nep-for New Economics Papers
on Forecasting
Issue of 2017‒05‒14
eight papers chosen by
Rob J Hyndman
Monash University

  1. On the importance of the long-term seasonal component in day-ahead electricity price forecasting. Part II – Probabilistic forecasting By Bartosz Uniejewski; Grzegorz Marcjasz; Rafal Weron
  2. Uncertainty and Forecasts of U.S. Recessions By Christian Pierdzioch; Rangan Gupta
  3. A Time Series Analysis-Based Forecasting Framework for the Indian Healthcare Sector By Jaydip Sen; Tamal Datta Chaudhuri
  4. The Quanto Theory of Exchange Rates By Kremens, Lukas; Martin, Ian
  5. Exchange rate forecasting with DSGE models By Marcin Kolasa; Michał Rubaszek; Michele Ca' Zorzi
  6. Clustering and forecasting inflation expectations using the World Economic Survey: the case of the 2014 oil price shock on inflation targeting countries By Hector M. Zarate-Solano; Daniel R. Zapata-Sanabria
  7. Multi-Period Trading via Convex Optimization By Stephen Boyd; Enzo Busseti; Steven Diamond; Ronald N. Kahn; Kwangmoo Koh; Peter Nystrup; Jan Speth
  8. Forecasting electricity prices through robust nonlinear models By Luigi Grossi; Fany Nan

