|
on Econometric Time Series |
| By: | C. Emre Alper; Salih Fendoglu; Burak Saltoglu |
| Date: | 2009–04 |
| URL: | http://d.repec.org/n?u=RePEc:bou:wpaper:2009/04&r=ets |
| By: | Giovanni Caggiano (Department of Economics, University of Padua, Via del Santo 33, 35123 Padova, Italy.); George Kapetanios (Department of Economics, Queen Mary University of London, Mile End Road, London E1 4NS, United Kingdom.); Vincent Labhard (European Central Bank, Kaiserstrasse 29, D-60311 Frankfurt am Main, Germany.) |
| Abstract: | Factor based forecasting has been at the forefront of developments in the macroeconometric forecasting literature in the recent past. Despite the flurry of activity in the area, a number of specification issues such as the choice of the number of factors in the forecasting regression, the benefits of combining factor-based forecasts and the choice of the dataset from which to extract the factors remain partly unaddressed. This paper provides a comprehensive empirical investigation of these issues using data for the euro area, the six largest euro area countries, and the UK. JEL Classification: C100,C150,C530. |
| Keywords: | Factors, Large Datasets, Forecast Combinations. |
| Date: | 2009–05 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:200901051&r=ets |
| By: | Proietti, Tommaso |
| Abstract: | The Beveridge-Nelson decomposition defines the trend component in terms of the eventual forecast function, as the value the series would take if it were on its long-run path. The paper introduces the multistep Beveridge-Nelson decomposition, which arises when the forecast function is obtained by the direct autoregressive approach, which optimizes the predictive ability of the AR model at forecast horizons greater than one. We compare our proposal with the standard Beveridge-Nelson decomposition, for which the forecast function is obtained by iterating the one-step-ahead predictions via the chain rule. We illustrate that the multistep Beveridge-Nelson trend is more efficient than the standard one in the presence of model misspecification and we subsequently assess the predictive validity of the extracted transitory component with respect to future growth. |
| Keywords: | Trend and Cycle; Forecasting; Filtering. |
| JEL: | E32 E31 C52 C22 |
| Date: | 2009–04–02 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:15345&r=ets |
| By: | Caiado, Jorge; Crato, Nuno; Peña, Daniel |
| Abstract: | In statistical data analysis it is often important to compare, classify, and cluster different time series. For these purposes various methods have been proposed in the literature, but they usually assume time series with the same sample size. In this paper, we propose a spectral domain method for handling time series of unequal length. The method make the spectral estimates comparable by producing statistics at the same frequency. The procedure is compared with other methods proposed in the literature by a Monte Carlo simulation study. As an illustrative example, the proposed spectral method is applied to cluster industrial production series of some developed countries. |
| Keywords: | Autocorrelation function; Cluster analysis; Interpolated periodogram; Reduced periodogram; Spectral analysis; Time series; Zero-padding. |
| JEL: | C32 C0 |
| Date: | 2009–04 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:15310&r=ets |