
on Forecasting 
By:  Kajal Lahiri; Xuguang Sheng 
Abstract:  Using a standard decomposition of forecasts errors into common and idiosyncratic shocks, we show that aggregate forecast uncertainty can be expressed as the disagreement among the forecasters plus the perceived variability of future aggregate shocks. Thus, the reliability of disagreement as a proxy for uncertainty will be determined by the stability of the forecasting environment, and the length of the forecast horizon. Using density forecasts from the Survey of Professional Forecasters, we find direct evidence in support of our hypothesis. Our results support the use of GARCHtype models, rather than the ex post squared errors in consensus forecasts, to estimate the ex ante variability of aggregate shocks as a component of aggregate uncertainty. 
Date:  2009 
URL:  http://d.repec.org/n?u=RePEc:nya:albaec:0906&r=for 
By:  Kajal Lahiri; Xuguang Sheng 
Abstract:  Using a Bayesian learning model with heterogeneity across agents, our study aims to identify the relative importance of alternative pathways through which professional forecasters disagree and reach consensus on the term structure of inflation and real GDP forecasts, resulting in different patterns of forecast accuracy. Forecast disagreement arises from two primary sources in our model: differences in the initial prior beliefs, and differences in the interpretation of new public information. Estimated model parameters, together with two separate case studies on (i) the dynamics of forecast disagreement in the aftermath of the 9/11 terrorist attack in the U.S. and (ii) the successful inflation targeting experience in Italy after 1997, firmly establish the importance of these two pathways to expert disagreement, and help to explain the relative forecasting accuracy of these two macroeconomic variables. 
Date:  2009 
URL:  http://d.repec.org/n?u=RePEc:nya:albaec:0905&r=for 
By:  Jan J. J. Groen (Federal Reserve Bank of New York); Richard Paap; Francesco Ravazzolo (Norges Bank (Central Bank of Norway)) 
Abstract:  This paper revisits ination forecasting using reduced form Phillips curve forecasts, i.e., inflation forecasts using activity and expectations variables. We propose a Phillips curvetype model that results from averaging across different regression specifications selected from a set of potential predictors. The set of predictors includes lagged values of inflation, a host of real activity data, term structure data, nominal data and surveys. In each of the individual specifications we allow for stochastic breaks in regression parameters, where the breaks are described as occasional shocks of random magnitude. As such, our framework simultaneously addresses structural change and model certainty that unavoidably affects Phillips curve forecasts. We use this framework to describe PCE deflator and GDP deflator inflation rates for the United States across the postWWII period. Over the full 19602008 sample the framework indicates several structural breaks across different combinations of activity measures. These breaks often coincide with, amongst others, policy regime changes and oil price shocks. In contrast to many previous studies, we find less evidence for autonomous variance breaks and inflation gap persistence. Through a realtime outofsample forecasting exercise we show that our model specification generally provides superior onequarter and oneyear ahead forecasts for quarterly inflation relative to a whole range of forecasting models that are typically used in the literature. 
Keywords:  Inflation forecasting, Phillips correlations, realtime data, structural breaks, model uncertainty, Bayesian model averaging. 
JEL:  C11 C22 C53 E31 
Date:  2009–08–01 
URL:  http://d.repec.org/n?u=RePEc:bno:worpap:2009_16&r=for 
By:  Dominique Guegan (Paris School of Economics  Centre d'Economie de la Sorbonne); Patrick Rakotomarolahy (Centre d'Economie de la Sorbonne) 
Abstract:  Forecasting current quarter GDP is a permanent task inside the central banks. Many models are known and proposed to solve this problem. Thanks to new results on the asymptotic normality of the multivariate knearest neighbor regression estimate, we propose an interesting and new approach to solve in particular the forecasting of economic indicators, included GDP modelling. Considering dependent mixing data sets, we prove the asymptotic normality of multivariate knearest neighbor regression estimate under weak conditions, providing confidence intervals for point forecasts. We introduce an application for economic indicators of euro area, and compare our method with other classical ARMAGARCH modelling. 
