nep-for New Economics Papers
on Forecasting
Issue of 2011‒10‒22
seven papers chosen by
Rob J Hyndman
Monash University

  1. Incorporating theoretical restrictions into forecasting by projection methods By Giacomini, Raffaella; Ragusa, Giuseppe
  2. What does financial volatility tell us about macroeconomic fluctuations? By Chauvet, Marcelle; Senyuz, Zeynep; Yoldas, Emre
  3. Forecasting Inflation using Commodity Price Aggregates By Yu-chin Chen; Stephen J. Turnovsky; Eric Zivot
  4. Some lessons from the financial crisis for the economic analysis By Geoff Kenny; Julian Morgan
  5. Forecasting seasonality in prices of potatoes and onions: challenge between geostatistical models, neuro fuzzy approach and Winter method By Amiri, Arshia; Bakhshoodeh, Mohamad; Najafi, Bahaeddin
  6. Markov Switching Models in Empirical Finance By Massimo Guidolin
  7. Balancing and Intraday Market Design: Options for Wind Integration By Frieder Borggrefe; Karsten Neuhoff

  1. By: Giacomini, Raffaella; Ragusa, Giuseppe
    Abstract: We propose a method for modifying a given density forecast in a way that incorporates the information contained in theory-based moment conditions. An example is "improving" the forecasts from atheoretical econometric models, such as factor models or Bayesian VARs, by ensuring that they satisfy theoretical restrictions given for example by Euler equations or Taylor rules. The method yields a new density (and thus point-) forecast which has a simple and convenient analytical expression and which by construction satisfies the theoretical restrictions. The method is flexible and can be used in the realistic situation in which economic theory does not specify a likelihood for the variables of interest, and thus cannot be readily used for forecasting.
    Keywords: Bayesian VAR; Euler conditions; Exponential tilting; Forecast comparisons
    JEL: C53
    Date: 2011–10
  2. By: Chauvet, Marcelle; Senyuz, Zeynep; Yoldas, Emre
    Abstract: This paper provides an extensive analysis of the predictive ability of financial volatility measures for economic activity. We construct monthly measures of aggregated and industry-level stock volatility, and bond market volatility from daily returns. We model log financial volatility as composed of a long-run component that is common across all series, and a short-run component. If volatility has components, volatility proxies are characterized by large measurement error, which veils analysis of their fundamental information and relationship with the economy. We find that there are substantial gains from using the long term component of the volatility measures for linearly projecting future economic activity, as well as for forecasting business cycle turning points. When we allow for asymmetry in the long-run volatility component, we find that it provides early signals of upcoming recessions. In a real-time out-of-sample analysis of the last recession, we find that these signals are concomitant with the first signs of distress in the financial markets due to problems in the housing sector around mid-2007 and the implied chronology is consistent with the crisis timeline.
    Keywords: Realized Volatility; Business Cycles; Forecasting; Dynamic Factor Models; Markov Switching
    JEL: C32 E32 E44
    Date: 2010–10
  3. By: Yu-chin Chen; Stephen J. Turnovsky; Eric Zivot
    Abstract: This paper shows that for five small commodity-exporting countries that have adopted inflation targeting monetary policies, world commodity price aggregates have predictive power for their CPI and PPI inflation, particularly once possible structural breaks are taken into account. This conclusion is robust to using either disaggregated or aggregated commodity price indexes (although the former perform better), the currency denomination of the commodity prices, and to using mixed-frequency data. In pseudo out-of-sample forecasting, commodity indexes outperform the random walk and AR(1) processes, although the improvements over the latter are sometimes modest.
    Date: 2011–09
  4. By: Geoff Kenny (European Central Bank, Kaiserstrasse 29, 60311 Frankfurt am Main); Julian Morgan (European Central Bank, Kaiserstrasse 29, 60311 Frankfurt am Main)
    Abstract: The economics profession in general, and economic forecasters in particular, have faced some understandable criticism for their failure to predict the timing and severity of the recent economic crisis. In this paper, we offer some assessment of the performance of the Economic Analysis conducted at the ECB both in the run up to and since the onset of the crisis. Drawing on this assessment, we then offer some indications of how the analysis of economic developments could be improved looking forward. The key priorities identifi ed include the need to: i) extend existing tools and/or develop new tools to account for important feedback mechanisms, for instance, improved real-fi nancial linkages and non-linear dynamics; ii) develop ways to handle the complexity arising from the presence of multiple models and alternative economic paradigms; and iii) given the limitations of point forecasts, to further develop risk and scenario analysis around baseline projections. JEL Classification: E02, E30, E2, C53
    Date: 2011–10
  5. By: Amiri, Arshia; Bakhshoodeh, Mohamad; Najafi, Bahaeddin
    Abstract: This paper, we studied the ability of geostatistical models (ordinary kriging (OK) and Inverse distance weighting (IDW)), adaptive neuro-fuzzy inference system (ANFIS) and Winter method for prediction of seasonality in prices of potatoes and onions in Iran over the seasonal period 1986_2001. Results show that the best estimators in order are winter method, ANFIS and geostatistical methods. The results indicate that Winter and ANFIS had powerful results for prediction the prices while geostatistical models were not useful in this respect.
    Keywords: Price; Geostatistical model; Kiriging; Inverse distance weighting; Winter’s method; Adaptive neuro fuzzy inference system; Potatoes; Onions; Iran
    JEL: Q1 C53
    Date: 2011–10–13
  6. By: Massimo Guidolin
    Abstract: I review the burgeoning literature on applications of Markov regime switching models in empirical finance. In particular, distinct attention is devoted to the ability of Markov Switching models to fit the data, filter unknown regimes and states on the basis of the data, to allow a powerful tool to test hypotheses formulated in the light of financial theories, and to their forecasting performance with reference to both point and density predictions. The review covers papers concerning a multiplicity of sub-fields in financial economics, ranging from empirical analyses of stock returns, the term structure of default-free interest rates, the dynamics of exchange rates, as well as the joint process of stock and bond returns. JEL Classification Codes: G00, C00. Keywords: Markov switching, Regimes, Regime shifts, Nonlinearities, Predictability, Autoregressive Conditional Heteroskedasticity.
    Date: 2011
  7. By: Frieder Borggrefe; Karsten Neuhoff
    Abstract: EU Member States increase deployment of intermittent renewable energy sources to deliver the 20% renewable target formulated in the European Renewables Directive of 2008. To incorporate these intermittent sources, a power market needs to be flexible enough to accommodate short-term forecasts and quick turn transactions. This flexibility is particularly valuable with respect to wind energy, where wind forecast uncertainty decreases significantly in the final 24 hours before actual generation. Therefore, current designs of intraday and balancing markets need to be altered to make full use of the flexibility of the transmission system and the different generation technologies to effectively respond to increased uncertainty. This paper explores the current power market designs in European countries and North America and assesses these designs against criteria that evaluate whether they are able to adequately handle wind intermittency.
    Keywords: Power market design, integrating renewables, wind energy, balancing, intraday
    Date: 2011

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