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In this issue we have:

  1. Evaluating Phillips curve based inflation forecasts in Europe: A note
    Croonenbroeck, Carsten; Stadtmann, Georg
  2. Forecasting Bank Leverage
    Gerhard Hambusch; Sherrill Shaffer
  3. Forecasting with Factor-Augmented Regression: A Frequentist Model Averaging Approach
    Xu Cheng; Bruce E. Hansen
  4. The predictive power of Google searches in forecasting unemployment
    Francesco D'Amuri; Juri Marcucci
  5. Which Aspects of Central Bank Transparency Matter? Constructing a Weighted Transparency Index
    Csaba Csávás; Szilárd Erhart; Dániel Felcser; Anna Naszodi
  6. Oil price density forecasts: Exploring the linkages with stock markets
    Francesco Ravazzolo; Marco J. Lombardi
  7. Evaluating a global vector autoregression for forecasting
    Neil R. Ericsson; Erica L. Reisman
  8. Credit spreads as predictors of real-time economic activity: a Bayesian Model-Averaging approach
    Jon Faust; Simon Gilchrist; Jonathan H. Wright; Egon Zakrajsek
  9. Federal Reserve Private Information in Forecasting Interest Rates
    B. Onur Tas
  10. Artificial Neural Networks and Aggregate Consumption Patterns in New Zealand
    Dan Farhat
  11. Forecasting through the rear-view mirror: data revisions and bond return predictability
    Eric Ghysels; Casidhe Horan; Emanuel Moench
  12. ants and voters maximum entropy prediction and agent based models with recruitment.
    Barde, Sylvain
  13. Comment on "Forecast Rationality Tests Based on Multi-Horizon Bounds" by Andrew Patton and Allan Timmermann. Journal of Business and Economic Statistics, No. 1, Vol. 30, 2012, pp.1-17.
    Kajal Lahiri

Contents.

  1. Evaluating Phillips curve based inflation forecasts in Europe: A note
    Date: 2012
    By: Croonenbroeck, Carsten
    Stadtmann, Georg
    URL: http://d.repec.org/n?u=RePEc:zbw:euvwdp:329&r=for
    We run out-of-sample forecasts for the inflation rate of 15 euro-zone countries using a NAIRU Phillips curve and a naïve reference model. Comparisons show that the naïve model returns better forecasts in almost all cases. We provide evidence that the Phillips curves' goodness of fit is rather high. However, forecasting power is comparatively low. --
    Keywords: Phillips Curve,Forecasting,Europe,RMSE
    JEL: C53
  2. Forecasting Bank Leverage
    Date: 2012-12-01
    By: Gerhard Hambusch (Finance Discipline Group, UTS Business School, University of Technology, Sydney)
    Sherrill Shaffer (Department of Economics and Finance, University of Wyoming)
    URL: http://d.repec.org/n?u=RePEc:uts:wpaper:176&r=for
    Standard early warning models to predict bank failures cannot be estimated during periods of few or zero failures, precluding any updating of such models during times of good performance. Here we address this problem using an alternative approach, forecasting the simple leverage ratio (equity/assets) as a continuous variable that does not suffer from the small sample problem. Out-of-sample performance shows some promise as a supplement to the standard approach, despite measurable deterioration in prediction accuracy during the crisis years.
    Keywords: bank leverage; forecasts; early warning
    JEL: G21
  3. Forecasting with Factor-Augmented Regression: A Frequentist Model Averaging Approach
    Date: 2012-10-01
    By: Xu Cheng (Department of Economics, University of Pennsylvania)
    Bruce E. Hansen (Department of Economics, University of Wisconsin-Madison)
    URL: http://d.repec.org/n?u=RePEc:pen:papers:12-046&r=for
    This paper considers forecast combination with factor-augmented regression. In this framework, a large number of forecasting models are available, varying by the choice of factors and the number of lags. We investigate forecast combination using weights that minimize the Mallows and the leave-h-out cross validation criteria. The unobserved factor regressors are estimated by principle components of a large panel with N predictors over T periods. With these generated regressors, we show that the Mallows and leave-h-out cross validation criteria are approximately unbiased estimators of the one-step-ahead and multi-step-ahead mean squared forecast errors, respectively, provided that N, T —› ∞. In contrast to well-known results in the literature, the generated-regressor issue can be ignored for forecast combination, without restrictions on the relation between N and T. Simulations show that the Mallows model averaging and leave-h-out cross-validation averaging methods yield lower mean squared forecast errors than alternative model selection and averaging methods such as AIC, BIC, cross validation, and Bayesian model averaging. We apply the proposed methods to the U.S. macroeconomic data set in Stock and Watson (2012) and find that they compare favorably to many popular shrinkage-type forecasting methods.
