We use a Taylor rule with time-varying policy coefficients in combination with an unobserved components model for the output gap to estimate the uncertainty about future values of the Federal Funds Rate. The model makes it possible to separate ex-ante interest rate uncertainty into three...

The Kalman Filter is used to estimate a structural time-series model of cotton supply for 30 countries and 16 aggregated regions. Estimated short run supply elasticities with respect to the world price are presented for all 46 countries and regions. While they are broadly within the expected...

This paper outlines the practical steps which need to be undertaken to use autoregressive integrated moving average (ARIMA) time series models for forecasting Irish inflation. A framework for ARIMA forecasting is drawn up. It considers two alternative approaches to the issue of identifying ARIMA...

In this paper, we aim at forecasting the stochastic volatility of key financial market variables with the Kalman filter using stochastic models developed by Taylor (1986,1994) and Nelson (1990). First, we compare a stochastic volatility model relying on the Kalman filter to the conditional...

This paper uses the cross-sectional variance of the betas from the CAPM model to study herd behavior towards market index in Romania. For time-varying beta determination, three different modeling techniques are employed: two bivariate GARCH models (DCC and FIDCC GARCH), two Kalman filter based...

This paper revisits the empirical existence of the Phillips curve in the Indian context. To estimate the Phillips curve we need two variables – inflation and the output gap. In the case of India, incorrect measurement of both variables causes much difficulty in estimating the Phillipscurve. We...

This note presents a simple generalization of the adaptive expectations mechanism in which the learning parameter is time variant. It is shown that expectations generated in this way are rational in the sense of producing minimum mean squared forecast errors for a broad class of time series...

This aim of this study is to estimate the price of coffee and cocoa using a methodology based on Hodrick-Prescott filter, Kalman filter and a Markov Switching Model which, unlike linear models, allows the parameters to vary depending on the economic situation, the transitions between regimes are...

The standard state space model (SSM) treats observations as imprecise measures of the Markov latent states. Our flexible SSM treats the states and observables symmetrically, which are simultaneously determined by historical observations and up to first-lagged states. The only distinction between...

The paper presents estimations of the informal economy size in Bolivia from an application of a Dynamic General Equilibrium Model. The parameter estimation is performed using maximum likelihood method to obtain, as an intermediate result, a latent variable estimation of the informal economy...