nep-for New Economics Papers
on Forecasting
Issue of 2016‒03‒10
fourteen papers chosen by
Rob J Hyndman
Monash University

  1. The Role of Current Account Balance in Forecasting the US Equity Premium: Evidence from a Quantile Predictive Regression Approach By Rangan Gupta; Anandamayee Majumdar; Mark Wohar
  2. Forecasting Daily Stock Volatility Using GARCH-CJ Type Models with Continuous and Jump Variation By BOUSALAM, Issam; HAMZAOUI, Moustapha; ZOUHAYR, Otman
  3. A dynamic component model for forecasting high-dimensional realized covariance matrices By BAUWENS, L.; BRAIONE, M.; STORTI, G.
  4. The effect of interest rate and communication shocks on private inflation expectations By Paul Hubert
  5. Non-homogeneous boosting for predictor selection in ensemble post-processing By Jakob W. Messner; Georg J. Mayr; Achim Zeileis
  6. To combine or not to combine? Recent trends in electricity price forecasting By Jakub Nowotarski; Rafal Weron
  7. Solar energy production: Short-term forecasting and risk management By Cédric Join; Michel Fliess; Cyril Voyant; Frédéric Chaxel
  8. Revisiting the transitional dynamics of business-cycle phases with mixed frequency data By Marie Bessec
  9. Predicting Road Conditions with Internet Search By Nikos Askitas
  10. Macroeconomic Uncertainty Through the Lens of Professional Forecasters By Soojin Jo; Rodrigo Sekkel
  11. Sparse Change-Point Time Series Models By Dufays, A.; Rombouts, V.
  12. Two historical changes in the narrative of energy forecasts By Minh Ha-Duong; Franck Nadaud; Martin Jegard
  13. Forecasting the volatility of crude oil futures using intraday data By Benoît Sévi
  14. On short-term traffic flow forecasting and its reliability By Hassane Abouaissa; Michel Fliess; Cédric Join

  1. By: Rangan Gupta (Department of Economics, University of Pretoria); Anandamayee Majumdar (Center for Advanced Statistics and Econometrics, Soochow University, China); Mark Wohar (Department of Economics, University of Nebraska-Omaha, USA and Loughborough University, UK)
    Abstract: The purpose of this paper is to investigate whether the current account balance can help in forecasting the quarterly S&P500-based equity premium out-of-sample. We consider an out-of-sample period of 1970:Q3 to 2014:Q4, with a corresponding in-sample period of 1947:Q2 to 1970:Q2. We employ a quantile predictive regression model. The quantile-based approach is more informative relative to any linear model, as it investigates the ability of the current account to forecast the entire conditional distribution of the equity premium, rather than being restricted just to the conditional-mean. In addition, we employ a recursive estimation of both the conditional-mean and quantile predictive regression models over the out-of-sample period which allows for time-varying parameters in the forecast evaluation part of the sample for both these models. Our results indicate that unlike as suggested by the linear (mean-based) predictive regression model, the quantile regression model shows that the (changes in the) real current account balance contains significant out-of-sample information especially when the stock market is performing poorly (below the quantile value of 0.3), but not when the market is in normal to bullish modes (quantile value above 0.3). This result seems to be intuitive in the sense that, when the markets are performing average to well, that is performing around the median and above of the conditional distribution of the equity premium, the excess returns is inherently a random-walk and hence, no information, from a predictor (changes in the real current account balance) is necessary.
    Keywords: stock markets, current account, predictability, quantile regression
    JEL: C22 C53 F32 G10
    Date: 2016–02
  2. By: BOUSALAM, Issam; HAMZAOUI, Moustapha; ZOUHAYR, Otman
    Abstract: In this paper we decompose the realized volatility of the GARCH-RV model into continuous sample path variation and discontinuous jump variation to provide a practical and robust framework for non-parametrically measuring the jump component in asset return volatility. By using 5-minute high-frequency data of MASI Index in Morocco for the period (January 15, 2010 - January 29, 2016), we estimate parameters of the constructed GARCH and EGARCH-type models (namely, GARCH, GARCH-RV, GARCH-CJ, EGARCH, EGARCH-RV, and EGARCH-CJ) and evaluate their predictive power to forecast future volatility. The results show that the realized volatility and the continuous sample path variation have certain predictive power for future volatility while the discontinuous jump variation contains relatively less information for forecasting volatility. More interestingly, the findings show that the GARCH-CJ-type models have stronger predictive power for future volatility than the other two types of models. These results have a major contribution in financial practices such as financial derivatives pricing, capital asset pricing, and risk measures.
