nep-for New Economics Papers
on Forecasting
Issue of 2007‒10‒20
eight papers chosen by
Rob J Hyndman
Monash University

  1. Learning in Real Time: Theory and Empirical Evidence from the Term Structure of Survey Forecasts By Patton, Andrew J; Timmermann, Allan G
  2. Forecasting the Yield Curve Using Priors from No Arbitrage Affine Term Structure Models By Andrea Carriero
  3. Modelling and Forecasting Oil Prices: The Role of Asymmetric Cycles By Jesus Crespo Cuaresma; Adusei Jumah; Sohbet Karbuz
  4. Mixtures of t-distributions for Finance and Forecasting By Giacomini, Raffaella; Gottschling, Andreas; Haefke, Christian; White, Halbert
  5. Forecasting elections using expert surveys: an application to U.S. presidential elections By Jones, Randall J.; Armstrong, J. Scott; Cuzan, Alfred G.
  6. Forecasting Weekly Electricity Prices at Nord Pool By Hipòlit Torró
  7. The Political Economy of EDP Fiscal Forecasts: An Empirical Assessment By Álvaro Pina; Nuno Venes
  8. Analyzing Strongly Periodic Series in the Frequency Domain: A Comparison of Alternative Approaches with Applications By Artis, Michael J; Clavel, Jose Garcia; Hoffmann, Mathias; Nachane, Dilip M

