nep-for New Economics Papers
on Forecasting
Issue of 2016‒06‒18
eleven papers chosen by
Rob J Hyndman
Monash University

  1. Exchange rate forecasting with DSGE models By Ca' Zorzi, Michele; Kolasa, Marcin; Rubaszek, Michał
  2. On the design of data sets for forecasting with dynamic factor models By Rünstler, Gerhard
  3. Gold Futures Returns and Realized Moments: A Forecasting Experiment Using a Quantile-Boosting Approach By Matteo Bonato; Riza Demirer; Rangan Gupta; Christian Pierdzioch
  4. Central Bank Transparency and the consensus forecast: What does The Economist poll of forecasters tell us? By Emna Trabelsi
  5. Forecasting the Great Trade Collapse By Hakan Yilmazkuday
  6. Stock Return Predictability in South Africa: An Alternative Approach By Ailie Charteris and Barry Strydom
  7. Forecasting Airport Demand: Review of UK Airports Commission Forecasts and Scenarios By OECD
  8. Estimating and Forecasting Generalized Fractional Long Memory Stochastic Volatility Models By Shelton Peiris; Manabu Asai; Michael McAleer
  9. How Low Can House Prices Go? Estimating a Conservative Lower Bound By Alexander N. Bogin; Stephen D. Bruestle; William M. Doerner
  10. Reading Between the Lines: Prediction of Political Violence Using Newspaper Text By Hannes Mueller; Christopher Rauh
  11. Econometric-wavelet prediction in spatial aspect By Monika Hadas-Dyduch

  1. By: Ca' Zorzi, Michele; Kolasa, Marcin; Rubaszek, Michał
    Abstract: We run a real exchange rate forecasting "horse race", which highlights that two 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. Abiding by these principles, an open-economy DSGE model performs well in real exchange rate forecasting. However, it fails to forecast nominal exchange rates better than the random walk. We find that the root cause is its inability to predict domestic and foreign inflation. This shortcoming leads us toward simpler ways to outperform the random walk. JEL Classification: C32, F31, F37
    Keywords: exchange rates, forecasting, mean reversion, new open economy macroeconomics
    Date: 2016–05
  2. By: Rünstler, Gerhard
    Abstract: Forecasts from dynamic factor models potentially benefit from refining the data set by eliminating uninformative series. The paper proposes to use prediction weights as provided by the factor model itself for this purpose. Monte Carlo simulations and an empirical application to short-term forecasts of euro area, German, and French GDP growth from unbalanced monthly data suggest that both prediction weights and Least Angle Regressions result in improved nowcasts. Overall, prediction weights provide yet more robust results. JEL Classification: E37, C53, C51
    Keywords: dynamic factor models, forecasting, LARS, variable selection
    Date: 2016–04
  3. By: Matteo Bonato (Department of Economics and Econometrics, University of Johannesburg, South Africa.); Riza Demirer (Department of Economics and Finance, Southern Illinois University Edwardsville, USA); Rangan Gupta (Department of Economics, University of Pretoria); Christian Pierdzioch (Department of Economics, Helmut Schmidt University, Germany)
    Abstract: This paper proposes an iterative model-building approach known as quantile boosting to trace out the predictive value of realized volatility and skewness for gold futures returns. Controlling for several widely studied market- and sentiment-based variables, we examine the predictive value of realized moments across alternative forecast horizons and across the quantiles of the conditional distribution of gold futures returns. We find that the realized moments often significantly improve the predictive value of the estimated forecasting models at intermediate forecast horizons and across quantiles representing distressed market conditions. We argue that realized moments carry information that reflects investors’ tradeoff between diversification and skewed payoffs, particularly during periods of market stress, which may be especially relevant for gold as the traditional accepted safe haven.
    Keywords: Gold futures returns, Realized volatility, Realized skewness, Forecasting, Quantile boosting
    JEL: C22 C53 Q02
    Date: 2016–06
  4. By: Emna Trabelsi (ISG - Institut Supérieur de Gestion de Tunis [Tunis] - Université de Tunis [Tunis])
    Abstract: We are interested, in this paper, in studying the effects that central banks exert on private sector forecasts by means of their transparency and communication measures. We analyze the impact of central bank transparency on the accuracy of the consensus forecasts (usually calculated as the mean or the median of the forecasts from a panel of individual forecasters) for a series of macroeconomic variables: inflation, Real output growth and the current account as a share of GDP for 7 advanced economies. Interestingly, while it is found of significance of central bank transparency and communication measures on forecasts themselves, there appear some limits of the same measures when we study their impact on forecast errors. Our findings, indeed, suggest that deviations of the economic forecast data from the realized ones (RGDP and the current account as a share of GDP) are a bit affected by the central bank transparency measures considered in the paper. Inflation forecast errors, especially, are not affected at all by those measures. A possible explanation (among others) could be attributed to the inefficiency of the mean forecasts. Inefficiency of the consensus forecasts is not a new issue from a theoretical point of view, but its empirical relevance is for the first time (to our knowledge) questioned on data extracted from the Economist poll of forecasters. More particularly, our paper extracts practical implications over the effectiveness of transparent announcements in forecast formation process. We rely on two noisy information models, though having different mechanisms (Kim et al, 2001; Morris and Shin, 2002) both of which are consistent with overweighting issue to explain the inefficiency of the consensus forecast.
