nep-for New Economics Papers
on Forecasting
Issue of 2011‒05‒24
seven papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting Housing Prices: Dynamic Factor Model versus LBVAR Model By Li, Yarui; Leatham, David J.
  2. Forecasting Equicorrelation By Adam E Clements; Christopher A Coleman-Fenn; Daniel R Smith
  3. Modeling and forecasting realized range volatility By Massimiliano Caporin; Gabriel G. Velo
  4. Forecasting aggregate and disaggregates with common features By Antoni, Espasa; Iván, Mayo
  5. Leverage as a Predictor for Real Activity and Volatility By Robert Kollmann; Stefan Zeugner
  6. The ENSO Impact on Predicting World Cocoa Prices By Ubilava, David; Helmers, C. Gustav
  7. Weather Forecast Based Conditional Pest Management: A Stochastic Optimal Control Investigation By Lu, Liang; Elbakidze, Levan

  1. By: Li, Yarui; Leatham, David J.
    Abstract: The purpose of this paper is to compare the forecasting power of DFM and LBVAR models as they are used to forecast house price growth rates for 42 metropolitan areas in the United States. The forecasting performances of these two large-scale models are compared based on the Theil U-statistic.
    Keywords: Housing market, DFM, LBVAR, dynamic PCA, Demand and Price Analysis,
    Date: 2010
  2. By: Adam E Clements (QUT); Christopher A Coleman-Fenn (QUT); Daniel R Smith (QUT)
    Abstract: This article examines the out-of-sample forecast performance of several timeseries models of equicorrelation, a mean of the off-diagonal elements of a covariance matrix. Building on the existing Dynamic Conditional Correlation and Linear Dynamic Equicorrelation models, we propose adapting the latter to include measures of equicorrelation based on high-frequency intraday data, as well as a forecast of equicorrelation implied by the options market. Using state-of-the-art statistical evaluation technology, we find that the use of both the realised measures and the implied equicorrelation outperform those models that use daily data alone. However, the out-of-sample forecasting benefits of implied equicorrelation disappear when used in conjunction with the realised measures.
    Date: 2011–04–01
  3. By: Massimiliano Caporin (University of Padova); Gabriel G. Velo (University of Padova)
    Abstract: In this paper, we estimate, model and forecast Realized Range Volatility, a new realized measure and estimator of the quadratic variation of financial prices. This estimator was early introduced in the literature and it is based on the high-low range observed at high frequency during the day. We consider the impact of the microstructure noise in high frequency data and correct our estimations, following a known procedure. Then, we model the Realized Range accounting for the well-known stylized effects present in financial data. We consider an HAR model with asymmetric effects with respect to the volatility and the return, and GARCH and GJR-GARCH specifications for the variance equation. Moreover, we also consider a non Gaussian distribution for the innovations. The analysis of the forecast performance during the different periods suggests that including the HAR components in the model improve the point forecasting accuracy while the introduction of asymmetric effects only leads to minor improvements.
    Keywords: Statistical analysis of financial data, Econometrics, Forecasting methods, Time series analysis, Realized Range Volatility, Realized Volatility, Long-memory, Volatility forecasting
    JEL: C22 C52 C53
    Date: 2011–02
  4. By: Antoni, Espasa; Iván, Mayo
    Abstract: The paper is focused on providing joint consistent forecasts for an aggregate and all its components and in showing that this indirect forecast of the aggregate is at least as accurate as the direct one. The procedure developed in the paper is a disaggregated approach based on single-equation models for the components, which take into account common stable features which some components share between them. The procedure is applied to forecasting euro area, UK and US inflation and it is shown that its forecasts are significantly more accurate than the ones obtained by the direct forecast of the aggregate or by dynamic factor models. A by-product of the procedure is the classification of a large number of components by restrictions shared between them, which could be also useful in other respects, as the application of dynamic factors, the definition of intermediate aggregates or the formulation of models with unobserved components
    Keywords: Common trends, Common serial correlation, Inflation, Euro Area, UK, US, Cointegration, Single-equation econometric models
    Date: 2011–04
  5. By: Robert Kollmann; Stefan Zeugner
    Abstract: This paper explores the link between the leverage of the US financial sector, of households and non-financial businesses, and real activity. We document that leverage is negatively correlated with the future growth of real activity, and positively linked to the conditional volatility of future real activity and of equity returns. The joint information in sectoral leverage series is more relevant for predicting future real activity than the information contained in any individual leverage series. Using in-sample regressions and out-of sample forecasts, we show that the predictive power of leverage is roughly comparable to that of macro and financial variables commonly used by forecasters. Leverage information would not have allowed to predict thr "Great Recession" of 2008-2009 any better than macro/financial predictors.
    Keywords: Leverage; Financial crisis; Forecasts; Real activity; Volatility
    JEL: E32 E37 C53 G20
    Date: 2011–04
  6. By: Ubilava, David; Helmers, C. Gustav
    Abstract: Cocoa beans are produced in equatorial and sub-equatorial regions of West Africa, Southeast Asia and South America. These are also the regions most affected by El Nino Southern Oscillation (ENSO) -- a climatic anomaly affecting temperature and precipitation in many parts of the world. Thus, ENSO, has a potential of affecting cocoa production and, subsequently, prices on the world market. This study investigates the benefits of using a measure of ENSO variable in world cocoa price forecasting through the application of a smooth transition autoregression (STAR) modeling framework to monthly data to examine potentially nonlinear dynamics of ENSO and cocoa prices. The results indicate that the nonlinear models appear to outperform linear models in terms of out-of-sample forecasting accuracy. Furthermore, the results of this study indicate evidence of Granger causality between ENSO and cocoa prices.
    Keywords: Cocoa Prices, El Nino Southern Oscillation, Out-of-Sample Forecasting, Smooth Transition Autoregression, Demand and Price Analysis, Environmental Economics and Policy, Research Methods/ Statistical Methods, C32, Q11, Q54,
    Date: 2011
  7. By: Lu, Liang; Elbakidze, Levan
    Abstract: In this paper, we examine conditional, forecast-based dynamic pest management in agricultural crop production given stochastic pest infestations and stochastic climate dynamics throughout the growing season. Using stochastic optimal control we show that correlation between forecast error for climate prediction and forecast error for pest outbreaks can be used to improve pesticide application efficiency. In the general setting, we apply modified Hamiltonian approach to discuss the steady state equilibrium. Given specific functional forms, a closed form solution can be found for the stochastic optimal control problem. Moreover, we find conditions for model parameters so that the optimal pesticide usage path will be monotonically increasing or decreasing in the correlation coefficient between climate forecast errors and pest growth disturbances.
    Keywords: Pest Management, Stochastic Optimal Control, Production Economics,
    Date: 2011

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