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Forecasting Australian Macroeconomic variables, evaluating innovations state space approaches
TLDR
In this article, state space time series models that encapsulate the exponential smoothing methodology have been shown to be an accurate forecasting tool for Australian macroeconomic data and new multivariate specifications are outlined and demonstrated to be accurate.Abstract:
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.read more
Citations
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Journal ArticleDOI
Forecasting australian macroeconomic variables using a large dataset
TL;DR: This article investigated the forecasting performance of the diffusion index approach for the Australian economy, and considered the forecast performance of diffusion index approaches relative to composite forecasts, and found that diffusion index forecasts tend to improve on the benchmark AR forecasts, but the size of the forecasting improvement is less marked than previous research.
References
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Book
Forecasting, Structural Time Series Models and the Kalman Filter
TL;DR: In this article, the Kalman filter and state space models were used for univariate structural time series models to estimate, predict, and smoothen the univariate time series model.
Journal Article
Optimal Filtering
TL;DR: This book helps to fill the void in the market and does that in a superb manner by covering the standard topics such as Kalman filtering, innovations processes, smoothing, and adaptive and nonlinear estimation.
Journal ArticleDOI
Forecasting Using Principal Components From a Large Number of Predictors
James H. Stock,Mark W. Watson +1 more
TL;DR: In this paper, the authors consider forecasting a single time series when there are many predictors (N) and time series observations (T), and they show that the difference between the feasible forecasts and the infeasible forecasts constructed using the actual values of the factors converges in probability to 0 as both N and T grow large.
Journal ArticleDOI
Automatic Time Series Forecasting: The forecast Package for R
Rob J. Hyndman,Yeasmin Khandakar +1 more
TL;DR: Two automatic forecasting algorithms that have been implemented in the forecast package for R, based on innovations state space models that underly exponential smoothing methods, are described.
Journal ArticleDOI
Macroeconomic Forecasting Using Diffusion Indexes
James H. Stock,Mark W. Watson +1 more
TL;DR: This paper used principal component analysis (PCA) to predict macroeconomic time series variable using a large number of predictors, and the predictors were summarized using a small number of indexes constructed by principal component analyzer.