nep-for New Economics Papers
on Forecasting
Issue of 2006‒10‒14
four papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting inflation and output: comparing data-rich models with simple rules By William T. Gavin; Kevin L. Kliesen
  2. High Dimensional Yield Curves: Models and Forecasting By Clive Bowsher; Roland Meeks
  3. Dynamic Factor GARCH: Multivariate Volatility Forecast for a Large Number of Series By Lucia Alessi; Matteo Barigozzi; Marco Capasso
  4. An Asymmetric Block Dynamic Conditional Correlation Multivariate GARCH Model By Vargas, Gregorio A.

  1. By: William T. Gavin; Kevin L. Kliesen
    Abstract: Decision makers, both public and private, use forecasts of economic growth and inflation to make plans and implement policies. In many situations, reasonably good forecasts can be made with simple rules of thumb that are extrapolations of a single data series. In principle, information about other economic indicators should be useful in forecasting a particular series like inflation or output. Including too many variables makes a model unwieldy and not including enough can increase forecast error. A key problem is deciding which other series to include. Recently, studies have shown that Dynamic Factor Models (DFMs) may provide a general solution to this problem. The key is that these models use a large data set to extract a few common factors (thus, the term #data-rich*). This paper uses a monthly DFM model to forecast inflation and output growth at horizons of 3, 12 and 24 months ahead. These forecasts are then compared to simple forecasting rules.
    Keywords: Inflation (Finance) ; Forecasting
    Date: 2006
  2. By: Clive Bowsher (Nuffield College, University of Oxford); Roland Meeks (Nuffield College, University of Oxford)
    Abstract: Functional Signal plus Noise (FSN) models are proposed for analysing the dynamics of a large cross-section of yields or asset prices in which contemporaneous observations are functionally related. The FSN models are used to forecast high dimensional yield curves for US Treasury bonds at the one month ahead horizon. The models achieve large reductions in mean square forecast errors relative to a random walk for yields and readily dominate both the Diebold and Li (2006) and random walk forecasts across all maturities studied. We show that the Expectations Theory (ET) of the term structure completely determines the conditional mean of any zero-coupon yield curve. This enables a novel evaluation of the ET in which its 1-step ahead forecasts are compared with those of rival methods such as the FSN models, with the results strongly supporting the growing body of empirical evidence against the ET. Yield spreads do provide important information for forecasting the yield curve, especially in the case of shorter maturities, but not in the manner prescribed by the Expectations Theory.
    Keywords: Yield curve, term structure, expectations theory, FSN models, functional time series, forecasting, state space form, cubic spline.
    JEL: C33 C51 C53 E47 G12
    Date: 2006–10–02
  3. By: Lucia Alessi; Matteo Barigozzi; Marco Capasso
    Abstract: We propose a new method for multivariate forecasting which combines the Generalized Dynamic Factor Model (GDFM) and the multivariate Generalized Autoregressive Conditionally Heteroskedastic (GARCH) model. We assume that the dynamic common factors are conditionally heteroskedastic. The GDFM, applied to a large number of series, captures the multivariate information and disentangles the common and the idiosyncratic part of each series; it also provides a first identification and estimation of the dynamic factors governing the data set. A time-varying correlation GARCH model applied on the estimated dynamic factors finds the parameters governing their covariances’ evolution. Then a modified version of the Kalman filter gets a more precise estimation of the static and dynamic factors’ in-sample levels and covariances. A method is suggested for predicting conditional out-of-sample variances and covariances of the original data series. Finally, we carry out an empirical application aiming at comparing volatility forecasting results of our Dynamic Factor GARCH model against the univariate GARCH.
    Keywords: Dynamic Factors, Multivariate GARCH, Covolatility Forecasting
    Date: 2006–10–02
  4. By: Vargas, Gregorio A.
    Abstract: The Block DCC model for determining dynamic correlations within and between groups of financial asset returns is extended to account for asymmetric effects. Simulation results show that the Asymmetric Block DCC model is competitive in in-sample forecasting and performs better than alternative DCC models in out-of-sample forecasting of conditional correlation in the presence of asymmetric effect between blocks of asset returns. Empirical results demonstrate that the model is able to capture the asymmetries in conditional correlations of some blocks of currencies in East Asia in the turbulent years of the late 1990s.
    Keywords: asymmetric effect; block dynamic conditional correlation; multivariate GARCH
    JEL: C32 G10 C5
    Date: 2006–01

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