nep-for New Economics Papers
on Forecasting
Issue of 2014‒12‒24
thirteen papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting the South African Inflation Rate: On Asymmetric Loss and Forecast Rationality By Christian Pierdzioch; Monique B. Reid; Rangan Gupta
  2. Microfounded Forecasting By Wagner Piazza Gaglianone; João Victor Issler
  3. Window Selection for Out-of-Sample Forecasting with Time-Varying Parameters By Inoue, Atsushi; Jin, Lu; Rossi, Barbara
  4. Forecasting Long Memory Series Subject to Structural Change: A Two-Stage Approach By Gustavo Fruet Dias; Fotis Papailias
  5. Information and Predictability: Bookmakers, Prediction Markets and Tipsters as Forecasters By James Reade
  6. Economic theory and forecasting: lessons from the literature By Giacomini, Raffaella
  7. Bayesian Combination for Inflation Forecasts: The Effects of a Prior Based on Central Banks’ Estimates By Luis F. Melo Velandia; Rubén A. Loaiza Maya; Mauricio Villamizar-Villegas
  8. Business confidence and forecasting of housing prices and rents in large German cities By Konstantin Kholodilin
  9. On the Selection of Common Factors for Macroeconomic Forecasting By Giovannelli, Alessandro; Proietti, Tommaso
  10. The stabilizing effect of hydro reservoir levels on intraday power prices under wind forecast errors By Mehtap Kilic; Elisa Trujillo-Baute
  11. Market Sentiment and Exchange Rate Directional Forecasting By Vasilios Plakandaras; Theophilos Papadimitriou; Periklis Gogas; Konstantinos Diamantaras
  12. Predicting the VIX and the Volatility Risk Premium: What's Credit and Commodity Volatility Risk Got To Do With It? By Andreou, Elena; Ghysels, Eric
  13. Predicting Stock Price Volatility by Analyzing Semantic Content in Media By Asgharian, Hossein; Sikström, Sverker

  1. By: Christian Pierdzioch (Department of Economics, Helmut-Schmidt-University); Monique B. Reid (Department of Economics, University of Stellenbosch); Rangan Gupta (Department of Economics, University of Pretoria)
    Abstract: Using forecasts of the inflation rate in South Africa, we study the rationality of forecasts and the shape of forecasters’ loss function. When we study micro-level data of individual forecasts, we find mixed evidence of an asymmetric loss function, suggesting that inflation forecasters are heterogeneous with respect to the shape of their loss function. We also find strong evidence that inflation forecasts are in line with forecast rationality. When we pool the data, and study sectoral inflation forecasts of financial analysts, trade unions, and the business sector, we find evidence for asymmetry in the loss function, and against forecast rationality. Upon comparing the micro-level results with those for pooled and sectoral data, we conclude that forecast rationality should be assessed based on micro-level data, and that freer access to this data would allow more rigorous analysis and discussion of the information content of the surveys.
    Keywords: inflation rate, forecasting, loss function, rationality
    JEL: C53 D82 E37
    Date: 2014
  2. By: Wagner Piazza Gaglianone; João Victor Issler
    Abstract: In this paper, we propose a microfounded framework to investigate a panel of forecasts (e.g. model-driven or survey-based) and the possibility to improve their out-of-sample forecast performance by employing a bias-correction device. Following Patton and Timmermann (2007), we theoretically justify the modeling of forecasts as function of the conditional expectation, based on the optimization problem of individual forecasters. This approach allows us to relax the standard assumption of mean squared error (MSE) loss function and, thus, to obtain optimal forecasts under more general functions. However, different from these authors, we apply our results to a panel of forecasts, in order to construct an optimal (combined) forecast. In this sense, a feasible GMM estimator is proposed to aggregate the information content of each individual forecast and optimally recover the conditional expectation. Our setup can be viewed as a generalization of the three-way forecast error decomposition of Davies and Lahiri (1995); and as an extension of the bias-corrected average forecast of Issler and Lima (2009). A real-time forecasting exercise using the Brazilian Focus survey illustrates the proposed methodology
    Date: 2014–12
  3. By: Inoue, Atsushi; Jin, Lu; Rossi, Barbara
    Abstract: While forecasting is a common practice in academia, government and business alike, practitioners are often left wondering how to choose the sample for estimating forecasting models. When we forecast inflation in 2014, for example, should we use the last 30 years of data or the last 10 years of data? There is strong evidence of structural changes in economic time series, and the forecasting performance is often quite sensitive to the choice of such window size. In this paper, we develop a novel method for selecting the estimation window size for forecasting. Specifically, we propose to choose the optimal window size that minimizes the forecaster's quadratic loss function, and we prove the asymptotic validity of our approach. Our Monte Carlo experiments show that our method performs quite well under various types of structural changes. When applied to forecasting US real output growth and inflation, the proposed method tends to improve upon conventional methods.
