Forecasting commodity price indexes using macroeconomic and financial predictors

Using a long sample of commodity spot price indexes over the period 1947–2010, we examine the out-of-sample predictability of commodity prices by means of macroeconomic and financial variables. Commodity currencies are found to have some predictive power at short (monthly and quarterly) forecast horizons, while growth in industrial production and the investment–capital ratio have some predictive power at longer (yearly) horizons. Commodity price predictability is strongest when based on multivariate approaches that account for parameter estimation error. Commodity price predictability varies substantially across economic states, being strongest during economic recessions.     

Global recessions as a cascade phenomenon with interacting agents

This paper examines global recessions as a cascade phenomenon. In other words, how recessions arising within one or more countries might percolate across a network of connected economies. An agent based model is set up in which the agents are Western economies. A country has a probability of entering recession in any given year and one of emerging from it the next. In addition, the agents have a threshold propensity, which varies across time, to import a recession from the agents most closely connected to them. The agents are connected on a network, and an agent’s neighbours at any time are either in (state 1) or out (state 0) of recession. If the weighted sum exceeds the threshold, the agent also goes into recession. Annual real GDP growth for 17 Western countries 1871–2006 is used as the data set. The model is able to replicate three key features of the statistical distribution of recessions: the distribution of the number of countries in recession in any given year, the duration of recessions within the individual countries, and the distribution of ‘wait time’ between recessions i.e. the number of years between them. The network structure is important for the interacting agents to replicate the stylised facts. The country-specific probabilities of entering and emerging from recession by themselves give results which are by no means as well matched to the actual data. We are grateful to an anonymous referee for some extremely helpful comments.     

The macroeconomic effects of uncertainty shocks: The role of the financial channel

This paper studies the macroeconomic effects of uncertainty shocks with an emphasis on the interaction between elevated uncertainty and credit market conditions when the economy is in different regimes (recessions vs. non-recessions). We use a smooth-transition factor-augmented vector autoregression (ST-FAVAR) and a large monthly panel of U.S. macroeconomic and financial indicators in our estimation. Our findings are twofold. First, while an unanticipated increase in uncertainty has adverse effects on the real economy and financial markets, the effects are quantitatively larger during recessions. Second, the financial channel is important in the transmission of uncertainty shocks, with a greater role during recessions and in the short run.     

Financial stress and economic contractions

This paper examines why some financial stress episodes lead to economic downturns. The paper identifies episodes of financial turmoil in advanced economies using a financial stress index (FSI), and proposes an analytical framework to assess the impact of financial stress – in particular banking distress – on the real economy. It concludes that financial turmoil characterized by banking distress is more likely to be associated with deeper and longer downturns than stress mainly in securities or foreign exchange markets. Economies with more arm's-length financial systems seem to be more exposed to contractions in activity following financial stress, due to the greater procyclicality of leverage in their banking systems.     

Did established Early Warning Signals predict the 2008 crises?

Over the past 60 years, a voluminous literature has painstakingly developed crises theories and their associated Early Warning Signals. The hallmark of this literature is the consistency with which selected Early Warning Signals, such as the level of reserves and exchange rate appreciations, are presumed to predict different types of crises across countries and time. The diversity of crises theories that motivate Early Warning Signals presents, however, a challenge to empirical implementations. Given that the true model of Early Warning Signals is unknown, omitted variable bias contaminates estimates and confidence levels when the uncertainty surrounding a particular theory has been ignored. After addressing model uncertainty in Early Warning Signal regressions, using an extended version of Frankel and Saravelos (2012. J. Int. Econ. 87, 216–231) dataset, we do not find a single Early Warning Signal that alerts to all dimensions of the 2008 crisis. Instead, distinct sets of Early Warning Signals identify different dimensions of the crisis: banking, balance of payments, exchange rate pressure, and recession.     

Improved recession dating using stock market volatility

We offer an improved dating of U.S. business cycle turning points both retrospectively and in real time. This improvement is made possible by augmenting existing Markov-switching dynamic factor models with additional information on the stock return volatility. The model improves the prediction of the state of the economy using fully revised data significantly. Real-time identification can be made noticeably earlier than the NBER announcements, beating both the peak and trough announcements for recent recessions by several months.     

Uncertainty shocks and unemployment dynamics in U.S. recessions

What are the effects of uncertainty shocks on unemployment dynamics? We answer this question by estimating non-linear (Smooth-Transition) VARs with post-WWII U.S. data. The relevance of uncertainty shocks is found to be much larger than that predicted by standard linear VARs in terms of (i) magnitude of the reaction of the unemployment rate to such shocks, and (ii) contribution to the variance of the prediction errors of unemployment at business cycle frequencies. The ability of different classes of DSGE models to replicate our results is discussed.     

Interest rates,government purchases and the Taylor rule in recessions and expansions

In this paper we study asymmetries in the Taylor rule for the United States during the 1970–2012 period. We show that monetary authorities have been constantly concerned with excess demand in overheated periods – when the output gap is positive or the unemployment rate falls below 7% or 7.5% – raising the interest rate aggressively in that case. However, the Fed seems more reluctant to decrease the fund’s rate during recessions. On the contrary, monetary authorities react symmetrically and forcefully to inflation in booms and busts. Finally, we provide evidence that an expansionary fiscal policy does not lead to an increase in interest rates, and thus there is not necessary a “crowding-out” effect in recessions.     

Real-time macroeconomic forecasting with leading indicators: An empirical comparison

This paper demonstrates that the Conference Board’s Composite Leading Index (CLI) has significant real-time predictive ability for Industrial Production (IP) growth rates at horizons from one to six months ahead over the period 1989-2009. A popular but unrealistic analysis, which combines real-time data for CLI and final vintage data for IP as predictor variables, obscures the actual predictive content of the CLI, in the sense that in that case, the improvements in forecast accuracy relative to a univariate AR model are not significant. The CLI appears to be less useful for forecasting growth rates of the Conference Board’s Composite Coincident Index (CCI) in real time, as a univariate AR model performs better. This result is mostly due to its disappointing performance during the first five years of the forecast period. The CLI may not be the best way of exploiting the information contained in the underlying individual leading indicator variables. The use of principal components instead of CLI leads to more accurate real-time forecasts for both IP and CCI growth rates.