We document the empirical properties of revisions to major macroeconomic variables in the United States. Our findings suggest that they do not satisfy simple desirable statistical properties. In particular, we find that these revisions do not have a zero mean, which indicates that the initial...

This paper proposes full-Bayes priors for time-varying parameter vector autoregressions (TVP-VARs) which are more robust and objective than existing choices proposed in the literature. We formulate the priors in a way that they allow for straightforward posterior computation, they require...

We use Bayesian factor regression models to construct a financial conditions index (FCI) for the U.S. Within this context we develop Bayesian model averaging methods that allow the data to select which variables should be included in the FCI or not. We also examine the importance of different...

Stock market return is one of financial variables that contain information to forecast real activity such as industrial production and real GDP growth. However, it is still controversial that stock market return can have a predictive content on real activity. This paper attempts to investigate...

Manufacturing Purchasing Managers Index (PMI) has been increasingly used by various central banks for assessing the direction and strength of economic activity. One of the sub-indices or component level information provided by PMI is that of industrial input and output prices trends as surveyed...

While forecasting is a common practice in academia, government and business alike, practitioners are often left wondering how to choose the sample for estimating forecasting models. When we forecast inflation in 2014, for example, should we use the last 30 years of data or the last 10 years of...

This paper develops a method for producing current-quarter forecasts of GDP growth with a (possibly large) range of available within-the-quarter monthly observations of economic indicators, such as employment and industrial production, and financial indicators, such as stock prices and interest...

We analyse the multihorizon forecasting performance of several strategies to estimate the stationary AR(1) model in a near-unity context. We focus on the Andrews' (1993) exact median-unbiased estimator (BC), the OLS estimator, and the driftless random walk (RW). In addition, we explore the...

Trend analysis and forecasting of time series data on air-pollutants is important to design effective measures to minimize damages to ecosystems and human health. In this study, autoregressive, moving average, autoregressive-moving average and autoregressive integrated moving average processes...

The volatility clustering often seen in financial data has increased the interest of researchers in applying good models to measure and forecast stock returns. This paper aims to model the volatility for daily and weekly returns of the Portuguese Stock Index PSI-20. By using simple GARCH,...