This paper examines real exchange rate responses to shocks in exchange rate determinants and monetary policy for eight Asian developing countries. The analysis is based on a panel pseudo-Bayesian structural vector error correction model, and the shocks are identified using sign and zero...

This paper establishes vector autoregressive moving average (VARMA) models for Malaysian monetary policy analysis by efficiently identifying and simultaneously estimating the model parameters using full information maximum likelihood. The monetary literature is largely dominated by vector...

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...

Estimation in two classes of popular models, single-index models and partially linear single-index models, is studied in this paper. Such models feature nonstationarity. Orthogonal series expansion is used to approximate the unknown integrable link function in the models and a profile approach...

In this paper, we argue that there is no compelling reason for restricting the class of multivariate models considered for macroeconomic forecasting to VARs given the recent advances in VARMA modelling methodology and improvements in computing power. To support this claim, we use real...

This paper develops an EM algorithm for the estimation of a consumer demand system involving variably aggregated data. The methodology is based on the observation that more highly aggregated data does in fact contain information on the finer subcategories. It is therefore possible, under certain...

We present an empirical analysis of a long run Purchasing Power Parity (PPP) for thirteen Asian-Pacific countries using cointegration techniques.

In many applications, there are multiple time series that are hierarchically organized and can be aggregated at several different levels in groups based on products, geography or some other features. We call these "hierarchical time series". They are commonly forecast using either a "bottom-up"...

Autoregressive models are commonly employed to analyze empirical time series. In practice, however, any autoregressive model will only be an approximation to reality and in order to achieve a reasonable approximation and allow for full generality the order of the autoregression, h say, must be...

Using vector autoregressive (VAR) models and Monte-Carlo simulation methods we investigate the potential gains for forecasting accuracy and estimation uncertainty of two commonly used restrictions arising from economic relationships. The first reduces parameter space by imposing long-term...