A structural Time Series Model with Markov Switching.

We propose an innovations form of the structural model underlying exponential smoothing that is further augmented by a latent Markov switching process. A particular case of the new model is the local level model with a switching drift, where the switching component describes the change between high and low growth rate periods. This new model is used to analyse the US business cycle using US Quarterly real GNP data. Model parameters are estimated using a Gibbs sampling algorithm and subsequently used for forecasting purposes. In addition, the stability of the new model is tested against Hamilton's model over a range of observation periods.Structural models, Markov switching regime, Gibbs sampling Business cycle.

Bayesian Model Averaging and Identification of Structural Breaks in Time Series

Bayesian model averaging is used for testing for multiple break points in uni- variate series using conjugate normal-gamma priors. This approach can test for the number of structural breaks and produce posterior probabilities for a break at each point in time. Results are averaged over speciÖcations including: station- ary; stationary around trend; and, unit root models, each containing di§ erent types and numbers of breaks and di§ erent lag lengths. The procedures are used to test for structural breaks on 14 annual macroeconomic series and 11 natural resource price series. The results indicate that there are structural breaks in al l of the natural resource series and most of the macroeconomic series. Many of the series had multiple breaks. Our Öndings regarding the existence of unit roots, having al lowed for structural breaks in the data, are largely consistent with previous work.Bayesian Model Averaging, Structural Breaks, Unit Root, Macro- economic Data, Natural Resource data

The vector innovation structural time series framework: a simple approach to multivariate forecasting

The vector innovation structural time series framework is proposed as a way of modelling a set of related time series. Like all multi-series approaches, the aim is to exploit potential inter-series dependencies to improve the fit and forecasts. A key feature of the framework is that the series are decomposed into common components such as trend and seasonal effects. Equations that describe the evolution of these components through time are used as the sole way of representing the inter-temporal dependencies. The approach is illustrated on a bivariate data set comprising Australian exchange rates of the UK pound and US dollar. Its forecasting capacity is compared to other common single- and multi-series approaches in an experiment using time series from a large macroeconomic database.Vector innovation structural time series, state space model, multivariate time series, exponential smoothing, forecast comparison, vector autoregression.

MODELING U.S. BROILER SUPPLY RESPONSE: A STRUCTURAL TIME SERIES APPROACH

A structural time series model is used to estimate the supply response function for broiler production in the United States using quarterly data and a structural time series model. This model has the advantage of expressing trend and seasonal elements as stochastic components, allowing a dynamic interpretation of the results and improving the forecast capabilities of the model. The results of the estimation indicate the continued importance of feed cost to poultry production and of technology as expressed by the stochastic trend variable. However, seasonal influences appear to have become less important, since the seasonal component was not statistically significant.Livestock Production/Industries,

Investigating Inflation Dynamics and Structural Change with an Adaptive ARFIMA Approach

Previous models of monthly CPI inflation time series have focused on possible regime shifts, non-linearities and the feature of long memory. This paper proposes a new time series model, named Adaptive ARFIMA; which appears well suited to describe inflation and potentially other economic time series data. The Adaptive ARFIMA model includes a time dependent intercept term which follows a Flexible Fourier Form. The model appears to be capable of succesfully dealing with various forms of breaks and discontinities in the conditional mean of a time series. Simulation evidence justifies estimation by approximate MLE and model specfication through robust inference based on QMLE. The Adaptive ARFIMA model when supplemented with conditional variance models is found to provide a good representation of the G7 monthly CPI inflation series.ARFIMA; FIGARCH, long memory, structural change, inflation, G7.

WAGE DYNAMICS IN A STRUCTURAL TIME SERIES MODEL FOR LUXEMBOURG

This paper examines the relationships between monetary wage and its theoretical explanatory variables using a Structural Time Series (STS) model in order to take into account the unobserved components (trend, cycle, seasonal and irregular) of wage. Theoretically, the monetary wage is negatively related to labor productivity and unemployment rate but positively to the consumer price index and foreign prices. Our empirical results for a small open economy as Luxembourg indicate that the wage is positively related to the consumer price index and foreign prices as predicted by the theory, but the labor productivity and unemployment rate are not significant in the explanation of wages dynamics in the Luxembourg economy.Wage Bargaining, Labor Unions, Unobserved Components Models, Structural Time Series

Testing for Structural Breaks in Australia's Monetary Aggregates and Interest Rates: An Application of the Innovational Outlier and Additive Outlier Models

This paper employs all quarterly time series currently available to endogenously determine the timing of structural breaks for various monetary aggregates and interest rates in Australia over the last thirty years. The Innovational Outlier model (IO) and the Additive Outlier model (AO) are then used to test for nonstationarity. After accounting for the single most significant structural break, the results from both models clearly indicate that the null of at least one unit root cannot be rejected for almost all series examined. The structural breaks found coincide with important policy changes during the period of financial deregulation starting in the 1980s.Monetary aggregates, interest rates, Innovational Outlier Model, Additive Outlier Model

Estimating Price Elasticities of Supply for Cotton: A Structural Time-Series Approach

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 range in light of previous work, they indicate extensive cross-country and regional heterogeneity, as well as considerable parameter uncertainty in some cases. Finally, some proposals are made for incorporating both the core estimates and their sampling distributions into applied equilibrium models.Cotton; price elasticity of supply; structural time-series model; Kalman Filter