Forecasting macroeconomic variables using a structural state space model

This paper has a twofold purpose; the first is to present a small macroeconomic model in state space form, the second is to demonstrate that it produces accurate forecasts. The first of these objectives is achieved by fitting two forms of a structural state space macroeconomic model to Australian data. Both forms model short and long run relationships. Forecasts from these models are subsequently compared to a structural vector autoregressive specification. This comparison fulfills the second objective demonstrating that the state space formulation produces more accurate forecasts for a selection of macroeconomic variables.State space, multivariate time series, macroeconomic model, forecast, SVAR

A multivariate innovations state space Beveridge Nelson decomposition

The Beveridge Nelson vector innovation structural time series framework is new formu- lation that decomposes a set of variables into their permanent and temporary components. The framework models inter-series relationships and common features in a simple man- ner. In particular, it is shown that this new speci¯cation is more simple than conventional state space and cointegration approaches. The approach is illustrated using a trivariate data set comprising the GD(N)P of Australia, America and the UK.vector innovation structural time series; multivariate time series; Bev- eridge Nelson; common components

Forecasting Australian Macroeconomic variables, evaluating innovations state space approaches

Innovations state space time series models that encapsulate the exponential smoothing methodology have been shown to be an accurate forecasting tool. These models for the first time are applied to Australian macroeconomic data. In addition new multivariate specifications are outlined and demonstrated to be accurate.exponential smoothing, state space models, multivariate time series, macroeconomic variables

Multivariate exponential smoothing for forecasting tourist arrivals to Australia and New Zealand

In this paper we propose a new set of multivariate stochastic models that capture time varying seasonality within the vector innovations structural time series (VISTS) framework. These models encapsulate exponential smoothing methods in a multivariate setting. The models considered are the local level, local trend and damped trend VISTS models with an additive multivariate seasonal component. We evaluate their performances for forecasting international tourist arrivals from eleven source countries to Australia and New Zealand.Holt-Winters’ method, Stochastic seasonality, Vector innovations state space models.

A re-appraisal of the fertility response to the Australian baby bonus

The Australian baby bonus offering parents $3,000 on the birth of a new child was announced on May 11 2004. The availability of five years of birth data following the introduction of the baby bonus allows for a more comprehensive analysis of the policy implications than is current in the literature. The focus of this paper is to identify if there is a positive fertility choice response to the introduction of the Australian baby bonus policy and if this response is sustained over time. To do this 19 years of birth and macroeconomic data, beginning 1990, is analysed using an unobservable components model. The results indicate a significant increase in birth numbers ten months following the announcement of the baby bonus, and this overall increase was sustained up to the end of the observed period. A cumulative growth in birth numbers which commenced in January 2006 slows in 2008 and 2009. It is suggested that the initial increase in births, identified in March 2005, is a direct fertility response to the introduction of the policy and that the subsequent change in the growth of birth numbers may be the result of a delayed effect working through a number of channels. It is estimated that approximately 119,000 births are attributable to the baby bonus over the period, at an approximate cost of$39000 per extra child.Fertility Rate, Time Series, baby bonus

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.

Forecasting macroeconomic variables using a structural state space model

This paper has a twofold purpose; the first is to present a small macroeconomic model in state space form, the second is to demonstrate that it produces accurate forecasts. The first of these objectives is achieved by fitting two forms of a structural state space macroeconomic model to Australian data. Both forms model short and long run relationships. Forecasts from these models are subsequently compared to a structural vector autoregressive specification. This comparison fulfills the second objective demonstrating that the state space formulation produces more accurate forecasts for a selection of macroeconomic variables

A multivariate innovations state space Beveridge Nelson decomposition

The Beveridge Nelson vector innovation structural time series framework is new formu- lation that decomposes a set of variables into their permanent and temporary components. The framework models inter-series relationships and common features in a simple man- ner. In particular, it is shown that this new speci¯cation is more simple than conventional state space and cointegration approaches. The approach is illustrated using a trivariate data set comprising the GD(N)P of Australia, America and the UK

A multivariate innovations state space Beveridge Nelson decomposition

The Beveridge Nelson vector innovation structural time series framework is new formu- lation that decomposes a set of variables into their permanent and temporary components. The framework models inter-series relationships and common features in a simple man- ner. In particular, it is shown that this new speci¯cation is more simple than conventional state space and cointegration approaches. The approach is illustrated using a trivariate data set comprising the GD(N)P of Australia, America and the UK