We study the relationship between conditional quantiles of returns and the long-, medium- and short-term volatility in a portfolio of financial assets. We argue that the combination of quantile panel regression and wavelet decomposition of the volatility time series provides us with new insights...

We augment the existing literature using the Log-Periodic Power Law Singular (LPPLS) structures in the log-price dynamics to diagnose financial bubbles by providing three main innovations. First, we introduce the quantile regression to the LPPLS detection problem. This allows us to disentangle...

This paper reinterprets Maganelli's (2009) idea of "Forecasting with Judgment" to obtain a dynamic algorithm for combining survey data and time series models for macroeconomic forecasting. Unlike existing combination approaches which typically assign weights to alternative forecasts, the...

This paper considers the problem of prediction in a panel data regression model with spatial auto-correlation in the context of a simple demand equation for liquor. This is based on a panel of 43 states over the period 1965-1994. The spatial auto-correlation due to neighboring states and the...