Keywords:  Multivariate knearest neighbor, asymptotic normality of the regression, mixing time series, confidence intervals, forecasts, economic indicators, Euro area. 
JEL:  C22 C53 E32 
Date:  2009–07 
URL:  http://d.repec.org/n?u=RePEc:mse:cesdoc:09050&r=for 
By:  Kajal Lahiri; Fushang Liu 
Abstract:  Abstract: We consider how to use information from reported density forecasts from surveys to identify asymmetry in forecasters¡¯ loss functions. We show that, for the three common loss functions  LinLin, Linex, and QuadQuad  we can infer the direction of loss asymmetry by just comparing point forecasts and the central tendency (mean or median) of the underlying density forecasts. If we know the entire distribution of the density forecast, we can calculate the loss function parameters based on the first order condition of forecast optimality. This method is applied to forecasts for annual real output growth and inflation obtained from the Survey of Professional Forecasters (SPF). We find that forecasters treat underprediction of real output growth more dearly than overprediction, reverse is true for inflation. 
Date:  2009 
URL:  http://d.repec.org/n?u=RePEc:nya:albaec:0903&r=for 
By:  Martin Ellison; Thomas J. Sargent 
Abstract:  We defend the forecasting performance of the FOMC from the recent criticism of Christina and David Romer. Our argument is that the FOMC forecasts a worstcase scenario that it uses to design decisions that will work well enough (are robust) despite possible misspecification of its model. Because these FOMC forecasts are not predictions of what the FOMC expects to occur under its model, it is inappropriate to compare their performance in a horse race against other forecasts. Our interpretation of the FOMC as a robust policymaker can explain all the findings of the Romers and rationalises differences between FOMC forecasts and forecasts published in the Greenbook by the staff of the Federal Reserve System. 
Keywords:  Forecasting, Monetary policy, Robustness 
JEL:  C53 E52 E58 
Date:  2009 
URL:  http://d.repec.org/n?u=RePEc:oxf:wpaper:457&r=for 
By:  Silvia Muzzioli 
Abstract:  The aim of this paper is twofold: to investigate how the information content of implied volatility varies according to moneyness and option type and to compare the latter option based forecasts with historical volatility in order to see if they subsume all the information contained in the latter. We run a horse race of different implied volatility estimates: at the money and out of the money call and put implied volatilities and average implied that is a weighted average of at the money call and put implied volatilities with weights proportional to trading volume. Two hypotheses are tested: unbiasedness and efficiency of the different volatility forecasts. The investigation is pursued in the Dax index options market, by using synchronous prices matched in a one minute interval. The results highlight that the information content of implied volatility has a humped shape, with out of the money options being less informative than at the money ones. Overall, the best forecast is at the money put implied volatility: it is unbiased (after a constant adjustment) and efficient, in that it subsumes all the information contained in historical volatility. 
Keywords:  implied Volatility; volatility Smile; volatility forecasting; option type 
JEL:  G13 G14 
Date:  2009–07 
URL:  http://d.repec.org/n?u=RePEc:mod:depeco:0617&r=for 
By:  Karina Gallardo (School of Economic Sciences, Washington State University) 
Abstract:  Prediction markets are becoming a widely used tool to predict outcomes as diverse as presidential election results, movie box office receipts, corporate earnings, and football scores. Prediction markets allow individuals to buy and sell, in an active market, contracts that pay money if an event occurs on or before a specified date. Probably the most well known prediction markets are the Iowa Electronic Markets, which are primarily used to forecast the outcomes of political elections. For example, people trade contracts that pay $1 if Candidate A wins and $0 if Candidate B wins. Participants in the market buy and sell the contracts depending on the expected success of each candidate. If the “going price” of the contract is $0.60, this indicates, under certain assumptions, a 60% chance that Candidate A will win the presidential election. 
Date:  2009–09 
URL:  http://d.repec.org/n?u=RePEc:wsu:wpaper:gallardo3&r=for 