continuously or with some jumps. This view is widely held in the forecasting literature and under this view, the time series … contemporary forecasting methods is compared to ours using a number of macroeconomic data. …

distribution theory involves cube-root asymptotics and it is used to shed light on forecasting practice. We show that the … conventional forecasting methods do not necessarily produce the best forecasts in our setting. We also propose a new forecasting … strategy, which incorporates our new distribution theory, and apply our forecasting method to numerous macroeconomic data. The …

decomposing, smoothing and forecasting two-dimensional sparse data. In some ways, ROPES is similar to Ridge Regression, the LASSO … practical method of forecasting mortality rates, as well as a new method for interpolating and extrapolating sparse longitudinal …

forecasting errors in realized volatility are substantive. Even though returns standardized by ex post quadratic variation …

returns and intradaily squared returns for forecasting horizons rangingfrom 1 to 10 days. For the daily squared returns we …

In this paper we consider modeling and forecasting of large realized covariance matrices by penalized vector …