroeconomic variable is relative to the unconditional forecast error distribution. We use forecast error distributions based on the nowcasts and forecasts of the Survey of Professional Forecasters. We further compare the new indices with those proposed in the literature and assess their macroeconomic impact.

We propose a theoretical framework for assessing whether a forecast model estimated over one period can provide good forecasts over a subsequent period. We formalize this idea by defining a forecast breakdown as a situation in which the out-of-sample performance of the model, judged by some loss function, is significantly worse than its in-sample performance. Our framework, which is valid under general conditions, can be used not only to detect past forecast breakdowns but also to predict future ones. We show that main causes of forecast breakdowns are instabilities in the data-generating process and relate the properties of our forecast breakdown test to those of structural break tests. The empirical application finds evidence of a forecast breakdown in the Phillips' curve forecasts of U.S. inflation, and links it to inflation volatility and to changes in the monetary policy reaction function of the Fed.

Taking advantage of key recent advances in long-run economic and financial data, we analyze the statistical properties of global long-maturity real interest rates over the past seven centuries. In contrast to existing consensus, we find that real interest rates are in fact trend stationary and exhibit a persistent downward trend since the Renaissance. We investigate structural breaks in real interest rates over time and find that overall the Black Death and the 1557 “Trinity default” appear as consistent inflection points. We further show that demographic and productivity factors do not represent convincing drivers of real interest rates over long spans.

We show that “commodity currency” exchange rates have surprisingly robust power in predicting global commodity prices, both in-sample and out-of-sample, and against a variety of alternative benchmarks. This result is of particular interest to policy makers, given the lack of deep forward markets in many individual commodities, and broad aggregate commodity indices in particular. We also explore the reverse relationship (commodity prices forecasting exchange rates) but find it to be notably less robust. We offer a theoretical resolution, based on the fact that exchange rates are strongly forward-looking, whereas commodity price fluctuations are typically more sensitive to short-term demand imbalances.