  1. By: Patton, Andrew J; Timmermann, Allan G
    Abstract: We develop a theoretical framework for understanding how agents form expectations about economic variables with a partially predictable component. Our model incorporates the effect of measurement errors and heterogeneity in individual forecasters' prior beliefs and their information signals and also accounts for agents' learning in real time about past, current and future values of economic variables. We use the model to develop insights into the term structure of forecast errors, and test its implications on a data set comprising survey forecasts of annual GDP growth and inflation with horizons ranging from 1 to 24 months. The model is found to closely match the term structure of forecast errors for consensus beliefs and is able to replicate the cross-sectional dispersion in forecasts of GDP growth but not for inflation - the latter appearing to be too high in the data at short horizons. Our analysis also suggests that agents systematically underestimated the persistent component of GDP growth but overestimated it for inflation during most of the 1990s.
    Keywords: real time learning; survey forecasts; term structure of forecasts
    JEL: C53 E37
    Date: 2007–10
  2. By: Andrea Carriero (Queen Mary, University of London)
    Abstract: In this paper we propose a strategy for forecasting the term structure of interest rates which may produce significant gains in predictive accuracy. The key idea is to use the restrictions implied by Affine Term Structure Models (ATSM) on a vector autoregression (VAR) as prior information rather than imposing them dogmatically. This allows to account for possible model misspecification. We apply the method to a system of five US yields, and we find that the gains in predictive accuracy can be substantial. In particular, for horizons longer than 1-step ahead, our proposed method produces systematically better forecasts than those obtained by using a pure ATSM or an unrestricted VAR, and it also outperforms very competitive benchmarks as the Minnesota prior, the Diebold-Li (2006) model, and the random walk.
    Keywords: Bayesian methods, Forecasting, Term structure
    JEL: C11 C53 E43 E47
    Date: 2007–10
  3. By: Jesus Crespo Cuaresma; Adusei Jumah; Sohbet Karbuz
    Abstract: We propose a new time series model aimed at forecasting crude oil prices. The proposed specification is an unobserved components model with an asymmetric cyclical component. The asymmetric cycle is defined as a sine-cosine wave where the frequency of the cycle depends on past oil price observations. We show that oil price forecasts improve significantly when this asymmetry is explicitly modelled.
    Keywords: Oil price, forecasting, nonlinear time series analysis, asymmetric cycles.
    JEL: C22 O13 C53
  4. By: Giacomini, Raffaella (University College London); Gottschling, Andreas (Deutsche Bank AG, Credit RiskManagement); Haefke, Christian (Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria); White, Halbert (Department of Economics, University of California, San Diego)
    Abstract: We explore convenient analytic properties of distributions constructed as mixtures of scaled and shifted t-distributions. A feature that makes this family particularly desirable for econometric applications is that it possesses closed-form expressions for its anti-derivatives (e.g., the cumulative density function). We illustrate the usefulness of these distributions in two applications. In the first application, we use a scaled and shifted t-distribution to produce density forecasts of U.S. inflation and show that these forecasts are more accurate, out-of-sample, than density forecasts obtained using normal or standard t-distributions. In the second application, we replicate the option-pricing exercise of Abadir and Rockinger (2003) using a mixture of scaled and shifted t-distributions and obtain comparably good results, while gaining analytical tractability.
    Keywords: ARMA-GARCH models, neural networks, nonparametric density estimation, forecast accuracy, option pricing, risk neutral density
    JEL: C63 C53 C45
    Date: 2007–10
  5. By: Jones, Randall J.; Armstrong, J. Scott; Cuzan, Alfred G.
    Abstract: Prior research offers a mixed view of the value of expert surveys for long-term election forecasts. On the positive side, experts have more information about the candidates and issues than voters do. On the negative side, experts all have access to the same information. Based on prior literature and on our experiences with the 2004 presidential election and the 2008 campaign so far, we have reason to believe that a simple expert survey (the Nominal Group Technique) is preferable to Delphi. Our survey of experts in American politics was quite accurate in the 2004 election. Following the same procedure, we have assembled a new panel of experts to forecast the 2008 presidential election. Here we report the results of the first survey, and compare our experts’ forecasts with predictions by the Iowa Electronic Market .
    Keywords: forecasting; elections; expert surveys; Delphi
    JEL: Y80
    Date: 2007–10–02
  6. By: Hipòlit Torró (Universitat de València)
    Abstract: This paper analyses the forecasting power of weekly futures prices at Nord Pool. The forecasting power of futures prices is compared to an ARIMAX model of the spot price. The time series model contains lagged external variables such as: temperature, precipitation, reservoir levels and the basis (futures price less the spot price); and generally reflects the typical seasonal patterns in weekly spot prices. Results show that the time series model forecasts significantly beat futures prices when using the Diebold and Mariano (1995) test. Furthermore, the average forecasting error of futures prices reveals that they are significantly above the settlement spot price at the ‘delivery week’ and their size increases as the time to maturity increases. Those agents taking positions in weekly futures contracts at Nord Pool might find the estimated ARIMAX model useful for improving their expectation formation process for the underlying spot price.
    Keywords: Electricity Markets, Power Derivatives and Forecasting Electricity Prices
    JEL: G13 L94
    Date: 2007–09
  7. By: Álvaro Pina; Nuno Venes
    Abstract: This paper analyses the track record of fiscal forecasts reported by 15 European countries in the context of the Excessive Deficit Procedure. For the budget balance, gross fixed capital formation (GFCF) and interest payments, we study the statistical properties of forecast errors and their politico-institutional determinants. While errors in interest and GFCF expenditure present few systematic patterns, budget balance errors are responsive to fiscal institutions and to opportunistic motivations, especially from 1999 onwards: upcoming elections induce over-optimism, whereas commitment or mixed forms of fiscal governance and numerical expenditure rules (but not deficit and debt rules) are associated to greater prudence.
    Keywords: fiscal forecasting; Stability and Growth Pact; Excessive Deficit Procedure; fiscal rules
    JEL: E62 H62 H68
    Date: 2007
  8. By: Artis, Michael J; Clavel, Jose Garcia; Hoffmann, Mathias; Nachane, Dilip M
    Abstract: Strongly periodic series occur frequently in many disciplines. This paper reviews one specific approach to analyzing such series viz. the harmonic regression approach. In this paper, the five major methods suggested under this approach are critically reviewed and compared, and their empirical potential highlighted via two applications. The out-of-sample forecast comparisons are made using the Superior Predictive Ability test, which specifically guards against the perils of data snooping. Certain tentative conclusions are drawn regarding the relative forecasting ability of the different methods.
    Keywords: autoregressive methods; data snooping; dynamic harmonic regression; eigenvalue methods; mixed spectrum; multiple forecast comparisons
    JEL: C22 C53
    Date: 2007–10

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