nep-for New Economics Papers
on Forecasting
Issue of 2014‒06‒02
eleven papers chosen by
Rob J Hyndman
Monash University

  1. Do High-Frequency Financial Data Help Forecast Oil Prices? The MIDAS Touch at Work By Baumeister, Christiane; Guérin, Pierre; Kilian, Lutz
  2. Anchoring the Yield Curve Using Survey Expectations By Altavilla, Carlo; Giacomini, Raffaella; Ragusa, Giuseppe
  3. “A multivariate neural network approach to tourism demand forecasting” By Oscar Claveria; Enric Monte; Salvador Torra
  4. No Arbitrage Priors, Drifting Volatilities, and the Term Structure of Interest Rates By Carriero, Andrea; Clark, Todd; Marcellino, Massimiliano
  5. Conditional forecasts and scenario analysis with vector autoregressions for large cross-sections By Banbura, Marta; Giannone, Domenico; Lenza, Michele
  6. Autoregressive augmentation of MIDAS regressions By Cláudia Duarte
  7. The Effectiveness of Non-Standard Monetary Policy Measures: Evidence from Survey Data By Carlo Altavilla; Domenico Giannone
  8. Predictive Power of Aggregate Short Interest By Yu, Eric Jinsan
  9. Markov-Switching Mixed-Frequency VAR Models By Foroni, Claudia; Guérin, Pierre; Marcellino, Massimiliano
  10. The pairwise approach to model a large set of disaggregates with common trends By Guillermo Carlomagnol; Antoni Espasa
  11. Estimating the impact of wind generation and wind forecast errors on energy prices and costs in Ireland By Swinand, Gregory P; O'Mahoney, Amy

  1. By: Baumeister, Christiane; Guérin, Pierre; Kilian, Lutz
    Abstract: The substantial variation in the real price of oil since 2003 has renewed interest in the question of how to forecast monthly and quarterly oil prices. There also has been increased interest in the link between financial markets and oil markets, including the question of whether financial market information helps forecast the real price of oil in physical markets. An obvious advantage of financial data in forecasting oil prices is their availability in real time on a daily or weekly basis. We investigate whether mixed-frequency models may be used to take advantage of these rich data sets. We show that, among a range of alternative high-frequency predictors, especially changes in U.S. crude oil inventories produce substantial and statistically significant real-time improvements in forecast accuracy. The preferred MIDAS model reduces the MSPE by as much as 16 percent compared with the no-change forecast and has statistically significant directional accuracy as high as 82 percent. This MIDAS forecast also is more accurate than a mixed-frequency real-time VAR forecast, but not systematically more accurate than the corresponding forecast based on monthly inventories. We conclude that typically not much is lost by ignoring high-frequency financial data in forecasting the monthly real price of oil.
    Keywords: Forecasts; Mixed frequency; Oil price; Real-time data
    JEL: C53 G14 Q43
    Date: 2013–12
  2. By: Altavilla, Carlo; Giacomini, Raffaella; Ragusa, Giuseppe
    Abstract: The dynamic behavior of the term structure of interest rates is difficult to replicate with models, and even models with a proven track record of empirical performance have underperformed since the early 2000s. On the other hand, survey expectations are accurate predictors of yields, but only for very short maturities. We argue that this is partly due to the ability of survey participants to incorporate information about the current state of the economy as well as forward-looking information such as that contained in monetary policy announcements. We show how the informational advantage of survey expectations about short yields can be exploited to improve the accuracy of yield curve forecasts given by a base model. We do so by employing a flexible projection method that anchors the model forecasts to the survey expectations in segments of the yield curve where the informational advantage exists and transmits the superior forecasting ability to all remaining yields. The method implicitly incorporates into yield curve forecasts any information that survey participants have access to, without the need to explicitly model it. We document that anchoring delivers large and significant gains in forecast accuracy for the whole yield curve, with improvements of up to 52% over the years 2000-2012 relative to the class of models that are widely adopted by financial and policy institutions for forecasting the term structure of interest rates.
