nep-for New Economics Papers
on Forecasting
Issue of 2013‒03‒09
eleven papers chosen by
Rob J Hyndman
Monash University

  1. Martingale unobserved component models By Neil Shephard
  2. Forecasting Aggregate Retail Sales: The Case of South Africa By Goodness C. Aye; Mehmet Balcilar; Rangan Gupta; Anandamayee Majumdar
  3. Forecasting Latin-American yield curves: An artificial neural network approach By Daniel Vela
  4. Ensemble predictions of recovery rates By Joao A. Bastos
  5. Central Bank Forecasts of Policy Interest Rates: An Evaluation of the First Years By Beechey, Meredith; Österholm, Pär
  6. Monthly US business cycle indicators: A new multivariate approach based on a band-pass filter By Marczak, Martyna; Gómez, Victor
  7. What Drives Commodity Prices? By Shu-Ling Chen; John D. Jackson; Hyeongwoo Kim; Pramesti Resiandini
  8. Does Anything Beat 5-Minute RV? A Comparison of Realized Measures Across Multiple Asset Classes By Kevin Sheppard; Lily Liu; Andrew J. Patton
  9. The influence and policy signaling role of FOMC forecasts By Paul Hubert
  10. ECB projections as a tool for understanding policy decisions By Paul Hubert
  11. It’s all about volatility (of volatility): evidence from a two-factor stochastic volatility model By Stefano Grassi; Paolo Santucci de Magistris

