nep-for New Economics Papers
on Forecasting
Issue of 2015‒01‒09
ten papers chosen by
Rob J Hyndman
Monash University

  1. The Importance of a Time-Varying Variance and Cross-Country Interactions in Forecast Models By Steven Trypsteen
  2. Bayesian Combination for Inflation Forecasts: The Effects of a Prior Based on Central Banks’ Estimates By Luis F. Melo Velandia; Rubén A. Loaiza Maya; Mauricio Villamizar-Villegas
  3. Forecasting the U.S. Real House Price Index By Vasilios Plakandaras; Rangan Gupta; Periklis Gogas; Theophilos Papadimitriou
  4. Quantile aggregation of density forecasts By Fabio Busetti
  5. On the predictive content of nonlinear transformations of lagged autoregression residuals and time series observations By Rossen, Anja
  6. Bond Return Predictability: Economic Value and Links to the Macroeconomy By Gargano, Antonio; Pettenuzzo, Davide; Timmermann, Allan G
  7. Bayesian Semiparametric Modeling of Realized Covariance Matrices By Jin, Xin; Maheu, John M
  8. Measuring the Euro-Dollar Permanent Equilibrium Exchange Rate using the Unobserved Components Model By Chen, Xiaoshan; MacDonald, Ronald
  9. Improving predictability of time series using maximum entropy methods By Gregor Chliamovitch; Alexandre Dupuis; Bastien Chopard; Anton Golub
  10. Exchange Rate Forecasts and Expected Fundamentals By Christian D. Dick; Ronald MacDonald; Lukas Menkhoff