    Keywords: GARCH-CJ; Jumps variation; Realized volatility; MASI Index; Morocco.
    JEL: C22 F37 F47 G17
    Date: 2016–01–20
  3. By: BAUWENS, L. (Université catholique de Louvain, CORE, Belgium); BRAIONE, M. (Université catholique de Louvain, CORE, Belgium); STORTI, G. (Université catholique de Louvain, CORE, Belgium)
    Abstract: The Multiplicative MIDAS Realized DCC (MMReDCC) model of Bauwens et al. [5] decomposes the dynamics of the realized covariance matrix of returns into short-run transitory and long-run secular components where the latter reflects the effect of the continuously changing economic conditions. The model allows to obtain positive-definite forecasts of the realized covariance matrices but, due to the high number of parameters involved, estimation becomes unfeasible for large cross-sectional dimensions. Our contribution in this paper is twofold. First, in order to obtain a computationally feasible estimation procedure, we propose an algorithm that relies on the maximization of an iteratively re-computed moment-based profile likelihood function. We assess the finite sample properties of the proposed algorithm via a simulation study. Second, we propose a bootstrap procedure for generating multi-step ahead forecasts from the MMReDCC model. In an empirical application on realized covariance matrices for fifty equities, we find that the MMReDCC not only statistically outperforms the selected benchmarks in-sample, but also improves the out-of-sample ability to generate accurate multi-step ahead forecasts of the realized covariances.
    Keywords: Realized covariance, dynamic component models, multi-step forecasting, MIDAS, targeting, model confidence set
    Date: 2016–02–01
  4. By: Paul Hubert (OFCE – Sciences Po)
    Abstract: The European Central Bank publishes inflation projections quarterly. This paper aims at establishing empirically whether they influence private inflation forecasts and whether they may be considered as an enhanced means of implementing policy decisions by facilitating private agents’ information processing. We compare the effect of an ECB inflation projection shock to an ECB interest rate shock.We provide original evidence that ECB inflation projections do influence private inflation expectations positively. We find that ECB projections convey signals about future ECB rate movements. This paper suggests that ECB projections enable private agents to correctly interpret and predict policy decisions.
    Keywords: Monetary Policy, ECB, Private Forecasts, Influence, Structural VAR
    JEL: E52 E58
    Date: 2015–09–01
  5. By: Jakob W. Messner; Georg J. Mayr; Achim Zeileis
    Abstract: Non-homogeneous regression is often used to statistically post-process ensemble forecasts. Usually only ensemble forecasts of the predictand variable are used as input but other potentially useful information sources are ignored. Although it is straightforward to add further input variables, overfitting can easily deteriorate the forecast performance for increasing numbers of input variables. This paper proposes a boosting algorithm to estimate the regression coefficients while automatically selecting the most relevant input variables by restricting the coefficients of less important variables to zero. A case study with ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) shows that this approach effectively selects important input variables to clearly improve minimum and maximum temperature predictions at 5 central European stations.
    Keywords: non-homogeneous regression, variable selection, boosting, statistical ensemble post-processing
    Date: 2016–02
  6. By: Jakub Nowotarski; Rafal Weron
    Abstract: Essentially everyone agrees nowadays that electricity spot price forecasting is of prime importance to the energy business. A variety of methods and ideas have been tried over the years, with varying degrees of success. Yet, despite this diversity of models, it is impossible to select one single, most reliable approach. We argue here that combining forecasts – also known as averaging forecasts, aggregating experts, committee machines or ensemble averaging – is an idea worth considering. Using publicly available data from the Global Energy Forecasting Competition 2014 and four commonly used time series models, we show that for both point and probabilistic forecasts the quality of predictions can be improved if combined.