    Keywords: forecasting; GDP growth; inflation; instabilities; structural change
    JEL: C22 C52 C53
    Date: 2014–09
  4. By: Gustavo Fruet Dias (Aarhus University and CREATES); Fotis Papailias (Queen's University Belfast and quantf Research)
    Abstract: A two-stage forecasting approach for long memory time series is introduced. In the first step we estimate the fractional exponent and, applying the fractional differencing operator, we obtain the underlying weakly dependent series. In the second step, we perform the multi-step ahead forecasts for the weakly dependent series and obtain their long memory counterparts by applying the fractional cumulation operator. The methodology applies to stationary and nonstationary cases. Simulations and an application to seven time series provide evidence that the new methodology is more robust to structural change and yields good forecasting results.
    Keywords: Forecasting, Spurious Long Memory, Structural Change, Local Whittle
    JEL: C22 C53
    Date: 2014–12–15
  5. By: James Reade (Department of Economics, University of Reading)
    Abstract: The more information is available, and the more predictable are events, the better forecasts ought to be. In this paper forecasts by bookmakers, prediction markets and tipsters are evaluated for a range of events with varying degrees of predictability and information availability. All three types of forecast represent dierent structures of information processing and as such would be expected to perform dierently. By and large, events that are more predictable, and for which more information is available, do tend to be forecast better.
    Date: 2014–10–05
  6. By: Giacomini, Raffaella
    Abstract: Does economic theory help in forecasting key macroeconomic variables? This article aims to provide some insight into the question by drawing lessons from the literature. The definition of "economic theory" includes a broad range of examples, such as accounting identities, disaggregation and spatial restrictions when forecasting aggregate variables, cointegration and forecasting with Dynamic Stochastic General Equilibrium (DSGE) models. We group the lessons into three themes. The first discusses the importance of using the correct econometric tools when answering the question. The second presents examples of theory-based forecasting that have not proven useful, such as theory-driven variable selection and some popular DSGE models. The third set of lessons discusses types of theoretical restrictions that have shown some usefulness in forecasting, such as accounting identities, disaggregation and spatial restrictions, and cointegrating relationships. We conclude by suggesting that economic theory might help in overcoming the widespread instability that affects the forecasting performance of econometric models by guiding the search for stable relationships that could be usefully exploited for forecasting.
    Keywords: Bayesian methods; DSGE models; exponential tilting
    JEL: C52 C53
    Date: 2014–10
  7. By: Luis F. Melo Velandia; Rubén A. Loaiza Maya; Mauricio Villamizar-Villegas
    Abstract: Typically, central banks use a variety of individual models (or a combination of models) when forecasting inflation rates. Most of these require excessive amounts of data, time, and computational power; all of which are scarce when monetary authorities meet to decide over policy interventions. In this paper we use a rolling Bayesian combination technique that considers inflation estimates by the staff of the Central Bank of Colombia during 2002-2011 as prior information. Our results show that: 1) the accuracy of individual models is improved by using a Bayesian shrinkage methodology, and 2) priors consisting of staff's estimates outperform all other priors that comprise equal or zero-vector weights. Consequently, our model provides readily available forecasts that exceed all individual models in terms of forecasting accuracy at every evaluated horizon. Classification JEL: C22, C53, C11, E31.