    Keywords: Cross-validation, factor models, forecast combination, generated regressors, Mallows
    JEL: C52
  4. The predictive power of Google searches in forecasting unemployment
    Date: 2012-11
    By: Francesco D'Amuri (Bank of Italy)
    Juri Marcucci (Bank of Italy)
    URL: http://d.repec.org/n?u=RePEc:bdi:wptemi:td_891_12&r=for
    We suggest the use of an index of Internet job-search intensity (the Google Index, GI) as the best leading indicator to predict the US monthly unemployment rate. We perform a deep out-of-sample forecasting comparison analyzing many models that adopt our preferred leading indicator (GI), the more standard initial claims or combinations of both. We find that models augmented with the GI outperform the traditional ones in predicting the unemployment rate for different out-of-sample intervals that start before, during and after the Great Recession. Google-based models also outperform standard ones in most state-level forecasts and in comparison with the Survey of Professional Forecasters. These results survive a falsification test and are also confirmed when employing different keywords. Based on our results for the unemployment rate, we believe that there will be an increasing number of applications using Google query data in other fields of economics.
    Keywords: Google econometrics, forecast comparison, keyword search, US unemployment, time series models
    JEL: C22
  5. Which Aspects of Central Bank Transparency Matter? Constructing a Weighted Transparency Index
    Date: 2012
    By: Csaba Csávás (Magyar Nemzeti Bank (central bank of Hungary))
    Szilárd Erhart (Magyar Nemzeti Bank (central bank of Hungary))
    Dániel Felcser (Magyar Nemzeti Bank (central bank of Hungary))
    Anna Naszodi (Magyar Nemzeti Bank (central bank of Hungary))
    URL: http://d.repec.org/n?u=RePEc:mnb:wpaper:2012/6&r=for
    In this paper we investigate the effect of central bank transparency on survey forecasts. Similar to Ehrmann et al. (2010), we find that greater transparency can reduce the degree of disagreement across individual forecasters and it can also improve the forecasting performance of survey respondents. However, our empirical approach is more rigorous than that of Ehrmann et al. (2010) as we test both for causality and misspecification. The analysis is carried out on a panel dataset that is much richer than those used by previous studies. This unique dataset allows us to identify the effects of various aspects of transparency separately and to assign weights to them reflecting their relative importance in reducing uncertainty. Finally, we construct a new composite measure of central bank transparency using the estimated weights.
    Keywords: central bank transparency, survey forecast, weighted transparency index, dynamic panel model, overlapping observations
    JEL: C53
  6. Oil price density forecasts: Exploring the linkages with stock markets
    Date: 2012-12
    By: Francesco Ravazzolo
    Marco J. Lombardi
    URL: http://d.repec.org/n?u=RePEc:bny:wpaper:0008&r=for
    In the recent years several commentators hinted at an increase of the correlation between equity and commodity prices, and blamed investment in commodity-related products for this. First, this paper investigates such claims by looking at various measures of correlation. Next, we assess to what extent correlations between oil and equity prices can be exploited for asset allocation. We develop a time-varying Bayesian Dynamic Conditional Correlation model for volatilities and correlations and find that joint modelling of oil and equity prices produces more accurate point and density forecasts for oil which lead to substantial benefits in portfolio wealth.
    Keywords: Oil price, stock price, density forecasting, correlation, Bayesian DCC
    JEL: C11
  7. Evaluating a global vector autoregression for forecasting
    Date: 2012
    By: Neil R. Ericsson
    Erica L. Reisman
    URL: http://d.repec.org/n?u=RePEc:fip:fedgif:1056&r=for
    Global vector autoregressions (GVARs) have several attractive features: multiple potential channels for the international transmission of macroeconomic and financial shocks, a standardized economically appealing choice of variables for each country or region examined, systematic treatment of long-run properties through cointegration analysis, and flexible dynamic specification through vector error correction modeling. Pesaran, Schuermann, and Smith (2009) generate and evaluate forecasts from a paradigm GVAR with 26 countries, based on Dées, di Mauro, Pesaran, and Smith (2007). The current paper empirically assesses the GVAR in Dées, di Mauro, Pesaran, and Smith (2007) with impulse indicator saturation (IIS)—a new generic procedure for evaluating parameter constancy, which is a central element in model-based forecasting. The empirical results indicate substantial room for an improved, more robust specification of that GVAR. Some tests are suggestive of how to achieve such improvements.
