We construct an empirical heterogeneous agent model which optimally combines forecasts from fundamentalist and chartists agents and evaluate its out-of-sample forecast performance using daily date covering the period from January 1999 to June 2014 for six of the most widely traded currencies. We...

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...

Pakistan with highest number of terrorism related deaths of any country over the past decade, the number exceeding the total terrorism related deaths for both the European and North American continents, provides an ideal laboratory to study impact of terrorism on the macroeconomy (GTD, 2012)....

The purpose of this study is to forecast the short-term inflation rate of Bangladesh using the monthly Consumer Price Index (CPI) from January 2000 to December 2012. To do so, the study employed the Seasonal Auto-regressive Integrated Moving Average (SARIMA) models proposed by Box, Jenkins, and...

Real Business Cycle (RBC) and Dynamic Stochastic General Equilibrium (DSGE) methods have become essential components of the macroeconomist’s toolkit. This literature review stresses recently developed (often Bayesian) techniques for computation and inference, providing a supplement to the...

This paper studies the All Ordinaries Index in Australia, and its futures contract known as the Share Price Index. We use a new form of smooth transition model to account for a variety of nonlinearities caused by transaction costs and other market/data imperfections, and given the recent...

This paper develops a new approach to change-point modeling that allows the number of change-points in the observed sample to be unknown. The model we develop assumes regime durations have a Poisson distribution. It approximately nests the two most common approaches: the time varying parameter...

We exploit the richness of a large data set of daily and monthly business cycle indicators by pooling them to produce Nowcast of contemporaneous real GDP growth. We conduct predictions based on a pooling of bivariate forecasts which uses these indicators as predictors of GDP (Nowcast with...

Time varying patterns in US growth are analyzed using various univariate model structures, starting from a naive model structure where all features change every period to a model where the slow variation in the conditional mean and changes in the conditional variance are specified together with...

This research introduces an innovative GDP nowcasting strategy tailored for developing countries, specifically addressing challenges related to limited data timeliness. The study centers on Bolivia, where the official monthly indicator of economic growth is released with a substantial delay of...