Emerging-market economies have become increasingly important in driving global GDP growth over the past 10 to 15 years. This has made timely and accurate assessment of current and future economic activity in emerging markets important for policy-makers not only in these countries but also in...

Futures markets are a potentially valuable source of information about price expectations. Exploiting this information has proved difficult in practice, because time-varying risk premia often render the futures price a poor measure of the market expectation of the price of the underlying asset....

This paper evaluates the effects of high-frequency uncertainty shocks on a set of lowfrequency macroeconomic variables that are representative of the U.S. economy. Rather than estimating models at the same common low-frequency, we use recently developed econometric models, which allows us to...

The selection of upper order statistics in tail estimation is notoriously difficult. Methods that are based on asymptotic arguments, like minimizing the asymptotic MSE, do not perform well in finite samples. Here, we advance a data-driven method that minimizes the maximum distance between the...

Does extreme downside risk require a risk premium in the pricing of individual assets? Extreme downside risk is a conditional measure for the co-movement of individual stocks with the market, given that the state of the world is extremely bad. This measure, derived from statistical extreme value...

This paper focuses on econometric issues, especially the common assumption that monetary payoff is subjects' actual utility, in estimating subjects' learning behaviors using experimental data. I propose a generalized adaptive learning model that nests commonly used learning rules. First, I show...

We introduce a new approach for the estimation of high-dimensional factor models with regime-switching factor loadings by extending the linear three-pass regression filter to settings where parameters can vary according to Markov processes. The new method, denoted as Markov-switching three-pass...

This paper estimates a dynamic factor model (DFM) for nowcasting Canadian gross domestic product. The model is estimated with a mix of soft and hard indicators, and it features a high share of international data. The model is then used to generate nowcasts, predictions of the recent past and...

How does aggregate profit uncertainty influence investment activity at the firm level? We propose a parsimonious adaptation of a factor-autoregressive conditional heteroscedasticity model to exploit information in a subindustry sales panel for an efficient and tractable estimation of aggregate...

This paper introduces new weighting schemes for model averaging when one is interested in combining discrete forecasts from competing Markov-switching models. In particular, we extend two existing classes of combination schemes - Bayesian (static) model averaging and dynamic model averaging - so...