|
on Forecasting |
| By: | Michael P. Clements (University of Warwick); Ana Beatriz Galvão (Queen Mary, University of London) |
| Abstract: | Many macroeconomic series such as US real output growth are sampled quarterly, although potentially useful predictors are often observed at a higher frequency. We look at whether a mixed data-frequency sampling (MIDAS) approach can improve forecasts of output growth. The MIDAS approach is compared to other ways of making use of monthly data to predict quarterly output growth. The MIDAS specification used in the comparison employs a novel way of including an autoregressive term. We find that the use of monthly data on the current quarter leads to significant improvement in forecasting current and next quarter output growth, and that MIDAS is an effective way of exploiting monthly data compared to alternative methods. We also exploit the best method to use the monthly vintages of the indicators for real-time forecasting. |
| Keywords: | Mixed data frequency, Coincident indicators, Real-time forecasting, US output growth |
| JEL: | C51 C53 |
| Date: | 2007–10 |
| URL: | http://d.repec.org/n?u=RePEc:qmw:qmwecw:wp616&r=for |
| By: | Kovačić, Zlatko |
| Abstract: | This paper investigates the behavior of stock returns in an emerging stock market namely, the Macedonian Stock Exchange, focusing on the relationship between returns and conditional volatility. The conditional mean follows a GARCH-M model, while for the conditional variance one symmetric (GARCH) and four asymmetric GARCH types of models (EGARCH, GJR, TARCH and PGARCH) were tested. We examine how accurately these GARCH models forecast volatility under various error distributions. Three distributions were assumed, i.e. Gaussian, Student-t and Generalized Error Distribution. The empirical results show the following: (i) the Macedonian stock returns time series display stylized facts such as volatility clustering, high kurtosis, and low starting and slow-decaying autocorrelation function of squared returns; (ii) the asymmetric models show a little evidence on the existence of leverage effect; (iii) the estimated mean equation provide only a weak evidence on the existence of risk premium; (iv) the results are quite robust across different error distributions; and (v) GARCH models with non-Gaussian error distributions are superior to their counterparts estimated under normality in terms of their in-sample and out-of-sample forecasting accuracy. |
| Keywords: | Stock market; forecasting volatility; South-Eastern Europe; GARCH models; non-Gaussian error distribution; Macedonia |
| JEL: | G12 C52 C22 |
| Date: | 2007–10–24 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:5319&r=for |
| By: | de Silva, Ashton |
| Abstract: | 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. |
| Keywords: | vector innovation structural time series; multivariate time series; Bev- eridge Nelson; common components. |
| JEL: | E32 C32 C51 |
| Date: | 2007–10 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:5431&r=for |