  8. Credit spreads as predictors of real-time economic activity: a Bayesian Model-Averaging approach
    Date: 2012
    By: Jon Faust
    Simon Gilchrist
    Jonathan H. Wright
    Egon Zakrajsek
    URL: http://d.repec.org/n?u=RePEc:fip:fedgfe:2012-77&r=for
    Employing a large number of financial indicators, we use Bayesian Model Averaging (BMA) to forecast real-time measures of economic activity. The indicators include credit spreads based on portfolios--constructed directly from the secondary market prices of outstanding bonds--sorted by maturity and credit risk. Relative to an autoregressive benchmark, BMA yields consistent improvements in the prediction of the cyclically-sensitive measures of economic activity at horizons from the current quarter out to four quarters hence. The gains in forecast accuracy are statistically significant and economically important and owe almost exclusively to the inclusion of credit spreads in the set of predictors.
  9. Federal Reserve Private Information in Forecasting Interest Rates
    Date: 2012-12
    By: B. Onur Tas
    URL: http://d.repec.org/n?u=RePEc:tob:wpaper:1206&r=for
  10. Artificial Neural Networks and Aggregate Consumption Patterns in New Zealand
    Date: 2012-12
    By: Dan Farhat (Department of Economics, University of Otago, New Zealand)
    URL: http://d.repec.org/n?u=RePEc:otg:wpaper:1205&r=for
    This study uses artificial neural networks (ANNs) to reproduce aggregate per-capita consumption patterns for the New Zealand economy. Results suggest that non-linear ANNs can outperform a linear econometric model at out-of-sample forecasting. The best ANN at matching in-sample data, however, is rarely the best predictor. To improve the accuracy of ANNs using only in-sample information, methods for combining heterogeneous ANN forecasts are explored. The frequency that an individual ANN is a top performer during in-sample training plays a beneficial role in consistently producing accurate out-of-sample patterns. Possible avenues for incorporating ANN structures into social simulation models of consumption are discussed.
    Keywords: International Migration; International Agreements; Regional Labour Markets
    JEL: F22
  11. Forecasting through the rear-view mirror: data revisions and bond return predictability
    Date: 2012
    By: Eric Ghysels
    Casidhe Horan
    Emanuel Moench
    URL: http://d.repec.org/n?u=RePEc:fip:fednsr:581&r=for
    Real-time macroeconomic data reflect the information available to market participants, whereas final data—containing revisions and released with a delay—overstate the information set available to them. We document that the in-sample and out-of-sample Treasury return predictability is significantly diminished when real-time as opposed to revised macroeconomic data are used. In fact, much of the predictive information in macroeconomic time series is due to the data revision and publication lag components.
    Keywords: Real-time data ; Treasury bonds ; Government securities ; Time-series analysis ; Rate of return ; Macroeconomics
  12. ants and voters maximum entropy prediction and agent based models with recruitment.
    Date: 2012-10
    By: Barde, Sylvain (Sciences Po)
    URL: http://d.repec.org/n?u=RePEc:ner:sciepo:info:hdl:2441/f4rshpf3v1umfa09l8sao0qa3&r=for
    Maximum entropy predictions are made for the Kirman ant model as well as the Abrams-Strogatz model of language competition, also known as the voter model. In both cases the maximum entropy methodology provides good predictions of the limiting distribution of states, as was already the case for the Schelling model of segregation. An additional contribution, the analysis of the models reveals the key role played by relative entropy and the model in controlling the time horizon of the prediction.
    Keywords: information entropy;
  13. Comment on "Forecast Rationality Tests Based on Multi-Horizon Bounds" by Andrew Patton and Allan Timmermann. Journal of Business and Economic Statistics, No. 1, Vol. 30, 2012, pp.1-17.
    Date: 2012
    By: Kajal Lahiri
    URL: http://d.repec.org/n?u=RePEc:nya:albaec:12-10&r=for

This nep–for issue is ©2012 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, it must include this copyright notice. It may not be sold, or placed in something else for sale.
General information on the NEP project can be found at http://nep.repec.org/. For comments please write to the director of NEP, Marco Novarese at < director @ nep point repec point org >.