. Not only the extent but also the asymmetry of inflation risks evolve over time. Moreover, changes in this asymmetry have …

This chapter reviews the principal methods used by researchers when forecasting seasonal time series. In addition, the … seasonal time series, including merging short-term seasonal forecasts with those from long-term (nonseasonal) models. Periodic … seasonally varying in a Markov switching framework. Seasonality heteroskedasticity is considered for financial time series …

macroeconomic, financial, and news time series sampled at different frequencies, we focus on the sparse-group LASSO regularization … which can take advantage of the mixed frequency time series panel data structures. Our empirical results show the superior …-specific time series regression models, and standard machine learning methods …

-frequency data and, at the same time produces a direct forecast of the variance at the desired horizon, without iterating. The MIDAS …

large cross-section of firms with macroeconomic, financial, and news time series sampled at different frequencies, we focus … on the sparse-group LASSO regularization. This type of regularization can take advantage of the mixed frequency time …

financial data. Our analysis is designed to elucidate the value of daily information and provide real-time forecast updates of …

Skewness in Expected Macro Fundamentals and the Predictability of Equity Returns: Evidence and Theory.The paper to which these …

established predictive factors. We show that introducing time-varying skewness in the distribution of expected growth prospects in …