nep-for New Economics Papers
on Forecasting
Issue of 2014‒07‒13
sixteen papers chosen by
Rob J Hyndman
Monash University

  1. Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging By Katarzyna Maciejowska; Jakub Nowotarski; Rafal Weron
  2. Forecasting Global Recessions in a GVAR Model of Actual and Expected Output in the G7 By Anthony Garratt; Kevin Lee; Kalvinder Shields
  3. Can Macroeconomists Get Rich Forecasting Exchange Rates? By Jesus Crespo Cuaresma; Mauro Costantini; Jaroslava Hlouskova
  4. Information Rigidities: Comparing Average and Individual Forecasts for a Large International Panel By Jonas Dovern; Ulrich Fritsche; Prakash Loungani; Natalia T. Tamirisa
  5. Do Forecasters Believe in Okun’s Law? An Assessment of Unemployment and Output Forecasts By Laurence M. Ball; João Tovar Jalles; Prakash Loungani
  6. Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory By Walid Chkili; Shawkat Hammoudeh; Duc Khuong Nguyen
  7. Exponential Smoothing, Long Memory and Volatility Prediction By Proietti, Tommaso
  8. Combination of forecasts across estimation windows: An application to air travel demand By Jungmittag, Andre
  9. Forecasting Corn and Sotbean Yields with Crop Conditions By Nicholas Jorgensen; Matthew Diersen
  10. Why prediction markets work : The role of information acquisition and endogenous weighting By Siemroth, Christoph
  11. How Effective Is Central Bank Forward Guidance? By Clemens J. M. Kool Author-Name-First Clemens J. M.; Daniel L. Thornton Author-Name-First Daniel L.
  12. Balancing Forecast Errors in Continuous-Trade Intraday Markets By Garnier, Ernesto; Madlener, Reinhard
  13. The effectiveness of non-standard monetary policy measures: evidence from survey data By Carlo Altavilla; Domenico Giannone
  14. Asymmetric Realized Volatility Risk By David E. Allen; Michael McAleer; and Marcel Scharth
  15. Does historical volatility term structure contain valuable in-formation for predicting volatility index futures? By Juliusz Jabłecki; Ryszard Kokoszczyński; Paweł Sakowski; Robert Ślepaczuk; Piotr Wójcik
  16. On-line estimation of ARMA models using Fisher-scoring By Abdelhamid Ouakasse; Guy Melard

  1. By: Katarzyna Maciejowska; Jakub Nowotarski; Rafal Weron
    Abstract: We examine possible accuracy gains from using factor models, quantile regression and forecast averaging for computing interval forecasts of electricity spot prices. We extend the Quantile Regression Averaging (QRA) approach of Nowotarski and Weron (2014) and use principal component analysis to automate the selection process from among a large set of individual forecasting models available for averaging. We show that the resulting Factor Quantile Regression Averaging (FQRA) approach performs very well for price (and load) data from the British power market. In terms of unconditional coverage, conditional coverage and the Winkler score, we find the FQRA-implied prediction intervals to be more accurate than those of the benchmark ARX model and the QRA approach.
    Keywords: Probabilistic forecasting; Prediction interval; Quantile regression; Factor model; Forecasts combination; Electricity spot price
    JEL: C22 C32 C38 C53 Q47
    Date: 2014–06–30
  2. By: Anthony Garratt; Kevin Lee; Kalvinder Shields
    Abstract: The forecasting performance of a Global VAR model of actual and expected outputs in the G7 economies is compared with that of alternative models to judge the usefulness of modelling cross-country interdependencies and employing survey data. Both effects are found to be important in calculating density forecasts, in forecasting the occurrence of recessionary events deï¬ned at the national and G7-wide levels and, through a novel ‘fair bet’ exercise, in decision-making based on forecasts. The analysis argues for a nuanced approach to presenting output predictions, avoiding simple point forecasts and focusing on features of future growth dynamics relevant to decision-makers.
