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on Forecasting |
| By: | Sarantis Tsiaplias (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne); Chew Lian Chua |
| Abstract: | This paper investigates the forecasting performance of the diffusion index approach for the Australian economy, and considers the forecasting performance of the diffusion index approach relative to composite forecasts. Weighted and unweighted factor forecasts are benchmarked against composite forecasts, and forecasts derived from individual forecasting models. The results suggest that diffusion index forecasts tend to improve on the benchmark AR forecasts. We also observe that weighted factors tend to produce better forecasts than their unweighted counterparts. We find, however, that the size of the forecasting improvement is less marked than previous research, with the diffusion index forecasts typically producing mean square errors of a similar magnitude to the VAR and BVAR approaches. JEL Classification: C22; C53; E17 |
| Keywords: | PDiffusion indexes; Forecasting; Australia. |
| Date: | 2008–02 |
| URL: | http://d.repec.org/n?u=RePEc:iae:iaewps:wp2008n04&r=for |
| By: | Chew Lian Chua; Sarantis Tsiaplias (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne) |
| Abstract: | This paper examines whether the disaggregation of consumer sentiment data into its sub-components improves the real-time capacity to forecast GDP and consumption. A Bayesian error correction approach augmented with the consumer sentiment index and permutations of the consumer sentiment sub-indexes is used to evaluate forecasting power. The forecasts are benchmarked against both composite forecasts and forecasts from standard error correction models. Using Australian data, we find that consumer sentiment data increases the accuracy of GDP and consumption forecasts, with certain components of consumer sentiment consistently providing better forecasts than aggregate consumer sentiment data. |
| Keywords: | Bayesian; Composite forecast; Consumer sentiment; Cointegration. |
| JEL: | E27 C32 C11 |
| Date: | 2008–02 |
| URL: | http://d.repec.org/n?u=RePEc:iae:iaewps:wp2008n03&r=for |
| By: | Chun Liu; John M Maheu |
| Abstract: | How to measure and model volatility is an important issue in finance. Recent research uses high frequency intraday data to construct ex post measures of daily volatility. This paper uses a Bayesian model averaging approach to forecast realized volatility. Candidate models include autoregressive and heterogeneous autoregressive (HAR) specifications based on the logarithm of realized volatility, realized power variation, realized bipower variation, a jump and an asymmetric term. Applied to equity and exchange rate volatility over several forecast horizons, Bayesian model averaging provides very competitive density forecasts and modest improvements in point forecasts compared to benchmark models. We discuss the reasons for this, including the importance of using realized power variation as a predictor. Bayesian model averaging provides further improvements to density forecasts when we move away from linear models and average over specifications that allow for GARCH effects in the innovations to log-volatility. |
| Keywords: | power variation, bipower variation, Gibbs sampling, model risk |
| JEL: | C11 C22 G12 |
| Date: | 2008–04–03 |
| URL: | http://d.repec.org/n?u=RePEc:tor:tecipa:tecipa-313&r=for |
| By: | Dominique Guegan (Centre d'Economie de la Sorbonne et Paris School of Economics); Justin Leroux (HEC Montréal and CIRPEE) |
| Abstract: | We propose a novel methodology for forecasting chaotic systems which is based on the nearest-neighbor predictor and improves upon it by incorporating local Lyapunov exponents to correct for its inevitable bias. Using simulated data, we show that gains in prediction accuracy can be substantial. The general intuition behind to proposed method can readily be applied to other non-parametric predictors. |
| Keywords: | Chaos theory, Lyapunov exponent, logistic map, Monte Carlo simulations. |
| JEL: | C15 C22 C53 C65 |
| Date: | 2008–02 |
| URL: | http://d.repec.org/n?u=RePEc:mse:cesdoc:b08014&r=for |
| By: | Dimitrios Thomakos |
| Abstract: | In this paper I propose a novel optimal linear filter for smoothing, trend and signal extraction for time series with a unit root. The filter is based on the Singular Spectrum Analysis (SSA) methodology, takes the form of a particular moving average and is different from other linear filters that have been used in the existing literature. To best of my knowledge this is the first time that moving average smoothing is given an optimality justification for use with unit root processes. The frequency response function of the filter is examined and a new method for selecting the degree of smoothing is suggested. I also show that the filter can be used for successfully extracting a unit root signal from stationary noise. The proposed methodology can be extended to also deal with two cointegrated series and I show how to estimate the cointegrating coefficient using SSA and how to extract the common stochastic trend component. A simulation study explores some of the characteristics of the filter for signal extraction, trend prediction and cointegration estimation for univariate and bivariate series. The practical usefulness of the method is illustrated using data for the US real GDP and two financial time series. |
| Keywords: | cointegration, forecasting, linear filtering, singular spectrum analysis, smoothing, trend extraction and prediction, unit root. |
| Date: | 2008 |
| URL: | http://d.repec.org/n?u=RePEc:uop:wpaper:0024&r=for |
| By: | Dimitrios Thomakos |
| Abstract: | In this note I show that the method proposed in Thomakos (2008) for optimal linear filtering, smoothing and trend extraction for a unit root process can be applied with no changes when a drift parameter is added to the process. The method in the aforementioned paper is based on Singular Spectrum Analysis (SSA) and here I also derive an SSA-based consistent estimator of the drift parameter. |
| Keywords: | drift, forecasting, linear filtering, singular spectrum analysis, smoothing, trend extraction and prediction, unit root. |
| Date: | 2008 |
| URL: | http://d.repec.org/n?u=RePEc:uop:wpaper:0025&r=for |