  1. By: Bartosz Uniejewski; Grzegorz Marcjasz; Rafal Weron
    Abstract: A recent electricity price forecasting study has shown that the Seasonal Component AutoRegressive (SCAR) modeling framework, which consists of decomposing a series of spot prices into a trend-seasonal and a stochastic component, modeling them independently and then combining their forecasts, can yield more accurate point predictions than an approach in which the same autoregressive model is calibrated to the prices themselves. Here, we show that further accuracy gains can be achieved when the explanatory variables (load forecasts) are deseasonalized as well. More importantly, considering a novel extension of the SCAR concept to probabilistic forecasting and applying two methods of combining predictive distributions we find that (i) SCAR-type models nearly always significantly outperform the autoregressive benchmark but are in turn outperformed by combined SCAR forecasts, (ii) predictive distributions computed using Quantile Regression Averaging (QRA) outperform those obtained from historical simulation and bootstrap methods, and (iii) averaging over predictive distributions generally yields better probabilistic forecasts of electricity spot prices than averaging over quantiles.
    Keywords: Electricity spot price; Long-term seasonal component; Seasonal Component AutoRegressive (SCAR) model; Probabilistic forecasting; Quantile Regression Averaging (QRA); Pinball score
    JEL: C14 C22 C51 C53 Q47
    Date: 2017–05–03
  2. By: Christian Pierdzioch (Department of Economics, Helmut Schmidt University, Hamburg, Germany); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa)
    Abstract: We use a Boosted Regression Trees (BRT) approach to study the potentially nonlinear link between various standard predictors (stock-market returns, term spread, a short-term interest rate, among others), components of a news-based uncertainty index, and U.S recessions. The BRT approach shows that, according to a relative-importance measure, war-related uncertainty is among the top five predictors of recessions at three different forecast horizons. In the second half of the 20th century, uncertainty regarding the state of securities markets has gained in relative importance. Partial-dependence curves show that the probability of a recession is nonlinearly linked to war-related and securities-markets uncertainty. An analysis based on receiver-operating-characteristic (ROC) curves shows that including war-related uncertainty in the list of predictors improves out-of-sample forecasting performance at a longer-term forecasting horizon, where the predictive value of this component relative to other components of uncertainty has fallen in the second half of the 20th century. Estimation results for a dynamic version of the BRT approach recover the relative importance of various lags of government-related uncertainty for recession forecasting at a longer forecast horizon.
    Keywords: Recessions, Uncertainty, Forecasting, Boosted regression trees, ROC curves
    JEL: C53 E32 E37
    Date: 2017–05
  3. By: Jaydip Sen; Tamal Datta Chaudhuri
    Abstract: Designing efficient and robust algorithms for accurate prediction of stock market prices is one of the most exciting challenges in the field of time series analysis and forecasting. With the exponential rate of development and evolution of sophisticated algorithms and with the availability of fast computing platforms, it has now become possible to effectively and efficiently extract, store, process and analyze high volume of stock market data with diversity in its contents. Availability of complex algorithms which can execute very fast on parallel architecture over the cloud has made it possible to achieve higher accuracy in forecasting results while reducing the time required for computation. In this paper, we use the time series data of the healthcare sector of India for the period January 2010 till December 2016. We first demonstrate a decomposition approach of the time series and then illustrate how the decomposition results provide us with useful insights into the behavior and properties exhibited by the time series. Further, based on the structural analysis of the time series, we propose six different methods of forecasting for predicting the time series index of the healthcare sector. Extensive results are provided on the performance of the forecasting methods to demonstrate their effectiveness.
    Date: 2017–04
  4. By: Kremens, Lukas; Martin, Ian
    Abstract: We present a new, theoretically motivated, forecasting variable for exchange rates that is based on the prices of quanto index contracts, and show via panel regressions that the quanto forecast variable is a statistically and economically significant predictor of currency appreciation. We also test the quanto variable's ability to forecast differential currency appreciation out of sample, and find that it outperforms predictions based on uncovered interest parity, on purchasing power parity, and on a random walk.
    Keywords: carry trade; currency; Exchange rate; exchange rate forecast; Forecasting; predictability; quanto contracts
    JEL: F31 F37 F47 G12 G15
    Date: 2017–04
  5. By: Marcin Kolasa (Narodowy Bank Polski and Warsaw School of Economics); Michał Rubaszek (Narodowy Bank Polski and Warsaw School of Economics); Michele Ca' Zorzi (European Central Bank)
    Abstract: We run an exchange rate forecasting “horse race”, which highlights that three principles hold. First, forecasts should not replicate the high volatility of exchange rates observed in sample. Second, models should exploit the mean reversion of the real exchange rate over long horizons. Third, they should account for the international price co-movement seen in the data. Abiding by the first two principles an open-economy dynamic stochastic general equilibrium (DSGE) model performs well in forecasting the real but not the nominal exchange rate. Only approaches that conform to all three principles tend to outperform the random walk.
    Keywords: Forecasting; exchange rates; New Open Economy Macroeconomics; mean reversion
    JEL: C32 F31 F41 F47
    Date: 2017
  6. By: Hector M. Zarate-Solano (Banco de la República de Colombia); Daniel R. Zapata-Sanabria (Banco de la República de Colombia)
    Abstract: This paper examines inflation expectations of the World Economic Survey for ten inflation targeting countries. First, by a Self Organizing Maps methodology, we cluster the trajectory of agents inflation expectations using the beginning of the oil price shock occurred in June of 2014 as a benchmark in order to discriminate between those countries that anticipated the shock smoothly and those with brisk changes in expectations. Then, the expectations are modeled by artificial neural networks forecasting models. Second, for each country we investigate the information content of the quantitative survey forecast by comparing it to the average annual inflation based on national consumer price indices. The results indicate the presence of heterogeneity among countries to anticipate inflation under the oil shock and, also different patterns of accuracy to predict average annual inflation were found depending on the observed inflation trend. Classification JEL: C02, C222, C45, C63, E27
    Keywords: Inflation expectations, machine learning, self-organizing maps, nonlinear autoregressive neural network, expectation surveys
    Date: 2017–05
  7. By: Stephen Boyd; Enzo Busseti; Steven Diamond; Ronald N. Kahn; Kwangmoo Koh; Peter Nystrup; Jan Speth
    Abstract: We consider a basic model of multi-period trading, which can be used to evaluate the performance of a trading strategy. We describe a framework for single-period optimization, where the trades in each period are found by solving a convex optimization problem that trades off expected return, risk, transaction cost and holding cost such as the borrowing cost for shorting assets. We then describe a multi-period version of the trading method, where optimization is used to plan a sequence of trades, with only the first one executed, using estimates of future quantities that are unknown when the trades are chosen. The single-period method traces back to Markowitz; the multi-period methods trace back to model predictive control. Our contribution is to describe the single-period and multi-period methods in one simple framework, giving a clear description of the development and the approximations made. In this paper we do not address a critical component in a trading algorithm, the predictions or forecasts of future quantities. The methods we describe in this paper can be thought of as good ways to exploit predictions, no matter how they are made. We have also developed a companion open-source software library that implements many of the ideas and methods described in the paper.
    Date: 2017–04
  8. By: Luigi Grossi (Department of Economics (University of Verona)); Fany Nan (Joint Research Centre of EU (Ispra))
    Abstract: In this paper a robust approach to modelling electricity spot prices is introduced. Differently from what has been recently done in the literature on electricity price forecasting, where the attention has been mainly drawn by the prediction of spikes, the focus of this contribution is on the robust estimation of nonlinear SETARX models. In this way, parameters estimates are not, or very lightly, influenced by the presence of extreme observations and the large majority of prices, which are not spikes, could be better forecasted. A Monte Carlo study is carried out in order to select the best weighting function for GM-estimators of SETAR processes. A robust procedure to select and estimate nonlinear processes for electricity prices is introduced, including robust tests for stationarity and nonlinearity and robust information criteria. The application of the procedure to the Italian electricity market reveals the forecasting superiority of the robust GM-estimator based on the polynomial weighting function on the non-robust Least Squares estimator. Finally, the introduction of external regressors in the robust estimation of SETARX processes contributes to the improvement of the forecasting ability of the model.
    Keywords: Electricity price, Nonlinear time series, Price forecasting, Robust GM-stimator, Spikes, Threshold models
    Date: 2017–05

This nep-for issue is ©2017 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, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.