This article proposes a modified method for the construction of diffusion indexes in macroeconomic forecasting using principal component regres- sion. The method aims to maximize the amount of variance of the origi- nal predictor variables retained by the diffusion indexes, by matching the data...

Realized variance, being the summation of squared intra-day returns, has quickly gained popularity as a measure of daily volatility. Following Parkinson (1980) we replace each squared intra-day return by the high-low range for that period to create a novel and more efficient estimator called the...

This paper demonstrates that the class of conditionally linear and Gaussian state-space models offers a general and convenient framework for simultaneously handling nonlinearity, structural change and outliers in time series. Many popular nonlinear time series models, including threshold, smooth...

Forecasting with many predictors is of interest, for instance, in macroeconomics and finance. This paper compares two methods for dealing with many predictors, that is, principal component regression (PCR) and principal covariate regression (PCovR). The forecast performance of these methods is...

We analyse the impact of the Engle and Granger (1987) article by its citations over time, and find evidence of a second life starting in the new millennium. Next, we propose a possible explanation of the success of this citation classic. We argue that the conditions for its success were just...

Two important empirical features of monthly US unemployment are that shocks to the series seem rather persistent and that unemployment seems to rise faster in recessions than that it falls during expansions. To jointly capture these features of long memory and nonlinearity, respectively, we put...

We develop a formal statistical approach to investigate the possibility that leading indicator variables have different lead times at business cycle peaks and troughs. For this purpose, we propose a novel Markov switching vector autoregressive model, where economic growth and leading indicators...

This paper develops a return forecasting methodology that allows for instabil ity in the relationship between stock returns and predictor variables, for model uncertainty, and for parameter estimation uncertainty. The predictive regres sion speci¯cation that is put forward allows for...

We develop a parsimonious panel model for quarterly regional house prices, for which both the cross-section and the time series dimension is large. The model allows for stochastic trends, cointegration, cross-equation correlations and, most importantly, latent-class clustering of regions. Class...

This paper is concerned with time series forecasting in the presence of a large number of predictors. The results are of interest, for instance, in macroeconomic and financial forecasting where often many potential predictor variables are available. Most of the current forecast methods with many...