nep-for New Economics Papers
on Forecasting
Issue of 2014‒02‒21
eight papers chosen by
Rob J Hyndman
Monash University

  1. On The Theory and Practice of Singular Spectrum Analysis Forecasting By M. Atikur Rahman Khan; D.S. Poskitt
  2. Revenue Forecast Errors in the European Union By António Afonso; Rui Carvalho
  3. Exchange Rate Predictability in a Changing World By Byrne, Joseph P; Korobilis, Dimitris; Ribeiro, Pinho J
  4. Forecasting Bank Credit Ratings By Gogas, Periklis; Papadimitriou, Theophilos; Agrapetidou, Anna
  5. Generalized Nelson-Siegel Term Structure Model : Do the second slope and curvature factors improve the in-sample fit and out-of-sample forecast? By Wali Ullah; Yasumasa Matsuda
  6. Semiparametric Generalized Long Memory Modelling of GCC Stock Market Returns: A Wavelet Approach By Heni Boubaker; Nadia Sghaier
  7. RIN Market: price behavior and its forecast By Kakorina, Ekaterina

  1. By: M. Atikur Rahman Khan; D.S. Poskitt
    Abstract: Theoretical results on the properties of forecasts obtained using singular spectrum analysis are presented in this paper. The mean squared forecast error is derived under broad regularity conditions, and it is shown that the forecasts obtained in practice will converge to their population ensemble counterparts. The theoretical results are illustrated by examining the performance of singular spectrum analysis forecasts when applied to autoregressive processes and a random walk process. Simulation experiments suggest that the asymptotic properties developed are reflected in observed finite sample behaviour. Empirical applications using real world data sets indicate that forecasts based on singular spectrum analysis are competitive with other methods currently in vogue.
    Keywords: Linear recurrent formula, Mean squared forecast error, Signal dimension, Window length.
    Date: 2014
  2. By: António Afonso; Rui Carvalho
    Abstract: In this paper we assess the determinants of revenue forecast errors for the EU-15 between 1999 and 2012, based on the forecasts published bi-annually by the European Commission. Our results show that personal income rate changes increase the revenue forecast errors: for forecasts made in t for t, increases in the corporate tax rate implies a decrease in the revenue forecast errors, in t+1 and t+2. Moreover, an increase in GDP forecast errors decreases revenue errors, whereas an increase in the inflation error will increase revenue errors. GDP errors, minority governments, election year and corporate tax rate changes can be associated with optimistic revenue forecasts. On the other hand, yield, inflation errors and VAT tax rate changes are associated with more prudent forecast behaviour.
    Keywords: macro forecasts, revenue forecast errors, EU
    JEL: C23 H20 H68
    Date: 2014–01
  3. By: Byrne, Joseph P; Korobilis, Dimitris; Ribeiro, Pinho J
    Abstract: An expanding literature articulates the view that Taylor rules are helpful in predicting exchange rates. In a changing world however, Taylor rule parameters may be subject to structural instabilities, for example during the Global Financial Crisis. This paper forecasts exchange rates using such Taylor rules with Time Varying Parameters (TVP) estimated by Bayesian methods. In core out-of-sample results, we improve upon a random walk benchmark for at least half, and for as many as eight out of ten, of the currencies considered. This contrasts with a constant parameter Taylor rule model that yields a more limited improvement upon the benchmark. In further results, Purchasing Power Parity and Uncovered Interest Rate Parity TVP models beat a random walk benchmark, implying our methods have some generality in exchange rate prediction.
    Keywords: Exchange Rate Forecasting; Taylor Rules; Time-Varying Parameters; Bayesian Methods.
    JEL: C53 E52 F31 F37 G17
    Date: 2014–02–14
  4. By: Gogas, Periklis (Democritus University of Thrace, Department of Economics); Papadimitriou, Theophilos (Democritus University of Thrace, Department of Economics); Agrapetidou, Anna (Democritus University of Thrace, Department of Economics)
    Abstract: Purpose - This study presents an empirical model designed to forecast bank credit ratings using only quantitative and publicly available information from their financial statements. For this reason we use the long term ratings provided by Fitch in 2012. Our sample consists of 92 U.S. banks and publicly available information in annual frequency from their financial statements from 2008 to 2011. Methodology - First, in the effort to select the most informative regressors from a long list of financial variables and ratios we use stepwise least squares and select several alternative sets of variables. Then these sets of variables are used in an ordered probit regression setting to forecast the long term credit ratings. Findings - Under this scheme, the forecasting accuracy of our best model reaches 83.70% when 9 explanatory variables are used. Originality/value - The results indicate that bank credit ratings largely rely on historical data making them respond sluggishly and after any financial problems are already known to the public.