    Keywords: Electricity price forecasting; Combining forecasts; Ensemble averaging; Aggregating experts; Probabilistic forecasts
    JEL: C22 C32 C53 Q47
    Date: 2016–01–18
  7. By: Cédric Join (CRAN - Centre de Recherche en Automatique de Nancy - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique, INRIA Lille - Nord Europe - INRIA, AL.I.E.N. - ALgèbre pour Identification & Estimation Numériques - ALIEN); Michel Fliess (LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau] - Polytechnique - X - CNRS - Centre National de la Recherche Scientifique, AL.I.E.N. - ALgèbre pour Identification & Estimation Numériques - ALIEN); Cyril Voyant (SPE - Sciences pour l'environnement - Université Pascal Paoli - CNRS - Centre National de la Recherche Scientifique, Hôpital d'Ajaccio); Frédéric Chaxel (CRAN - Centre de Recherche en Automatique de Nancy - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique)
    Abstract: Electricity production via solar energy is tackled via short-term forecasts and risk management. Our main tool is a new setting on time series. It allows the definition of "confidence bands" where the Gaussian assumption, which is not satisfied by our concrete data, may be abandoned. Those bands are quite convenient and easily implementable. Numerous computer simulations are presented.
    Keywords: confidence bands,normality tests,risk,volatility,Solar energy,intelligent knowledge-based systems,time series,forecasts,persistence
    Date: 2016–06–28
  8. By: Marie Bessec (LEDa - Laboratoire d'Economie de Dauphine - Université Paris IX - Paris Dauphine)
    Abstract: This paper introduces a Markov-Switching model where transition probabilities depend on higher frequency indicators and their lags, through polynomial weighting schemes. The MSV-MIDAS model is estimated via maximum likel ihood methods. The estimation relies on a slightly modified version of Hamilton’s recursive filter. We use Monte Carlo simulations to assess the robustness of the estimation procedure and related test-statistics. The results show that ML provides accurate estimates, but they suggest some caution in the tests on the parameters involved in the transition probabilities. We apply this new model to the detection and forecast of business cycle turning points. We properly detect recessions in United States and United Kingdom by exploiting the link between GDP growth and higher frequency variables from financial and energy markets. Spread term is a particularly useful indicator to predict recessions in the United States, while stock returns have the strongest explanatory power around British turning points.
    Keywords: Markov-Switching,mixed frequency data,business cycles
    Date: 2015–06–22
  9. By: Nikos Askitas
    Abstract: Traffic jams are an important problem both on an individual and on a societal level and much research has been done on trying to explain their emergence. The mainstream approach to road traffic monitoring is based on crowdsourcing roaming GPS devices such as cars or cellphones. These systems are expectedly able to deliver good results in reflecting the immediate present. To my knowledge there is as yet no system which offers advance notice on road conditions. Google Search intensity for the German word stau (i.e. traffic jam) peaks2 hours ahead of the number of traffic jam reports as reported by the ADAC, a well known German automobile club and the largest of its kind in Europe. This is true both in the morning(7 am to 9 am) and in the evening (5 pm to 7 pm). I propose such searches as a way of forecasting road conditions. The main result of this paper is that after controlling for time of day and day of week effects we can still explain a significant portion of the variation of the number of traffic jam reports with Google Trends and we can thus explain well over 80% of the variation of road conditions using Google search activity. A one percent increase in Google stau searches implies a .4 percent increase of traffic jams. Our paper is a proof of concept that aggregate, timely delivered behavioural data can help fine tune modern societies.
    Keywords: stau, traffic jams, highways, road conditions, Google Trends, prediction,forecasting, complexity, endogeneity, behaviour, big data, data science,computational social science, complex systems
    JEL: R41
    Date: 2016
  10. By: Soojin Jo; Rodrigo Sekkel
    Abstract: We analyze the evolution of macroeconomic uncertainty in the United States, based on the forecast errors of consensus survey forecasts of different economic indicators. Comprehensive information contained in the survey forecasts enables us to capture a real-time subjective measure of uncertainty in a simple framework. We jointly model and estimate macroeconomic (common) and indicator-specific uncertainties of four indicators, using a factor stochastic volatility model. Our macroeconomic uncertainty has three major spikes, aligned with the 1973–75, 1980, and 2007–09 recessions, while other recessions were characterized by increases in indicator-specific uncertainties. We also demonstrate for the first time in the literature that the selection of data vintages substantially affects the relative size of jumps in estimated uncertainty series. Finally, our macroeconomic uncertainty has a persistent negative impact on real economic activity, rather than producing “wait-and-see” dynamics.