    Date: 2014–11
  8. By: Konstantin Kholodilin
    Abstract: The role of the housing market in the everyday life of society is difficult to overestimate. The housing rents and prices directly affect standard of living of virtually every person. Housing loans constitute the largest liability of households and account for a large proportion of bank lending. In Germany, the housing accounts for more than a half of wealth of private households. It is well known that speculative price bubbles on real-estate markets are likely to trigger financial crises, which can, in turn spill, over to the real economy by producing deep recessions accompanied by huge employment reductions. Since the end of 2010, after more than a decade of falling real housing prices, strong rent and especially price increases have been observed in Germany. This raised doubts and fears in German society. On the one hand, it is feared that Germany can follow the path of Spain, Ireland, and other bubble countries that ended in a severe economic crisis. On the other hand, the tenants that constitute a majority of German population are afraid of substantial rent increases that will erode their welfare. The tenants' discontent takes a form of massive protests and manifestations endangering political stability in the country. For this reason of the major issues debated in during recent elections and ongoing coalition negotiations among two leading German parties CDU/CSU and SPD is the housing policy. Therefore, it is very important to be able to predict the dynamics of home rents and prices in the nearest future. In this paper, we evaluate the forecasting ability of 115 indicators to predict the prices and rents for existing and new housing in 71 German cities with population exceeding 100,000 persons. Above all, we are interested in whether the local business confidence indicators can allow substantially improving the forecasts, given the local nature of real-estate markets. The forecast accuracy of different predictors is tested in a framework of a quasi out-of-sample forecasting. Its results are quite heterogeneous. No single indicator appears to dominate all others for all cities and market segments. However, there are several predictors that are especially useful, namely business confidence at the national level, consumer confidence, and price-to-rent ratios. Even better forecast precision can be achieved by combining individual forecasts. On average, the forecast improvements attain about 20%, measured by reduction in RMSFE, compared to autoregressive model. In separate cases, however, the magnitude of improvement is about 50%.
    JEL: C21 C23 C53
    Date: 2014–11
  9. By: Giovannelli, Alessandro; Proietti, Tommaso
    Abstract: We address the problem of selecting the common factors that are relevant for forecasting macroeconomic variables. In economic forecasting using diffusion indexes the factors are ordered, according to their importance, in terms of relative variability, and are the same for each variable to predict, i.e. the process of selecting the factors is not supervised by the predictand. We propose a simple and operational supervised method, based on selecting the factors on the basis of their significance in the regression of the predictand on the predictors. Given a potentially large number of predictors, we consider linear transformations obtained by principal components analysis. The orthogonality of the components implies that the standard t-statistics for the inclusion of a particular component are independent, and thus applying a selection procedure that takes into account the multiplicity of the hypotheses tests is both correct and computationally feasible. We focus on three main multiple testing procedures: Holm’s sequential method, controlling the family wise error rate, the Benjamini-Hochberg method, controlling the false discovery rate, and a procedure for incorporating prior information on the ordering of the components, based on weighting the p-values according to the eigenvalues associated to the components. We compare the empirical performances of these methods with the classical diffusion index (DI) approach proposed by Stock and Watson, conducting a pseudo-real time forecasting exercise, assessing the predictions of 8 macroeconomic variables using factors extracted from an U.S. dataset consisting of 121 quarterly time series. The overall conclusion is that nature is tricky, but essentially benign: the information that is relevant for prediction is effectively condensed by the first few factors. However, variable selection, leading to exclude some of the low order principal components, can lead to a sizable improvement in forecasting in specific cases. Only in one instance, real personal income, we were able to detect a significant contribution from high order components.
    Keywords: Variable selection; Multiple testing; p-value weighting.