  1. By: Steven Trypsteen
    Abstract: This paper examines growth forecasts of models that allow for crosscountry interactions and/or a time-varying variance plus feedback from volatility to growth. Allowing for these issues is done by augmenting an autoregressive model with cross-country weighted averages of growth and/or the GARCH-M framework. The models also allow for structural breaks in the mean and variance of growth. The obtained forecasts are then evaluated using statistical criteria, i.e. point and density forecasts, and an economic criterion, i.e. forecasting recessionary events. The results show that the two components are important to obtain improved point and density forecasts, but that forecasting recessionary events remains difficult.
  2. By: Luis F. Melo Velandia; Rubén A. Loaiza Maya; Mauricio Villamizar-Villegas
    Abstract: Typically, central banks use a variety of individual models (or a combination of models) when forecasting inflation rates. Most of these require excessive amounts of data, time, and computational power; all of which are scarce when monetary authorities meet to decide over policy interventions. In this paper we use a rolling Bayesian combination technique that considers inflation estimates by the staff of the Central Bank of Colombia during 2002-2011 as prior information. Our results show that: 1) the accuracy of individual models is improved by using a Bayesian shrinkage methodology, and 2) priors consisting of staff's estimates outperform all other priors that comprise equal or zero-vector weights. Consequently, our model provides readily available forecasts that exceed all individual models in terms of forecasting accuracy at every evaluated horizon.
    Keywords: Bayesian shrinkage, inflation forecast combination, internal forecasts, rolling window estimation
    JEL: C22 C53 C11 E31
    Date: 2014–11–20
  3. By: Vasilios Plakandaras (Department of Economics, Democritus University of Thrace, Greece); Rangan Gupta (Department of Economics, Pretoria University, South Africa); Periklis Gogas (Department of Economics, Democritus University of Thrace, Greece; The Rimini Centre for Economic Analysis, Italy); Theophilos Papadimitriou (Department of Economics, Democritus University of Thrace, Greece)
    Abstract: The 2006 sudden and immense downturn in U.S. House Prices sparked the 2007 global financial crisis and revived the interest about forecasting such imminent threats for economic stability. In this paper we propose a novel hybrid forecasting methodology that combines the Ensemble Empirical Mode Decomposition (EEMD) from the field of signal processing with the Support Vector Regression (SVR) methodology that originates from machine learning. We test the forecasting ability of the proposed model against a Random Walk (RW) model, a Bayesian Autoregressive and a Bayesian Vector Autoregressive model. The proposed methodology outperforms all the competing models with half the error of the RW model with and without drift in out-of-sample forecasting. Finally, we argue that this new methodology can be used as an early warning system for forecasting sudden house prices drops with direct policy implications.
    Date: 2014–11
  4. By: Fabio Busetti (Bank of Italy)
    Abstract: Quantile aggregation (or 'Vincentization') is a simple and intuitive way of combining probability distributions, originally proposed by S. B. Vincent in 1912. In certain cases, such as under Gaussianity, the Vincentized distribution belongs to the same family as that of the individual distributions and can be obtained by averaging the individual parameters. This paper compares the properties of quantile aggregation with those of the forecast combination schemes normally adopted in the econometric forecasting literature, based on linear or logarithmic averages of the individual densities. In general we find that: (i) larger differences among the combination schemes occur when there are biases in the individual forecasts, in which case quantile aggregation seems preferable overall; (ii) the choice of the combination weights is important in determining the performance of the various methods. Monte Carlo simulation experiments indicate that the properties of quantile aggregation fall between those of the linear and the logarithmic pool, and that quantile averaging is particularly useful for combining forecast distributions with large differences in location. An empirical illustration is provided with density forecasts from time series and econometric models for Italian GDP.
    Keywords: Fan charts, macroeconomic forecasts, model combination.
    JEL: C53 E17
    Date: 2014–10
  5. By: Rossen, Anja
    Abstract: Although many macroeconomic time series are assumed to follow nonlinear processes, nonlinear models often do not provide better predictions than their linear counterparts. Furthermore, such models easily become very complex and difficult to estimate. The aim of this study is to investigate whether simple nonlinear extensions of autoregressive processes are able to provide more accurate forecasting results than linear models. Therefore, simple autoregressive processes are extended by means of nonlinear transformations (quadratic, cubic, trigonometric, exponential functions) of lagged time series observations and autoregression residuals. The proposed forecasting models are applied to a large set of macroeconomic and financial time series for 10 European countries. Findings suggest that such models, including nonlinear transformation of lagged autoregression residuals, are somewhat able to provide better forecasting results than simple linear models. Thus, it may be possibile to improve the forecasting accuracy of linear models by including nonlinear components.
    Keywords: nonlinear models,forecasting,transformations
    JEL: C22 C53 C51
    Date: 2014
  6. By: Gargano, Antonio; Pettenuzzo, Davide; Timmermann, Allan G
    Abstract: Studies of bond return predictability find a puzzling disparity between strong statistical evidence of return predictability and the failure to convert return forecasts into economic gains. We show that resolving this puzzle requires accounting for important features of bond return models such as time varying parameters and volatility dynamics. A three-factor model comprising the Fama-Bliss (1987) forward spread, the Cochrane-Piazzesi (2005) combination of forward rates and the Ludvigson-Ng (2009) macro factor generates notable gains in out-of-sample forecast accuracy compared with a model based on the expectations hypothesis. Importantly, we find that such gains in predictive accuracy translate into higher risk-adjusted portfolio returns after accounting for estimation error and model uncertainty, as evidenced by the performance of model combinations. Finally, we find that bond excess returns are predicted to be significantly higher during periods with high inflation uncertainty and low economic growth and that the degree of predictability rises during recessions.
    Keywords: Bayesian estimation; bond returns; model uncertainty; stochastic volatility; time-varying parameters
    JEL: G11 G12 G17
    Date: 2014–08
  7. By: Jin, Xin; Maheu, John M
    Abstract: This paper introduces several new Bayesian nonparametric models suitable for capturing the unknown conditional distribution of realized covariance (RCOV) matrices. Existing dynamic Wishart models are extended to countably infinite mixture models of Wishart and inverse-Wishart distributions. In addition to mixture models with constant weights we propose models with time-varying weights to capture time dependence in the unknown distribution. Each of our models can be combined with returns to provide a coherent joint model of returns and RCOV. The extensive forecast results show the new models provide very significant improvements in density forecasts for RCOV and returns and competitive point forecasts of RCOV.
    Keywords: multi-period density forecasts, inverse-Wishart distribution, beam sampling, hierarchical Dirichlet process, infinite hidden Markov model
    JEL: C11 C14 C32 C58 G17
    Date: 2014–11
  8. By: Chen, Xiaoshan; MacDonald, Ronald
    Abstract: This paper employs an unobserved component model that incorporates a set of economic fundamentals to obtain the Euro-Dollar permanent equilibrium exchange rates (PEER) for the period 1975Q1 to 2008Q4. The results show that for most of the sample period, the Euro-Dollar exchange rate closely followed the values implied by the PEER. The only significant deviations from the PEER occurred in the years immediately before and after the introduction of the single European currency. The forecasting exercise shows that incorporating economic fundamentals provides a better long-run exchange rate forecasting performance than a random walk process.
    Keywords: Exchange rate forecasting; Unobserved Components Model; Permanent Equilibrium Exchange Rate
    Date: 2014–11
  9. By: Gregor Chliamovitch; Alexandre Dupuis; Bastien Chopard; Anton Golub
    Abstract: We discuss how maximum entropy methods may be applied to the reconstruction of Markov processes underlying empirical time series and compare this approach to usual frequency sampling. It is shown that, at least in low dimension, there exists a subset of the space of stochastic matrices for which the MaxEnt method is more efficient than sampling, in the sense that shorter historical samples have to be considered to reach the same accuracy. Considering short samples is of particular interest when modelling smoothly non-stationary processes, for then it provides, under some conditions, a powerful forecasting tool. The method is illustrated for a discretized empirical series of exchange rates.
    Date: 2014–11
  10. By: Christian D. Dick; Ronald MacDonald; Lukas Menkhoff
    Abstract: Using a large panel of individual professionals' forecasts, this paper demonstrates that good exchange rate forecasts are related to a proper understanding of fundamentals, specifically good interest rate forecasts. This relationship is robust to individual fixed effects and further controls. Reassuringly, the relationship is stronger during phases when the impact from fundamentals is more obvious, e.g., when exchange rates substantially deviate from their PPP values. Finally, forecasters largely agree that an interest rate increase relates to a currency appreciation, but only good forecasters get expected interest rates right
    Keywords: Exchange Rate Determination, Individual Expectations, Macroeconomic Fundamentals
    JEL: F31 F37 E44
    Date: 2014–11

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