    Keywords: Business fluctuations and cycles, Econometric and statistical methods
    JEL: C38 E17 E32
    Date: 2016
  11. By: Dufays, A. (Université catholique de Louvain, CORE, Belgium); Rombouts, V. (ESSEC Business School)
    Abstract: Change-point time series specifications constitute flexible models that capture unknown structural changes by allowing for switches in the model parameters. Nevertheless most models suffer from an over-parametrization issue since typically only one latent state vari- able drives the breaks in all parameters. This implies that all parameters have to change when a break happens. We introduce sparse change-point processes, a new approach for detecting which parameters change over time. We propose shrinkage prior distributions allowing to control model parsimony by limiting the number of parameters which evolve from one structural break to another. We also give clear rules with respect to the choice of the hyper parameters of the new prior distributions. Well-known applications are re-visited to emphasize that many popular breaks are, in fact, due to a change in only a subset of the model parameters. It also turns out that sizeable forecasting improvements are made over recent change-point models.
    Keywords: Time series, Shrinkage prior, Change-point model, Online forecasting
    JEL: C11 C15 C22 C51
    Date: 2015–07–10
  12. By: Minh Ha-Duong (CIRED - Centre International de Recherche sur l'Environnement et le Développement - EHESS - École des hautes études en sciences sociales - AgroParisTech - AgroParisTech - CIRAD - Centre de coopération internationale en recherche agronomique pour le développement - École des Ponts ParisTech (ENPC) - CNRS - Centre National de la Recherche Scientifique, CleanED - Clean Energy and Sustainable Development Lab - USTH - Université des Sciences et des Technologies de Hanoi); Franck Nadaud (CIRED - Centre International de Recherche sur l'Environnement et le Développement - EHESS - École des hautes études en sciences sociales - AgroParisTech - AgroParisTech - CIRAD - Centre de coopération internationale en recherche agronomique pour le développement - École des Ponts ParisTech (ENPC) - CNRS - Centre National de la Recherche Scientifique); Martin Jegard (CIRED - Centre International de Recherche sur l'Environnement et le Développement - EHESS - École des hautes études en sciences sociales - AgroParisTech - AgroParisTech - CIRAD - Centre de coopération internationale en recherche agronomique pour le développement - École des Ponts ParisTech (ENPC) - CNRS - Centre National de la Recherche Scientifique)
    Abstract: A collection of 417 energy scenarios was assembled and harmonized to compare what they said about nuclear, fossil and renewable energy thirty years from their publication. Based on data analysis, we divide the recent history of the energy forecasting in three periods. The first is defined by a decline in nuclear optimism, approximately until 1990. The second by a stability of forecasts, approximately until 2005. The third by a rise in the forecasted share of renewable energy sources. We also find that forecasts tend to cohere, that is they have a low dispersion within periods compared to the change across periods.
    Keywords: energy,scenario,periodization
    Date: 2016–02–17
  13. By: Benoît Sévi
    Date: 2016–02–18
  14. By: Hassane Abouaissa (LGI2A - Laboratoire de Génie Informatique et d'Automatique de l'Artois - UA - Université d'Artois); Michel Fliess (LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau] - Polytechnique - X - CNRS - Centre National de la Recherche Scientifique, ALIEN); Cédric Join (CRAN - Centre de Recherche en Automatique de Nancy - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique, NON-A - Non-Asymptotic estimation for online systems - INRIA Lille - Nord Europe - INRIA - CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - Université Lille 1 - Sciences et technologies - INRIA - Ecole Centrale de Lille - Université de Lille Sciences humaines et sociales - CNRS - Centre National de la Recherche Scientifique, ALIEN)
    Abstract: Recent advances in time series, where deterministic and stochastic modelings as well as the storage and analysis of big data are useless, permit a new approach to short-term traffic flow forecasting and to its reliability, i.e., to the traffic volatility. Several convincing computer simulations, which utilize concrete data, are presented and discussed.
    Keywords: financial engineering, volatility, risk, persistence, time series, forecasts,road traffic, transportation control, management systems, intelligent knowledge-based systems
    Date: 2016–06–28

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