    Keywords: Cross-country interactions, Survey expectations, Probability Forecasts, Global and National Recession, Forecast evaluation
    Date: 2014
  3. By: Jesus Crespo Cuaresma (Department of Economics, Vienna University of Economics and Business); Mauro Costantini (Department of Economics and Finance, Brunel University); Jaroslava Hlouskova (Institute for Advanced Studies, Vienna)
    Abstract: We provide a systematic comparison of the out-of-sample forecasts based on multivariate macroeconomic models and forecast combinations for the euro against the US dollar, the British pound, the Swiss franc and the Japanese yen. We use profit maximization measures based on directional accuracy and trading strategies in addition to standard loss minimization measures. When comparing predictive accuracy and profit measures, data snooping bias free tests are used. The results indicate that forecast combinations help to improve over benchmark trading strategies for the exchange rate against the US dollar and the British pound, although the excess return per unit of deviation is limited. For the euro against the Swiss franc or the Japanese yen, no evidence of generalized improvement in profit measures over the benchmark is found.
    Keywords: Exchange rate forecasting, forecast combination, multivariate time series models, profitability
    JEL: C53 F31 F37
    Date: 2014–06
  4. By: Jonas Dovern; Ulrich Fritsche; Prakash Loungani; Natalia T. Tamirisa
    Abstract: We study forecasts for real GDP growth using a large panel of individual forecasts from 36 advanced and emerging economies during 1989–2010. We show that the degree of information rigidity in average forecasts is substantially higher than that in individual forecasts. Individual level forecasts are updated quite frequently, a behavior more in line “noisy†information models (Woodford, 2002; Sims, 2003) than with the assumptions of the sticky information model (Mankiw and Reis, 2002). While there are cross-country variations in information rigidity, there is no systematic difference between advanced and emerging economies.
    Keywords: Economic forecasting;Economic growth;Developed countries;Emerging markets;Forecasting models;Rational inattention; aggregation bias; growth forecasts; information rigidity; forecast behaviour
    Date: 2014–02–12
  5. By: Laurence M. Ball; João Tovar Jalles; Prakash Loungani
    Abstract: This paper provides an assessment of the consistency of unemployment and output forecasts. We show that, consistent with Okun’s Law, forecasts of real GDP growth and the change in unemployment are negatively correlated. The Okun coefficient—the responsiveness of unemployment to growth—from forecasts is fairly similar to that in the data for various countries. Furthermore, revisions to unemployment forecasts are negatively correlated with revisions to real GDP forecasts. These results are based on forecasts taken from Consensus Economics for nine advanced countries since 1989.
    Keywords: Unemployment;Economic growth;Economic forecasting;Unemployment; forecast revisions; Okun’s Law; Great Recession; forecast assessment
    Date: 2014–02–10
  6. By: Walid Chkili; Shawkat Hammoudeh; Duc Khuong Nguyen
    Abstract: This paper explores the relevance of asymmetry and long memory in modeling and forecasting the conditional volatility and market risk of four widely traded commodities (crude oil, natural gas, gold, and silver). A broad set of the most popular linear and
    Keywords: commodity markets, GARCH models, asymmetries, long memory, volatility forecasts.
    JEL: C22 G17 Q47
    Date: 2014–06–23
  7. By: Proietti, Tommaso
    Abstract: Extracting and forecasting the volatility of financial markets is an important empirical problem. Time series of realized volatility or other volatility proxies, such as squared returns, display long range dependence. Exponential smoothing (ES) is a very popular and successful forecasting and signal extraction scheme, but it can be suboptimal for long memory time series. This paper discusses possible long memory extensions of ES and finally implements a generalization based on a fractional equal root integrated moving average (FerIMA) model, proposed originally by Hosking in his seminal 1981 article on fractional differencing. We provide a decomposition of the process into the sum of fractional noise processes with decreasing orders of integration, encompassing simple and double exponential smoothing, and introduce a lowpass real time filter arising in the long memory case. Signal extraction and prediction depend on two parameters: the memory (fractional integration) parameter and a mean reversion parameter. They can be estimated by pseudo maximum likelihood in the frequency domain. We then address the prediction of volatility by a FerIMA model and carry out a recursive forecasting experiment, which proves that the proposed generalized exponential smoothing predictor improves significantly upon commonly used methods for forecasting realized volatility.
