nep-for New Economics Papers
on Forecasting
Issue of 2013‒09‒25
seven papers chosen by
Rob J Hyndman
Monash University

  1. Labour market forecasting : is disaggregation useful? By Weber, Enzo; Zika, Gerd
  2. Reverse Kalman filtering U.S. inflation with sticky professional forecasts By James M. Nason; Gregor W. Smith
  3. Does the firm-analyst relationship matter in explaining analysts' earnings forecast errors? By Régis Breton; Sébastien Galanti; Christophe Hurlin; Anne-Gaël Vaubourg
  4. Stress-testing U.S. bank holding companies: a dynamic panel quantile regression approach By Francisco B. Covas; Ben Rump; Egon Zakrajsek
  5. What does financial volatility tell us about macroeconomic fluctuations? By Marcelle Chauvet; Zeynep Senyuz; Emre Yoldas
  6. A solution for forecasting pet chips prices for both short-term and long-term price forcasting, using genetic programming By Mojtaba Sedigh Fazli; Jean-Fabrice Lebraty
  7. The fine structure of volatility feedback II: overnight and intra-day effects By Pierre Blanc; R\'emy Chicheportiche; Jean-Philippe Bouchaud

  1. By: Weber, Enzo (Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany]); Zika, Gerd (Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany])
    Abstract: "Using the example of short-term forecasts for German employment figures, the article at hand examines the question whether the use of disaggregated information increases the forecast accuracy of the aggregate. For this purpose, the out-of-sample forecasts for the aggregated employment forecast are compared to and contrasted with forecasts based on a vector-autoregressive model, which includes not only the aggregate but also the numbers of gainfully employed people at the industry level. The Clark/West test is used in the model comparison. It becomes evident that disaggregation significantly improves the employment forecast. Moreover, fluctuation- window tests help identify the phases during which disaggregation increases forecast accuracy to the strongest extent." (Author's abstract, IAB-Doku) ((en))
    Keywords: Arbeitsmarktprognose - Methode, Prognostik, Beschäftigtenzahl
    JEL: J23 C53
    Date: 2013–09–17
  2. By: James M. Nason; Gregor W. Smith
    Abstract: We provide a new way to filter US inflation into trend and cycle components, based on extracting long-run forecasts from the Survey of Professional Forecasters. We operate the Kalman filter in reverse, beginning with observed forecasts, then estimating parameters, and then extracting the stochastic trend in inflation. The trend-cycle model with unobserved components is consistent with numerous studies of US inflation history and is of interest partly because the trend may be viewed as the Fed’s evolving inflation target or long-horizon expected inflation. The sluggish reporting attributed to forecasters is consistent with evidence on mean forecast errors. We find considerable evidence of inflation-gap persistence and some evidence of implicit sticky information. But statistical tests show we cannot reconcile these two widely used perspectives on US inflation forecasts, the unobserved-components model and the sticky-information model.
    Keywords: Inflation (Finance) - United States ; Forecasting
    Date: 2013
  3. By: Régis Breton (Centre de recherche de la Banque de France - Banque de France); Sébastien Galanti (LEO - Laboratoire d'économie d'Orleans - CNRS : UMR6221 - Université d'Orléans); Christophe Hurlin (LEO - Laboratoire d'économie d'Orleans - CNRS : UMR6221 - Université d'Orléans); Anne-Gaël Vaubourg (Larefi - Laboratoire d'analyse et de recherche en économie et finance internationales - Université Montesquieu - Bordeaux IV : EA2954)
    Abstract: We study whether financial analysts' concern for preserving good relationships with firms' managers motivates them to issue pessimistic or optimistic forecasts. Based on a dataset of one-yearahead EPS forecasts issued by 4 648 analysts concerning 241 French firms (1997-2007), we regress the analysts' forecast accuracy on its unintentional determinants. We then decompose the fixed effect of the regression and we use the firm-analyst pair effect as a measure of the intensity of the firm-analyst relationship. We find that a low (high) firm-analyst pair effect is associated with a low (high) forecast error. This observation suggests that pessimism and optimism result from the analysts' concern for cultivating their relationship with the firm's management.
    Keywords: financial analysts, earnings forecasts, soft information, panel regression.
    Date: 2013–08–16
  4. By: Francisco B. Covas; Ben Rump; Egon Zakrajsek