    Keywords: Banking; Forecasting; Credit Rating; Logit
    JEL: G20 G24
    Date: 2014–02–14
  5. By: Wali Ullah; Yasumasa Matsuda
    Abstract: The dynamic Nelson-Siegel (DNS) model and even the Svensson generalization of the model have trouble in fitting the short maturity yields and fail to grasp the characteristics of the Japanese government bonds (JGBs) yield curve, which is flat at the short end and have multiple inflection points. Therefore, a closely related generalized Nelson-Siegel model (GDNS) with two slopes and curvatures is considered and compared empirically to the traditional DNS in terms of in-sample fit as well as out-of-sample forecasts. Furthermore, the GDNS with time-varying volatility component, modelled as standard EGARCH process, is also considered to evaluate its performance in relation to the GDNS. The GDNS models unanimously outperforms the DNS in terms of in-sample fit as well as out-of-sample forecasts. Moreover, the extended model that accounts for time-varying volatility outpace the other models for fitting the yield curve and produce relatively more accurate 6- and 12-month ahead forecasts, while the GDNS model comes with more precise forecasts for very short forecast horizons.
    Date: 2014–02
  6. By: Heni Boubaker; Nadia Sghaier
    Abstract: This paper proposes a new class of semiparametric generalized long memory model with FIA- PARCH errors (SEMIGARMA-FIAPARCH model) that extends the conventionnel GARMA model to incorporate nonlinear deterministic trend, in the mean equation, and to allow for time varying volatility, in the conditional variance equation. The parameters of this model are estimated in a wavelet domain. We provide an empirical application of this model to examine the dynamic of the stock market returns in six GCC countries. The empirical results show that the model proposed o¤ers an interesting framework to describe the seasonal long range dependence and the nonlinear deterministic trend in the return as well as persistence to shocks in the conditional volatiliy. We also compare its performance predictive to the traditional long memory model with FIAPARCH errors (FARMA-FIAPARCH model). The predictive results indicate that the model proposed out performs the FARMA-FIAPARCH model.
    Keywords: semiparametric generalized long memory process, FIAPARCH errors, wavelet do- main, stock market returns.
    JEL: C13 C22 C32 G15
    Date: 2014–01–06
  7. By: Kakorina, Ekaterina
    Abstract: In the 90th the Kyoto Protocol was signed and a market for emissions emerged. This market has one problem: it is too difficult to measure how much the company is polluting. The USA solved this problem by creating a similar market, namely the RIN (Renewable Identification Number) market. Unlike emissions, presently RINs are traded without the exchange. The importance of the RIN trading is likely to increase in the future and the goal of this paper is to research the RIN price behavior and to forecast the prices using ARMA-t-GARCH models. This paper shows that it is not important how to estimate these series (separately or together), because the estimation of parameters are very similar and the forecasted gaps are similar too. Also the common estimation using DCC-GARCH model made it possible to ascertain that these series have positive correlation in each pair.
    Keywords: Energy, RIN market, RIN, Renewable Identification Number, ecology, security, DCC-GARCH, ARMA-t-GARCH, price behavior, price forecast
    JEL: G00 Q20 Q40
    Date: 2014–02–16
  8. By: Yasumasa Matsuda
    Abstract: This paper aims to provide a wavelet analysis for spatio-temporal data which are observed on irregularly spaced stations at discrete time points, where the spatial covariances show serious non-stationarity caused by local dependency. A specific example that is used for the demonstration is US precipitation data observed on about ten thousand stations in every month. By a reinterpretation of Whittle likelihood function for stationary time series, we propose a kind of Bayesian regression model for spatial data whose regressors are given by modified Haar wavelets and try a spatio-temporal extension by a state space approach. We also propose an empirical Bayes estimation for the parameters, which is regarded as a spatio-temporal extension of Whittle likelihood estimation originally defined for stationary time series. We conduct the extended Whittle estimate and compare mean square errors of the forecasts with those of some benchmarks to evaluate its goodness for the US precipitation data in August from 1987-1997.
    Date: 2014–01

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