This paper investigates the use of DMA approach for identifying good inflation predictors and forecasting inflation in Mongolia, one of the most commodity dependent economies, using dynamic model averaging (DMA). The DMA approach allows for both set of predictors for inflation and marginal...

In this paper, we propose a Bayesian estimation and prediction procedure for noncausal autoregressive (AR) models. Specifically, we derive the joint posterior density of the past and future errors and the parameters, which gives posterior predictive densities as a byproduct. We show that the...

We propose a new measure of underlying inflation that informs, in real time, about asymmetric risks on the outlook of inflationary pressures. The asymmetries are generated through nonlinearities induced by economic activity. The new indicator is based on a multivariate regime-switching framework...

In this paper we show that the exchange rates of some commodity exporter countries have the ability to predict the price of spot and future contracts of aluminum. This is shown with both in-sample and out-of-sample analyses. The theoretical underpinning of these results relies on the...

Two Bayesian sampling schemes are outlined to estimate a K-state Markov switching model with time-varying transition probabilities. Data augmentation for the multinomial logit model of the transition probabilities is alternatively based on a random utility and a difference in random utility...

We apply classical econometric method to characterize the dynamic behavior of the quarter-on-quarter inflation over the period 1997q1-2010q1. In particular, we estimate univariate autoregressive (AR) models for the aggregate consumer price inflation series and as well as for the consumer price...

This paper revisits inflation forecasting using reduced form Phillips curve forecasts, i.e., inflation forecasts using activity and expectations variables. We propose a Phillips curve-type model that results from averaging across different regression specifications selected from a set of...

This paper develops a Bayesian quantile regression model with time-varying parameters (TVPs) for forecasting in ation risks. The proposed parametric methodology bridges the empirically established benefits of TVP regressions for forecasting in ation with the ability of quantile regression to...

Time series estimates of inflation persistence incur an upward bias if shifts in the inflation target of the central bank remain unaccounted for. Using a structural time series approach we measure different sorts of inflation persistence allowing for an unobserved timevarying inflation target....

We apply both classical and Bayesian econometric methods to characterize the dynamic behavior of inflation for twelve industrial countries over the period 1984-2003, using four different price indices for each country. In particular, we estimate a univariate autoregressive (AR) model for each...