    Abstract: We propose an econometric framework for estimating capital shortfalls of bank holding companies (BHCs) under pre-specified macroeconomic scenarios. To capture the nonlinear dynamics of bank losses and revenues during periods of financial stress, we use a fixed effects quantile autoregressive (FE-QAR) model with exogenous macroeconomic covariates, an approach that delivers a superior out-of-sample forecasting performance compared with the standard linear framework. According to the out-of-sample forecasts, the realized net charge-offs during the 2007-09 crisis are within the multi-step-ahead density forecasts implied by the FE-QAR model, but they are frequently outside the density forecasts generated using the corresponding linear model. This difference reflects the fact that the linear specification substantially underestimates loan losses, especially for real estate loan portfolios. Employing the macroeconomic stress scenario used in CCAR 2012, we use the density forecasts generated by the FE-QAR model to simulate capital shortfalls for a panel of large BHCs. For almost all institutions in the sample, the FE-QAR model generates capital shortfalls that are considerably higher than those implied by its linear counterpart, which suggests that our approach has the potential for detecting emerging vulnerabilities in the financial system.
    Date: 2013
  5. By: Marcelle Chauvet; Zeynep Senyuz; Emre Yoldas
    Abstract: This paper provides an extensive analysis of the predictive ability of financial volatility measures for economic activity. We construct monthly measures of stock and bond market volatility from daily returns and model volatility as composed of a long-run component that is common across all series, and a set of idiosyncratic short-run components. Based on powerful in-sample predictive ability tests, we find that the stock volatility measures and the common factor significantly improve short-term forecasts of conventional financial indicators. A real-time out of sample assessment yields a similar conclusion under the assumption of noisy revisions in macroeconomic data. In a non-linear extension of the dynamic factor model for volatility series, we identify three regimes that describe the joint volatility dynamics: low, intermediate and high-volatility. We also find that the non-linear model performs remarkably well in tracking the Great Recession of 2007-2009 in real-time.
    Date: 2013
  6. By: Mojtaba Sedigh Fazli (Centre de Recherche Magellan - Université Jean Moulin - Lyon III : EA3713); Jean-Fabrice Lebraty (Centre de Recherche Magellan - Université Jean Moulin - Lyon III : EA3713)
    Abstract: Nowadays, forecasting what will happen in economic environments plays a crucial role. We showed that in PET market how a neuro-fuzzy hybrid model can assist the managers in decision-making. In this research, the target is to forecast the same item through another intelligent tool which obeys the evolutionary processing mechanisms. Again, the item for prediction here is PET (Poly Ethylene Terephthalate) which is the raw material for textile industries and it is highly sensitive against oil price fluctuations and also some other factors such as the demand and supply ratio. The main idea is coming through AHIS model which was presented by Mojtaba Sedigh Fazli and J.F. Lebraty in 2013. In this communication, the hybrid module is substituted with genetic programming. Finally, the simulation has been conducted and compared to three different answers which were presented before the results show that Genetic programming results (acting like hybrid model) which support both Fuzzy Systems and Neural Networks, satisfy the research question considerably.
    Keywords: Efficient Market Hypothesis; Financial Forecasting; Chemicals; Artificial Intelligence; Genetic Programming; Decision Support System; Hybrid Neuro Fuzzy Model.
    Date: 2013–07
  7. By: Pierre Blanc; R\'emy Chicheportiche; Jean-Philippe Bouchaud
    Abstract: We decompose, within an ARCH framework, the daily volatility of stocks into overnight and intraday contributions. We find, as perhaps expected, that the overnight and intraday returns behave completely differently. For example, while past intraday returns affect equally the future intraday and overnight volatilities, past overnight returns have a weak effect on future intraday volatilities (except for the very next one) but impact substantially future overnight volatilities. The exogenous component of overnight volatilities is found to be close to zero, which means that the lion's share of overnight volatility comes from feedback effects. The residual kurtosis of returns is small for intraday returns but infinite for overnight returns. We provide a plausible interpretation for these findings, and show that our IntraDay/Overnight model significantly outperforms the standard ARCH framework based on daily returns for Out-of-Sample predictions.
    Date: 2013–09

This nep-for issue is ©2013 by Rob J Hyndman. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.