New forecasting papers 2008-08-18

In this issue we have: Post-Construction Evaluation of Traffic Forecast Accuracy ; Bayesian Demographic Modeling and Forecasting: An Application to U.S. Mortality ; Forecasting Elections from Voters’ Perceptions of Candidates’ Positions on Issues and Policies and more.

1. THE CALIBRATION OF PROBABILISTIC ECONOMIC FORECASTS

   Date:	2008-07
   By:	John Galbraith Simon Van Norden
   URL:	http://d.repec.org/n?u=RePEc:mcl:mclwop:2008-04&r=for
   A probabilistic forecast is the estimated probability with which a future event will satisfy a specified criterion. One interesting feature of such forecasts is their calibration, or the match between predicted probabilities and actual outcome probabilities. Calibration has been evaluated in the past by grouping probability forecasts into discrete categories. Here we show that we can do so without discrete groupings; the kernel estimators that we use produce efficiency gains and smooth estimated curves relating predicted and actual probabilities. We use such estimates to evaluate the empirical evidence on calibration error in a number of economic applications including recession and inflation prediction, using both forecasts made and stored in real time and pseudoforecasts made using the data vintage available at the forecast date. We evaluate outcomes using both first-release outcome measures as well as later, th! oroughly-revised data. We find strong evidence of incorrect calibration in professional forecasts of recessions and inflation. We also present evidence of asymmetries in the performance of inflation forecasts based on real-time output gaps.
   JEL:	C73 D6 D9 O1 Q20

2. Practical Volatility Modeling for Financial Market Risk Management

   Date:	2008-05-15
   By:	Shamiri, Ahmed Shaari, Abu Hassan Isa, Zaidi
   URL:	http://d.repec.org/n?u=RePEc:pra:mprapa:9790&r=for
   Being able to choose most suitable volatility model and distribution specification is a more demanding task. This paper introduce an analyzing procedure using the Kullback-Leibler information criteria (KLIC) as a statistical tool to evaluate and compare the predictive abilities of possibly misspecified density forecast models. The main advantage of this statistical tool is that we use the censored likelihood functions to compute the tail minimum of the KLIC, to compare the performance of a density forecast models in the tails. We include an illustrative simulation and an empirical application to compare a set of distributions, including symmetric/asymmetric distribution, and a family of GARCH volatility models. We highlight the use of our approach to a daily index, the Kuala Lumpur Composite index (KLCI). Our results shows that the choice of the conditional distribution appear to be a more dominant factor in deter! mining the adequacy of density forecasts than the choice of volatility model. Furthermore, the results support the Skewed for KLCI return distribution.
   Keywords:	Density forecast; Conditional distribution; Forecast accuracy; KLIC; GARCH models
   JEL:	D53 C32 C16 C52

3. Forecasting Using Functional Coefficients Autoregressive Models

   Date:	2008-06
   By:	Giancarlo Bruno (ISAE – Institute for Studies and Economic Analyses)
   URL:	http://d.repec.org/n?u=RePEc:isa:wpaper:98&r=for
   The use of linear parametric models for forecasting economic time series is widespread among practitioners, in spite of the fact that there is a large evidence of the presence of non-linearities in many of such time series. However, the empirical results stemming from the use of non-linear models are not always as good as expected. This has been sometimes associated to the difficulty in correctly specifying a non-linear parametric model. I this paper I cope with this issue by using a more general non-parametric approach, which can be used both as a preliminary tool for aiding in specifying a suitable parametric model and as an autonomous modelling strategy. The results are promising, in that the non-parametric approach achieve a good forecasting record for a considerable number of series.
   Keywords:	Non-linear Time-Series Models, Non-Parametric Models.
   JEL:	C52 C53

4. The Information Content of Money in Forecasting Euro Area Inflation

   Date:	2008-07-09
   By:	Helge Berger Emil Stavrev
   URL:	http://d.repec.org/n?u=RePEc:imf:imfwpa:08/166&r=for
   This paper contributes to the debate on the role of money in monetary policy by analyzing the information content of money in forecasting euro-area inflation. We compare the predictive performance within and among various classes of structural and empirical models in a consistent framework using Bayesian and other estimation techniques. We find that money contains relevant information for inflation in some model classes. Money-based New Keynesian DSGE models and VARs incorporating money perform better than their cashless counterparts. But there are also indications that the contribution of money has its limits. The marginal contribution of money to forecasting accuracy is often small, money adds little to dynamic factor models, and it worsens forecasting accuracy of partial equilibrium models. Finally, non-monetary models dominate monetary models in an all-out horserace.
   Keywords:	Working Paper , Euro Area , Money , Inflation , Forecasting models , Monetary policy , Economic models ,