    Keywords: Realized Volatility. Signal Extraction. Permanent-Transitory Decomposition. Fractional equal-root IMA model.
    JEL: C22 C53 G17
    Date: 2014–07–10
  8. By: Jungmittag, Andre
    Abstract: This paper applies combining forecasts of air travel demand generated from the same model but over different estimation windows. The combination approach used resorts to Pesaran and Pick (2011), but the empirical application is extended in several ways. The forecasts are based on a seasonal Box-Jenkins model (SARIMA), which is adequate to forecast monthly air travel demand with distinct seasonal patterns at the largest German airport Frankfurt am Main. Furthermore, forecasts with forecast horizons from one to twelve months-ahead, which are based on different average estimation windows, expanding windows and single rolling windows, are compared with baseline forecasts based on an expanding window of the observations after a structural break. The forecast exercise shows that the average window forecasts mostly outperform the alternative single window forecasts. --
    Keywords: Air travel demand,Combination of forecasts,Estimation windows,Structural breaks
    JEL: C22 C53 L93
    Date: 2014
  9. By: Nicholas Jorgensen (Deparment of Economics South Dakota State University); Matthew Diersen
    Date: 2014–05–09
  10. By: Siemroth, Christoph
    Abstract: In prediction markets, investors trade assets whose values are contingent on the occurrence of future events, like election outcomes. Prediction market prices have been shown to be consistently accurate forecasts of these outcomes, but we don't know why. I formally illustrate an information acquisition explanation. Traders with more wealth to invest have stronger incentives to acquire information about the outcome, thus tend to have better forecasts. Moreover, their trades have larger weight in the market. The interaction implies that a few well-endowed traders can move the asset price toward the true value. One implication for institutions aggregating information is to put more weight on votes of agents with larger stakes, which improves on equal weighting, unless prior distribution accuracy and stakes are negatively related.
    Keywords: Information Acquisition , Information Aggregation , Forecasting , Futures Markets , Prediction Markets
    JEL: D83 D84 G10
    Date: 2014
  11. By: Clemens J. M. Kool Author-Name-First Clemens J. M. (Utrecht University); Daniel L. Thornton Author-Name-First Daniel L. (Federal Reserve Bank of St. Louis)
    Abstract: This paper investigates the effectiveness of forward guidance for the central banks of four countries: New Zealand, Norway, Sweden, and the United States. We test whether forward guidance improved market participantsÕ ability to forecast future short-term and long-term rates. We find some evidence that forward guidance improved market participantsÕ ability to forecast short-term rates over relatively short forecast horizons in New Zealand, Norway and Sweden, but not so for the United States. Most effects are small, often insignificant, and vary across benchmarks. In addition, forward guidance induces convergence of survey forecasts for New Zealand, but less so for the other countries, in particular the United States.
    Keywords: monetary policy, central bank transparency, interest rates, term structure, forecasting.
    JEL: E52 E43 E47
    Date: 2014
  12. By: Garnier, Ernesto (RWTH Aachen University); Madlener, Reinhard (E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN))
    Abstract: Forecasting the production of photovoltaic (PV) and wind power systems inevitably implies inaccuracies. Therefore, sales made based on forecasts almost always require the vendor to make balancing efforts. In the absence of resources available within their own portfolios, operators can turn towards the intraday market in order to avoid an engagement in the imbalance market with the resulting surcharges and regulatory penalties. In this paper, we combine a novel trade value concept with options valuation and dynamic programming to optimize volume and timing decisions of an individual operator without market power when compensating PV or wind power forecast errors in the market. The model employs a multi-dimensional binomial lattice, with trade value maximized at every node to help formulating bids in view of correlated, uncertain production forecast and price patterns. Inspired by the German electricity market's characteristics, we test the sensitivity of the model's output – namely trade timing and trade volume – to changing uncertainty and transaction cost parameters in 50 different setups. It shows that the model effectively outbalances price against volumetric risks. Trades are executed early and with large batch sizes in the case of price volatility. In contrast, increasing forecast error uncertainty leads to trade delays. High transaction costs trigger batch size reductions and ultimately further trade delays. Running 10,000 simulations across ten scenarios, we find that the model translates its flexible trade execution into a competitive advantage vis-à-vis static bidding strategy alternatives.