    Keywords: blue chip analysts survey; exponential tilting; forecast performance; macroeconomic factors; monetary policy forward guidance; term structure models
    JEL: C5 E4 G1
    Date: 2013–11
  3. By: Oscar Claveria (Faculty of Economics, University of Barcelona); Enric Monte (Department of Signal Theory and Communications, Polytechnic University of Catalunya); Salvador Torra (Faculty of Economics, University of Barcelona)
    Abstract: This study compares the performance of different Artificial Neural Networks models for tourist demand forecasting in a multiple-output framework. We test the forecasting accuracy of three different types of architectures: a multi-layer perceptron network, a radial basis function network and an Elman neural network. We use official statistical data of inbound international tourism demand to Catalonia (Spain) from 2001 to 2012. By means of cointegration analysis we find that growth rates of tourist arrivals from all different countries share a common stochastic trend, which leads us to apply a multivariate out-of-sample forecasting comparison. When comparing the forecasting accuracy of the different techniques for each visitor market and for different forecasting horizons, we find that radial basis function models outperform multi-layer perceptron and Elman networks. We repeat the experiment assuming different topologies regarding the number of lags used for concatenation so as to evaluate the effect of the memory on the forecasting results, and we find no significant differences when additional lags are incorporated. These results reveal the suitability of hybrid models such as radial basis functions that combine supervised and unsupervised learning for economic forecasting with seasonal data.
    Keywords: forecasting; tourism demand; cointegration; multiple-output; artificial neural networks. JEL classification: L83; C53; C45; R11
    Date: 2014–05
  4. By: Carriero, Andrea; Clark, Todd; Marcellino, Massimiliano
    Abstract: We propose a method to produce density forecasts of the term structure of government bond yields that accounts for (i) the possible mispecification of an underlying Gaussian Affine Term Structure Model (GATSM) and (ii) the time varying volatility of interest rates. For this, we derive a Bayesian prior from a GATSM and use it to estimate the coefficients of a BVAR for the term structure, specifying a common, multiplicative, time varying volatility for the VAR disturbances. Results based on U.S. data show that this method significantly improves the precision of point and density forecasts of the term structure. While this paper focuses on term structure modelling, the proposed method can be applied for a wide range of alternative models, including DSGE models, and is a generalization of the method of Del Negro and Schorfheide (2004) to VARs featuring drifting volatilities. The method also generalizes the model of Giannone et al. (2012), by specifying hierarchically not only the prior variance but also the prior mean of the VAR coefficients. Our results show that both time variation in volatilities, and a hierarchical specification for the prior means, improve model fit and forecasting performance.
    Keywords: density forecasting; no arbitrage; stochastic volatility; Term structure
    JEL: C32 C53 G17
    Date: 2014–03
  5. By: Banbura, Marta; Giannone, Domenico; Lenza, Michele
    Abstract: This paper describes an algorithm to compute the distribution of conditional forecasts, i.e. projections of a set of variables of interest on future paths of some other variables, in dynamic systems. The algorithm is based on Kalman filtering methods and is computationally viable for large vector autoregressions (VAR) and dynamic factor models (DFM). For a quarterly data set of 26 euro area macroeconomic and financial indicators, we show that both approaches deliver similar forecasts and scenario assessments. In addition, conditional forecasts shed light on the stability of the dynamic relationships in the euro area during the recent episodes of financial turmoil and indicate that only a small number of sources drive the bulk of the fluctuations in the euro area economy.
    Keywords: Bayesian Shrinkage; Conditional Forecast; Dynamic Factor Model; Large Cross-Sections; Vector Autoregression
    JEL: C11 C13 C33 C53
    Date: 2014–04
  6. By: Cláudia Duarte
    Abstract: Focusing on the MI(xed) DA(ta) S(ampling) regressions for handling different sampling frequencies and asynchronous releases of information, alternative techniques for the autoregressive augmentation of these regressions are presented and discussed. For forecasting quarterly euro area GDP growth using a small set of selected indicators, the results obtained suggest that no specific kind of MIDAS regressions clearly dominates in terms of forecast accuracy. Nevertheless, alternatives to common-factor MIDAS regressions with autoregressive terms perform well and in some cases are the best performing regressions.