    JEL: C22 C32 C38 C53 E3 E32
    Date: 2014–11–30
  10. By: Mehtap Kilic (Erasmus School of Economics); Elisa Trujillo-Baute (Universitat de Barcelona & IEB)
    Abstract: The power system has to deal with three main sources of uncertainty: demand uncertainty and load prediction errors, failure of power plants and uncertainty of wind. The growing share of wind and other intermittent generation sources in the European supply increases the uncertainty about power production in day-ahead and longer-term predictions. As EU member states increase the deployment of wind power and other intermittent renewable energy sources, the intraday and balancing market will gain more interest, as additional demand for reserve and response operations is needed. Hence, it becomes relevant to analyse the effect of wind power forecasting errors on intraday power prices. A higher forecast error will increase the need of intraday markets to balance out the oversupply or deficit of wind power on an hourly basis. This oversupply or deficit can be corrected though flexible hydropower plants; however the power price is highly influenced by the fluctuations in the reservoir level (Huisman et. al [2013]). In this paper, we question to what extent hydropower a stabilizing effect has on the impact of wind forecast errors on NordPool intraday prices. To do so, we examine the peak and off peak imbalance power prices for the Scandinavian market (ELBAS) from 2011 until 2013 with a Markov regime-switching model in periods with low and high hydro reservoir levels. Results indicate that under wind forecast error, the use of hydropower capacity in intraday markets is proven to be an effective volatility control mechanism. However, the price stabilizing effect of hydropower capacity does not take place at all times.
    Keywords: Electricity, intraday prices, wind forecast error, Markov-switching models
    JEL: L11 Q41 C24
    Date: 2014
  11. By: Vasilios Plakandaras (Department of Economics, Democritus University of Thrace, Greece); Theophilos Papadimitriou (Department of Economics, Democritus University of Thrace, Greece); Periklis Gogas (Department of Economics, Democritus University of Thrace, Greece; The Rimini Centre for Economic Analysis, Italy); Konstantinos Diamantaras (Department of Information Technology, TEI of Thessaloniki, Greece)
    Abstract: The microstructural approach to the exchange rate market claims that order flows on a currency can accurately reflect the short-run dynamics its exchange rate. In this paper, instead of focusing on order flows analysis we employ an alternative microstructural approach: we focus on investors' sentiment on a given exchange rate as a possible predictor of its future evolution. As a proxy of investors' sentiment we use StockTwits posts, a message board dedicated to finance. Within StockTwits investors are asked to explicitly state their market expectations. We collect daily data on the nominal exchange rate of four currencies against the U.S. dollar and the extracted market sentiment for the year 2013. Employing econometric and machine learning methodologies we develop models that forecast in out-of-sample exercise the future direction of the four exchange rates. Our empirical findings reject the Efficient Market Hypothesis even in its weak form for all four exchange rates. Overall, we find evidence that investors' sentiment as expressed in public message boards can be an additional source of information regarding the future directional movement of the exchange rates to the ones proposed by economic theory.
    Date: 2014–11
  12. By: Andreou, Elena; Ghysels, Eric
    Abstract: This paper presents an innovative approach to extracting factors which are shown to predict the VIX, the S&P 500 Realized Volatility and the Variance Risk Premium. The approach is innovative along two different dimensions, namely: (1) we extract factors from panels of filtered volatilities - in particular large panels of univariate financial asset ARCH-type models and (2) we price equity volatility risk using factors which go beyond the equity class. These are volatility factors extracted from panels of volatilities of short-term funding and long-run corporate spreads as well as volatilities of energy and metals commodities returns and sport/future spreads.
    Keywords: ARCH filters; Factor asset pricing models
    JEL: C2 C5 G1
    Date: 2014–11
  13. By: Asgharian, Hossein (Department of Economics, Lund University); Sikström, Sverker (Department of Psychology, Lund University)
    Abstract: Current models for predicting volatility do not incorporate information flow and are solely based on historical volatilities. We suggest a method to quantify the semantic content of words in news articles about a company and use this as a predictor of its stock volatility. The results show that future stock volatility is better predicted by our method than the conventional models. We also analyze the functional role of text in media either as a passive documentation of past information flow or as an active source for new information influencing future volatility. Our data suggest that semantic content may take both roles.
    Keywords: volatility; information flow; latent semantic analysis; GARCH
    JEL: G19
    Date: 2014–11–20

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