    Keywords: Bidding strategy; Production forecast; Renewable energy; Options; Intraday market
    JEL: G12 Q42 Q47
    Date: 2014–02
  13. By: Carlo Altavilla (European Central Bank); Domenico Giannone (LUISS University of Rome, EIEF, ECARES and CEPR)
    Abstract: We assess the perception of professional forecasters regarding the effectiveness of unconventional monetary policy measures undertaken by the U.S. Federal Reserve after the collapse of Lehman Brothers. Using individual survey data, we analyse the changes in forecasting of bond yields around the announcement and implementation dates of non-standard monetary policies. The results indicate that bond yields are expected to drop significantly for at least one year after the announcement and the implementation of accommodative policies.
    Keywords: Survey of Professional Forecasters, Large Scale Asset Purchases, Quantitative Easing, Operation Twist, Forward Guidance, Tapering.
    JEL: E58 E65
    Date: 2014
  14. By: David E. Allen (University of Sydney, and University of South Australia, Australia); Michael McAleer (National Tsing Hua University, Taiwan; Erasmus University Rotterdam, Tinbergen Institute, the Netherlands; Complutense University Madrid, Spain); and Marcel Scharth (University of New South Wales, Australia)
    Abstract: In this paper we document that realized variation measures constructed from high-frequency returns reveal a large degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which forecasting errors in realized volatility are substantive. Even though returns standardized by ex post quadratic variation measures are nearly gaussian, this unpredictability brings considerably more uncertainty to the empirically relevant ex ante distribution of returns. Explicitly modeling this volatility risk is fundamental. We propose a dually asymmetric realized volatility model, which incorporates the fact that realized volatility series are systematically more volatile in high volatility periods. Returns in this framework display time varying volatility, skewness and kurtosis. We provide a detailed account of the empirical advantages of the model using data on the S&P 500 index and eight other indexes and stocks.
    Keywords: Realized volatility, volatility of volatility, volatility risk, value-at-risk, forecasting, conditional heteroskedasticity
    JEL: C58 G12
    Date: 2014–06–23
  15. By: Juliusz Jabłecki (Faculty of Economic Sciences, University of Warsaw); Ryszard Kokoszczyński (Faculty of Economic Sciences, University of Warsaw); Paweł Sakowski (Faculty of Economic Sciences, University of Warsaw); Robert Ślepaczuk (Faculty of Economic Sciences, University of Warsaw); Piotr Wójcik (Faculty of Economic Sciences, University of Warsaw)
    Abstract: We suggest that the term structure of volatility futures (e.g. VIX futures) shows a clear pattern of dependence on the current level of VIX index. At the low level of VIX (below 20) the term structure is highly upward sloping; at the high VIX level (over 30) it is strongly downward sloping. We use those features to better predict future volatility and index futures. We begin by introducing some quantitative measures of volatility term structure (VTS) and volatility risk premium (VRP). We use them further to estimate the distance between the actual value and the fair (model) value of the VTS. We find that this distance has significant predictive power for volatility futures and index futures and we use this feature to design a simple strategy to invest in VIX index futures and S&P500.
    Keywords: volatility term structure, volatility risk premium, volatility and index futures, realized volatility, implied volatility, investment strategies, returns forecasting, efficient risk and return measures
    JEL: G11 G14 G15 G23 C61 C22
    Date: 2014
  16. By: Abdelhamid Ouakasse; Guy Melard
    Abstract: Recursive estimation methods for time series models usually make use of recurrences for the vector of parameters, the modelerror and its derivatives with respect to the parameters, plus a recurrence for the Hessian of the model error. An alternativemethod is proposed in the case of an autoregressive-moving average model, where the Hessian is not updated but is replaced,at each time, by the inverse of the Fisher information matrix evaluated at the current parameter. The asymptotic properties,consistency and asymptotic normality, of the new estimator are obtained. Monte Carlo experiments indicate that the estimatesmay converge faster to the true values of the parameters than when the Hessian is updated. The paper is illustrated by anexample on forecasting the speed of wind.
    Date: 2014

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