nep-for New Economics Papers
on Forecasting
Issue of 2014‒03‒01
seven papers chosen by
Rob J Hyndman
Monash University

  1. Nowcasting and forecasting economic growth in the euro area using principal components By Irma Hindrayanto; Siem Jan Koopman; Jasper de Winter
  2. Exchange Rate Predictability in a Changing World By Joseph P. Byrne; Dimitris Korobilis; Pinho J. Ribeiro
  3. The continuing power of the yield spread in forecasting recessions By Croushore, Dean; Marsten, Katherine
  4. Data-based priors for vector autoregressions with drifting coefficients By Korobilis, Dimitris
  5. How to measure the quality of financial tweets By Paola Cerchiello; Paolo Giudici
  6. Modelling spatiotemporal variability of temperature By Xiaofeng Cao; Ostap Okhrin; Martin Odening; Matthias Ritter
  7. Real-time versus Day-ahead Market Power in a Hydro-based Electricity Market By Mauritzen, Johannes; Tangerås, Thomas

  1. By: Irma Hindrayanto; Siem Jan Koopman; Jasper de Winter
    Abstract: Many empirical studies show that factor models have a relatively high forecast compared to alternative short-term forecasting models. These empirical findings have been established for different data sets and for different forecast horizons. However, choosing the appropriate factor model specification is still a topic of ongoing debate. Moreover, the forecast performance during the recent financial crisis is not well documented. In this study we investigate these two issues in depth. We empirically test the forecast performance of three factor model approaches and report our findings in an extended empirical out-of-sample forecasting competition for the euro area and its five largest countries over the period 1992-2012. Besides, we introduce two extensions to the existing factor models to make them more suited for real-time forecasting. We show that the factor models were able to systematically beat the benchmark autoregressive model, both before as well as during the financial crisis. The recently proposed collapsed dynamic factor model shows the highest forecast accuracy for the euro area and the majority of countries we analyzed. The improvement against the benchmark model can range up to 77%, depending on the country and forecast horizon.
    Keywords: Factor models; Principal component analysis; Forecasting, Kalman filter; State space method; Publication lag; Mixed frequency
    Date: 2014–01
  2. By: Joseph P. Byrne; Dimitris Korobilis; Pinho J. Ribeiro
    Abstract: An expanding literature articulates the view that Taylor rules are helpful in predicting exchange rates. In a changing world however, Taylor rule parameters may be subject to structural instabilities, for example during the Global Financial Crisis. This paper forecasts exchange rates using such Taylor rules with Time Varying Parameters (TVP) estimated by Bayesian methods. In core out-of-sample results, we improve upon a random walk benchmark for at least half, and for as many as eight out of ten, of the currencies considered. This contrasts with a constant parameter Taylor rule model that yields a more limited improvement upon the benchmark. In further results, Purchasing Power Parity and Uncovered Interest Rate Parity TVP models beat a random walk benchmark, implying our methods have some generality in exchange rate prediction.
    Keywords: Exchange Rate Forecasting; Taylor Rules; Time-Varying Parameters; Bayesian Methods.
    JEL: C53 E52 F31 F37 G17
    Date: 2014–02
  3. By: Croushore, Dean (Federal Reserve Bank of Philadelphia); Marsten, Katherine (University of Richmond)
    Abstract: In this paper, we replicate the main results of Rudebusch and Williams (2009), who show that the use of the yield spread in a probit model can predict recessions better than the Survey of Professional Forecasters. We investigate the robustness of their results in several ways: extending the sample to include the 2007-09 recession, changing the starting date of the sample, changing the ending date of the sample, using rolling windows of data instead of just an expanding sample, and using alternative measures of the \actual" value of real output. Our results show that the Rudebusch-Williams findings are robust in all dimensions.
    Keywords: Real-time data; Recession forecasts; Yield spread;
    Date: 2014–02–13
  4. By: Korobilis, Dimitris
    Abstract: This paper proposes full-Bayes priors for time-varying parameter vector autoregressions (TVP-VARs) which are more robust and objective than existing choices proposed in the literature. We formulate the priors in a way that they allow for straightforward posterior computation, they require minimal input by the user, and they result in shrinkage posterior representations, thus, making them appropriate for models of large dimensions. A comprehensive forecasting exercise involving TVP-VARs of different dimensions establishes the usefulness of the proposed approach.
    Keywords: TVP-VAR, shrinkage, data-based prior, forecasting
    JEL: C11 C22 C32 C52 C53 C63 E17 E58
    Date: 2014–01
  5. By: Paola Cerchiello (Department of Economics and Management, University of Pavia); Paolo Giudici (Department of Economics and Management, University of Pavia)
    Abstract: Twitter text data may be very useful to predict financial tangibles, such as share prices, as well as intangible assets, such as company reputation. While twitter data are becoming widely available to researchers, methods aimed at selecting which twitter data are reliable are, to our knowledge, not yet available. To overcome this problem, and allow to employ twitter data for nowcasting and forecasting purposes, in this contribution we propose an effective statistical method that formalises and extends a quality index employed in the context of the evaluation of academic research: the h-index. Our proposal will be tested on a list of twitterers described by the Financial Times as "the top financial tweeters to follow", for the year 2013. Using our methodology we rank these twitterers and provide confidence intervals to decide whether they are significantly different.
    Date: 2014–02
  6. By: Xiaofeng Cao; Ostap Okhrin; Martin Odening; Matthias Ritter
    Abstract: Forecasting temperature in time and space is an important precondition for both the design of weather derivatives and the assessment of the hedging effectiveness of index based weather insur-ance. In this article, we show how this task can be accomplished by means of Kriging techniques. Moreover, we compare Kriging with a dynamic semiparametric factor model (DSFM) that has been recently developed for the analysis of high dimensional financial data. We apply both methods to comprehensive temperature data covering a large area of China and assess their performance in terms of predicting a temperature index at an unobserved location. The results show that the DSFM performs worse than standard Kriging techniques. Moreover, we show how geographic basis risk inherent to weather derivatives can be mitigated by regional diversification.
    Keywords: weather insurance, semiparametric model, factor model, Kriging, geographic basis risk
    JEL: C14 C53 G32
    Date: 2014–02
  7. By: Mauritzen, Johannes (Research Institute of Industrial Economics (IFN)); Tangerås, Thomas (Research Institute of Industrial Economics (IFN))
    Abstract: We analyse in a theoretical framework the link between real-time and day-ahead market performance in a hydro-based and imperfectly competitive wholesale electricity market. Theoretical predictions of the model are tested on data from the Nordic power exchange, Nord Pool Spot (NPS).We reject the hypothesis that prices at NPS were at their competitive levels throughout the period under examination. The empirical approach uses equilibrium prices and quantities and does not rely on bid data nor on estimation of demand or marginal cost functions.
    Keywords: Hydro power; Market power; Nord Pool Spot
    JEL: D43 D92 L13 L94 Q41
    Date: 2014–02–19

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