This paper is concerned with the apparent change in the U.S. oil price-macroeconomy relationship. It is investigated to what extent this change can be accounted for by the large oil price surges witnessed in the 1970s. The innovative approach of rolling impulse responses is applied and both the...

This paper presents a generalized two-step maximum likelihood estimation method for partially identified vector autoregressive models. We suggest a likelihood ratio test for over-identification in a sub-system and derive the asymptotics for impulse responses and forecast-error variance...

This working paper presents a new coincident economic indicator developed by the COE, able to detect in real time peaks and troughs of the american business cycle. This probabilistic indicator is based on the Markov-Switching model proposed by Hamilton (1989), applied to various economic time...

Vector autoregressive models are increasingly being used in the analysis of relationships within and between financial markets. In such models, there are circumstances that require zero entries in the coefficient matrices. Such circumstances can be particularly relevant in the context of markets...

We address the issue of modelling and forecasting macroeconomic variables using medium and large datasets, by adopting VARMA models. We overcome the estimation issue that arises with this class of models by implementing an iterative ordinary least squares (IOLS) estimator. We establish the...

This paper estimates NAIRU (Non-Accelerating Inflation Rate of Unemployment) for the Turkish economy as an unobserved stochastic variable by systems approach. Based on a Phillips curve equation combined with an Okun law for output gap and unemployment gap, the systems approach imposes stochastic...

This paper estimates NAIRU (Non-Accelerating Inflation Rate of Unemployment) for the Turkish economy by systems approach, which includes an Okun-type relationship between output gap and unemployment gap, a Phillips curve equation, stochastic laws of motion for NAIRU and potential output and...

This paper focuses on finding starting-values for maximum likelihood estimation of Vector STAR models. Based on a Monte Carlo exercise, different procedures are evaluated. Their performance is assessed w.r.t. model fit and computational effort. I employ i) grid search algorithms, and ii)...

We describe some fast algorithms for reconciling large collections of time series forecasts with aggregation constraints. The constraints arise due to the need for forecasts of collections of time series with hierarchical or grouped structures to add up in the same manner as the observed time...