5. Multivariate Fractionally Integrated APARCH Modeling of Stock Market Volatility: A multi-country study

   Date:	2008-07
   By:	Christian Conrad (University of Heidelberg, Department of Economics) Menelaos Karanasos (Brunel University, Dept. of Economics and Finance) Ning Zeng (Brunel University, Dept. of Economics and Finance)
   URL:	http://d.repec.org/n?u=RePEc:awi:wpaper:0472&r=for
   Tse (1998) proposes a model which combines the fractionally integrated GARCH formulation of Baillie, Bollerslev and Mikkelsen (1996) with the asymmetric power ARCH speci¯cation of Ding, Granger and Engle (1993). This paper analyzes the applicability of a multivariate constant conditional correlation version of the model to national stock market returns for eight countries. We ¯nd this multivariate speci¯cation to be generally applicable once power, leverage and long-memory e®ects are taken into consideration. In addition, we ¯nd that both the optimal fractional di®erencing parameter and power transformation are remarkably similar across countries. Out-of-sample evidence for the superior forecasting ability of the multivariate FIAPARCH framework is provided in terms of forecast error statistics and tests for equal forecast accuracy of the various models.
   Keywords:	Asymmetric Power ARCH, Fractional integration, Stock returns, Volatility forecast evaluation
   JEL:	C13 C22 C52

6. Forecasting inflation and tracking monetary policy in the euro area – does national information help?

   Date:	2008-06
   By:	Riccardo Cristadoro (Banca d’Italia, Research Department, via Nazionale 91, I – 00184 Rome, Italy.) Fabrizio Venditti (Banca d’Italia, Research Department, via Nazionale 91, I – 00184 Rome, Italy.) Giuseppe Saporito (Banca d’Italia, Research Department, via Nazionale 91, I – 00184 Rome, Italy.)
   URL:	http://d.repec.org/n?u=RePEc:ecb:ecbwps:20080900&r=for
   The ECB objective is set in terms of year on year growth rate of the Euro area HICP. Nonetheless, a good deal of attention is given to national data by market analysts when they try to anticipate monetary policy moves. In this paper we use the Generalized Dynamic Factor model to develop a set of core inflation indicators that, combining national data with area wide information, allow us to answer two related questions. The first is whether country specific data actually bear any relevance for the future path of area wide price growth, over and above that already contained in area wide data. The second is whether in order to track ECB monetary policy decisions it is useful to take into account national information and not only area wide statistics. In both cases our findings point to the conclusion that, once area wide information is properly taken into account, there is little to be gained from considering nationa! l idiosyncratic developments. JEL Classification: C25, E37, E52.
   Keywords:	Forecasting, dynamic factor model, inflation, Taylor rule, monetary policy.

7. Inflation Targeting and Communication: Should the Public Read Inflation Reports or Tea Leaves?

   Date:	2007-12
   By:	Ales Bulir Katerina Smidkova Viktor Kotlan David Navratil
   URL:	http://d.repec.org/n?u=RePEc:cnb:wpaper:2007/14&r=for
   Inflation-targeting central banks have a respectable track record at explaining their policy actions and corresponding inflation outturns. Using a simple forward-looking policy rule and an assessment of inflation reports, we provide a new methodology for the empirical evaluation of consistency in central bank communication. We find that the three communication tools—inflation targets, inflation forecasts, and verbal assessments of inflation factors contained in quarterly inflation reports—provided a consistent message in five out of six observations in our 2000–05 sample of Chile, the Czech Republic, Hungary, Poland, Thailand, and Sweden.
   Keywords:	Emerging markets, forecasting, inflation targeting, monetary policy, transparency.
   JEL:	E31 E43 E47 E58

8. Predicting global stock returns

   Date:	2008
   By:	Erik Hjalmarsson
   URL:	http://d.repec.org/n?u=RePEc:fip:fedgif:933&r=for
   I test for stock return predictability in the largest and most comprehensive data set analyzed so far, using four common forecasting variables: the dividend- and earnings-price ratios, the short interest rate, and the term spread. The data contain over 20,000 monthly observations from 40 international markets, including 24 developed and 16 emerging economies. In addition, I develop new methods for predictive regressions with panel data. Inference based on the standard fixed effects estimator is shown to suffer from severe size distortions in the typical stock return regression, and an alternative robust estimator is proposed. The empirical results indicate that the short interest rate and the term spread are fairly robust predictors of stock returns in developed markets. In contrast, no strong or consistent evidence of predictability is found when considering

Taken from the NEP-FOR mailing list edited by Rob Hyndman.