  1. By: Neil Shephard
    Abstract: I discuss models which allow the local level model, which rationalised exponentially weighted moving averages, to have a time-varying signal/noise ratio.  I call this a martingale component model.  This makes the rate of discounting of data local.  I show how to handle such models effectively using an auxiliary particle filter which deploys M Kalman filters run in parallel competing against one another.  Here one thinks of M as being 1,000 or more.  The model is applied to inflation forecasting.  The model generalises to unobserved component models where Gaussian shocks are replaced by martingale difference sequences.
    Keywords: Auxiliary particle filter, EM algorithm, EWMA, forecasting, Kalman filter, likelihood, martingale unobserved component model, particle filter, stochastic volatility
    JEL: C01 C14 C58 D53 D81
    Date: 2013–02–10
  2. By: Goodness C. Aye (Department of Economics, University of Pretoria); Mehmet Balcilar (Department of Economics, Eastern Mediterranean University, Famagusta, North Cyprus,via Mersin 10, Turkey); Rangan Gupta (Department of Economics, University of Pretoria); Anandamayee Majumdar (Soochow University Center for Advance Statistics and Econometric Research, Suzhou, China.)
    Abstract: Forecasting aggregate retail sales may improve portfolio investors’ ability to predict movements in the stock prices of the retailing chains. Therefore, this paper uses 26 (23 single and 3 combination) forecasting models to forecast South Africa’s aggregate seasonal retail sales. We use data from 1970:01–2012:05, with 1987:01-2012:05 as the out-of-sample period. We deviate from the uniform symmetric quadratic loss function typically used in forecast evaluation exercises. Hence, we consider loss functions that overweight forecast error in booms and recessions to check whether a specific model that appears to be a good choice on average is also preferable in times of economic stress. To this end, we use the weighted RMSE and weighted version of the Diebold-Mariano tests to evaluate the different forecasts. Focussing on the single models alone, results show that their performances differ greatly across forecast horizons and for different weighting schemes. However, the combination forecasts models in general produced better forecasts and are largely unaffected by business cycles and time horizons.
    Keywords: seasonality, weighted loss, retail sales forecasting, combination forecasts, South Africa
    JEL: C32 C53 E32
    Date: 2013–02
  3. By: Daniel Vela
    Abstract: This document explores the predictive power of the yield curves in Latin America (Colombia, Mexico, Peru and Chile) taking into account the factors set by the specifications of Nelson & Siegel and Svensson. Several forecasting methodologies are contrasted: an autoregressive model, a vector autoregressive model, artificial neural networks on each individual factor, and artificial neural networks on all factors that explain the yield curve. The out-of-sample performance of the fitting models improves with the neural networks in the one-month-ahead forecast along all studied yield curves. Moreover, the three factor model developed by Nelson & Siegel proves to be the best choice for out-of-sample forecasting. Finally, the success of the cross variable interaction strongly depends on the selected yield curve.
    Date: 2013–02–28
  4. By: Joao A. Bastos (CEMAPRE, School of Economics and Management (ISEG), Technical University of Lisbon)
    Abstract: In many domains, the combined opinion of a committee of experts provides better decisions than the judgment of a single expert. This paper shows how to implement a successful ensemble strategy for predicting recovery rates on defaulted debts. Using data from Moody's Ultimate Recovery Database, it is shown that committees of models derived from the same regression method present better forecasts of recovery rates than a single model. More accurate predictions are observed whether we forecast bond or loan recoveries, and across the entire range of actual recovery values.
    Keywords: Recovery rate, Loss given default, Forecasting, Ensemble learning, Credit risk
    JEL: G17 G21
    Date: 2013–03
  5. By: Beechey, Meredith (Sveriges Riksbank); Österholm, Pär (National Institute of Economic Research)
    Abstract: In recent years the central banks of Norway and Sweden have published their endogenous policy interest-rate forecasts. In this paper, we evaluate those forecasts alongside policy-rate expectations inferred from market pricing. We find that for both economies there are only small differences in relative forecasting precision between the central bank and market-implied measures. However, both types of forecast fail tests for unbiasedness and efficiency at longer horizons.
    Keywords: Monetary policy; Market expectations; Norges Bank; Sveriges Riksbank
    JEL: E52
    Date: 2013–01–22
  6. By: Marczak, Martyna; Gómez, Victor
    Abstract: This article proposes a new multivariate method to construct business cycle indicators. The method is based on a decomposition into trend-cycle and irregular. To derive the cycle, a multivariate band-pass filter is applied to the estimated trend-cycle. The whole procedure is fully model-based. Using a set of monthly and quarterly US time series, two monthly business cycle indicators are obtained for the US. They are represented by the smoothed cycles of real GDP and the industrial production index. Both indicators are able to reproduce previous recessions very well. Series contributing to the construction of both indicators are allowed to be leading, lagging or coincident relative to the business cycle. Their behavior is assessed by means of the phase angle and the mean phase angle after cycle estimation. The proposed multivariate method can serve as an attractive tool for policy making, in particular due to its good forecasting performance and quite simple setting. The model ensures reliable realtime forecasts even though it does not involve elaborate mechanisms that account for, e.g., changes in volatility. --
    Keywords: business cycle,multivariate structural time series model,univariate band-pass filter,forecasts,phase angle
    JEL: E32 E37 C18 C32
    Date: 2013
  7. By: Shu-Ling Chen; John D. Jackson; Hyeongwoo Kim; Pramesti Resiandini
    Abstract: This paper examines common forces driving the prices of 51 highly tradable commodities. We demonstrate that highly persistent movements of these prices are mostly due to the first common component, which is closely related to the US nominal exchange rate. In particular, our simple factor-based model outperforms the random walk model in out-of-sample forecast for the US exchange rate. The second common factor and de-factored idiosyncratic components are consistent with stationarity, implying short-lived deviations from the equilibrium price dynamics. In concert, these results provide an intriguing resolution to the apparent inconsistency arising from stable markets with nonstationary prices.
    Keywords: Commodity Prices; US Nominal Exchange Rate; Panel Analysis of Nonstationarity in Idiosyncratic and Common Components; Cross-Section Dependence; Out-of-Sample Forecast
    JEL: C53 F31
    Date: 2013–02
  8. By: Kevin Sheppard; Lily Liu; Andrew J. Patton
    Abstract: We study the accuracy of a wide variety of estimators of asset price variation constructed from high-frequency data (so-called "realized measures"), and compare them with a simple "realized variance" (RV) estimator.  In total, we consider almost 400 different estimators, applied to 11 years of data on 31 different financial assets spanning five asset classes, including equities, equity indices, exchange rates and interest rates.  We apply data-based ranking methods to the realized measures and to forecasts based on these measures.  When 5-minute RV is taken as the benchmark realized measure, we find little evidence that it is outperformed by any of the other measures.  When using inference methods that do not require specifying a benchmark, we find some evidence that more sophisticated realized measures significantly outperform 5-minute RV.  In forecasting applications, we find that a low frequency "truncated" RV outperforms most other realized measures.  Overall, we conclude that it is difficult to significantly beat 5-minute RV.
    Keywords: Realized variance, volatility forecasting, high frequency data
    JEL: C58 C22 C53
    Date: 2013–02–12
  9. By: Paul Hubert (Ofce sciences-po)
    Abstract: Policymakers at the Federal Open Market Committee (FOMC) publish forecasts since 1979. We examine the effects of publishing FOMC inflation forecasts in two steps using a structural VAR model. We assess whether they influence private inflation expectations and the underlying mechanism at work: do they convey policy signals for forward guidance or help interpreting current policy decisions? We provide original evidence that FOMC inflation forecasts are able to influence private ones. We also find that FOMC forecasts give information about future Fed rate movements and affect private expectations in a different way than Fed rate shocks. This body of evidence supports the use of central bank forecasts to affect inflation expectations especially while conventional policy instruments are at the zero lower bound
    Keywords: Monetary policy, Forecasts, FOMC, influence, Policy signals, structural Var
    JEL: E52 E58
    Date: 2013–02
  10. By: Paul Hubert (Ofce sciences-po)
    Abstract: The European Central Bank publishes inflation projections quarterly. This paper aims at establishing whether they influence private forecasts and whether they may be considered as an enhanced means of implementing policy decisions by facilitating private agents’ information processing. We provide original evidence that ECB inflation projections do influence private inflation expectations. We also find that ECB projections give information about future ECB rate movements, and that the ECB rate has different effects if complemented or not with the publication of ECB projections. We conclude that ECB projections enable private agents to correctly interpret and predict policy decisions
    Keywords: Monetary policy, ECB, Private forecasts,Influence, structural Var
    JEL: E52 E58
    Date: 2013–02
  11. By: Stefano Grassi (Aarhus University and CREATES); Paolo Santucci de Magistris (Aarhus University and CREATES)
    Abstract: The persistent nature of equity volatility is investigated by means of a multi-factor stochastic volatility model with time varying parameters. The parameters are estimated by means of a sequential indirect inference procedure which adopts as auxiliary model a time-varying generalization of the HAR model for the realized volatility series. It emerges that during the recent financial crisis the relative weight of the daily component dominates over the monthly term. The estimates of the two factor stochastic volatility model suggest that the change in the dynamic structure of the realized volatility during the financial crisis is due to the increase in the volatility of the persistent volatility term. As a consequence of the dynamics in the stochastic volatility parameters, the shape and curvature of the volatility smile evolve trough time.
    Keywords: Time-Varying Parameters, On-line Kalman Filter, Simulation-based inference, Predictive Likelihood, Volatility Factors
    JEL: G01 C00 C11 C58
    Date: 2013–02–18

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