    JEL: C53 E37
    Date: 2014
  7. By: Carlo Altavilla; Domenico Giannone
    Abstract: We assess the perception of professional forecasters regarding the effectiveness of unconventionalmonetary policy measures undertaken by the U.S. Federal Reserve after the collapse of LehmanBrothers. Using individual survey data, we analyse the changes in forecasting of bond yields aroundthe announcement and implementation dates of non-standard monetary policies. The resultsindicate that bond yields are expected to drop significantly for at least one year after theannouncement 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: 2014–05
  8. By: Yu, Eric Jinsan
    Abstract: The short sale of a stock is motivated by financial profits an investor expects to gain from declining stock prices. Short interest, defined as the proportion of shares shorted to all outstanding shares for a given stock, represents the collective expectations of short sellers. While the variation in short interest at the firm level may be dominated by firm-specific expectations, the variation in an aggregate measure of short interest across a broad sample of stocks most likely reflects changing expectations of macroeconomic conditions. With this motivation, this paper examines the relationship between lagged aggregate short interest and cyclical changes in GDP using quarterly US data from 1973 to 2013. The results strongly suggest that lagged aggregate short interest is a statistically significant regressor in explaining cyclical changes in GDP at up to a 4 quarter lag. Moreover, these results do not change with the addition of control variables and are robust to the use of different filters to decompose the growth trend from the cyclical component of GDP.
    Keywords: Short Interest, Business Cycle Forecasting, Trend-Cycle Decomposition
    JEL: E32 E37 G17
    Date: 2014–05–09
  9. By: Foroni, Claudia; Guérin, Pierre; Marcellino, Massimiliano
    Abstract: This paper introduces regime switching parameters in the Mixed-Frequency VAR model. We first discuss estimation and inference for Markov-switching Mixed-Frequency VAR (MSMF-VAR) models. Next, we assess the finite sample performance of the technique in Monte-Carlo experiments. Finally, the MSMF-VAR model is applied to predict GDP growth and business cycle turning points in the euro area. Its performance is compared with that of a number of competing models, including linear and regime switching mixed data sampling (MIDAS) models. The results suggest that MSMF-VAR models are particularly useful to estimate the status of economic activity.
    Keywords: Fore-; Markov-switching; MIDAS; Mixed-frequency VAR; Nowcasting
    JEL: C53 E32 E37
    Date: 2014–02
  10. By: Guillermo Carlomagnol; Antoni Espasa
    Abstract: The objective of this paper is to model and forecast all the components of a macro orbusiness variable. Our contribution concerns cases with a large number (hundreds) ofcomponents where multivariate approaches are not feasible. We extend in several directions the pairwise approach originally proposed by Espasa and Mayo-Burgos(2013) and study its statistical properties. The pairwise approach consists on performing common features tests between the N(N-1)/2 pairs of series that exist in a group of N of them. Once this is done, groups of series that share common features can be formed. Next, all the components are forecast using single equation models that include the restrictions derived by the common features. In this paper we focus on discovering groups of components that share single common trends. The asymptotic properties of the procedure are studied analytically. Monte Carlo evidence on the small samples performance is provided and a small samples correction procedure designed. A comparison with a DFM alternative is also carried out, and results indicate that the pairwise approach dominates in many empirically relevant situations. A relevant advantage of the pairwise approach is that it does not need common features to be pervasive. A strategy for dealing with outliers and breaks in the context of the pairwise procedure is designed and its properties studied by Monte Carlo. Results indicate that the treatment of these observations may considerably improve the procedure's performance when series are 'contaminated'.
    Keywords: Common trends, Cointegration, Factor Models, Disaggregation, Forecast model selection, Forecast Combination, Outliers treatment
    Date: 2014–05
  11. By: Swinand, Gregory P; O'Mahoney, Amy
    Abstract: This paper studies the impact of wind generation on system costs and prices in Ireland. The need to mitigate climate change, achieve renewables energy targets, and use renewable sources of energy means that many countries are considering greater levels of wind generation in their power generation mix. The overall impact of wind generation on system costs and performance has only been studied recently, and often with limited actual data from power systems with increased wind penetration. The paper uses a unique dataset of half-hourly system demand, generation, wind forecast generation, and actual wind generation, along with Irish system marginal price (SMP) data from 2008 to autumn 2012. An econometric time-series model of SMP as a function of forecast and realized demand and wind generation is formed. The costs of balancing and system constraints are included in the cost of ‘uplift’, and thus the total cost of a variety of factors is included in our estimates for Ireland. Our results suggest that each 1% increase in wind generation reduces SMP in Ireland by about 0.06%, while each 1% wind forecast error increases SMP about 0.02%. In absolute terms, though, at the mean the impact of wind forecast errors is small, or about 0.4€cent/MWh-wind generated. However, the impact per MWh forecast error is about €1.
    Keywords: Cost function, econometrics, power generation economics, power system economics, wind power generation
    JEL: D0 D4 Q4 Q42
    Date: 2014

This nep-for issue is ©2014 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.