    Keywords: Economist poll of forecasters,Inefficiency,Consensus forecasts,Communication,Transparency
    Date: 2016
  5. By: Hakan Yilmazkuday (Department of Economics, Florida International University)
    Abstract: This paper introduces a simple methodology to forecast international trade. The main innovation is to calculate non-unitary expenditure elasticities of import demand implied by non-homothetic preferences in the previous year to be further combined with the current change in expenditure to forecast the current imports. Using U.S. data on aggregate expenditure and good-level imports, we test the performance of the methodology in forecasting international imports. The methodology is successful in forecasting not only the Great Trade Collapse and the corresponding recovery period but also the other periods in the sample.
    Keywords: Great Trade Collapse, Non-Homothetic Preferences, Forecasting
    JEL: F14 F17
    Date: 2016–06
  6. By: Ailie Charteris and Barry Strydom
    Abstract: There is considerable debate internationally as to whether share returns are predictable. The limited evidence in South Africa (Gupta and Modise, 2012a, b and 2013) reveals that valuation ratios have no forecasting power but the Treasury bill rate, term spread and money supply have been found to be able to predict share returns at a relatively short horizon. In this study, the consumption aggregate wealth ratio of Lettau and Ludvigson (2001) is applied to South African share returns to assess its forecasting power using in-sample tests over both short and long horizons. The forecasting power of this composite variable is compared to a number of traditional variables. Similarly to the developed market evidence, the results indicate that the consumption aggregate wealth ratio is a significant predictor of returns and combined with the term spread, can explain a substantial component of the variation in future share returns. The implications of these findings for practitioners and policy makers are discussed.
    Keywords: real returns, forecasting, cointegration, consumption aggregate wealth ratio
    JEL: G1 E21 C53
    Date: 2016
  7. By: OECD
    Abstract: The Airports Commission requires an external view on whether its forecasts yield plausible results, taking into account the ways in which the future of the aviation market may develop, prompted by comments received during stakeholder consultations on the forecasts and scenarios developed. This report summarises a review of the forecasts prepared by the International Transport Forum together with independent experts. The report provide views on the appropriateness of the outputs produced, on the most robust central scenarios and on any scenario results that should be treated with particular caution. it also examines one aspect of the methodology used in developing the baseline forecast, the module allocating traffic between London’s airports.
    Date: 2015–06–01
  8. By: Shelton Peiris (University of Sydney, Australia); Manabu Asai (Soka University, Japan); Michael McAleer (National Tsing Hua University, Taiwan; Erasmus University Rotterdam, the Netherlands; Complutense University of Madrid, Spain)
    Abstract: In recent years fractionally differenced processes have received a great deal of attention due to its flexibility in financial applications with long memory. This paper considers a class of models generated by Gegenbauer polynomials, incorporating the long memory in stochastic volatility (SV) components in order to develop the General Long Memory SV (GLMSV) model. We examine the statistical properties of the new model, suggest using the spectral likelihood estimation for long memory processes, and investigate the finite sample properties via Monte Carlo experiments. We apply the model to three exchange rate return series. Overall, the results of the out-of-sample forecasts show the adequacy of the new GLMSV model.
    Keywords: Stochastic volatility; GARCH models; Gegenbauer Polynomial; Long Memory; Spectral Likelihood; Estimation; Forecasting
    JEL: C18 C21 C58
    Date: 2016–06–06
  9. By: Alexander N. Bogin (Federal Housing Finance Agency); Stephen D. Bruestle (Penn State Erie); William M. Doerner (Federal Housing Finance Agency)
    Abstract: We develop a theoretically-based statistical technique to identify a conservative lower bound for house prices. Leveraging a model based upon consumer and investor incentives, we are able to explain the depth of housing market downturns at both the national and state level over a variety of market environments. This approach performs well in several historical back tests and has strong out-of-sample predictive ability. When back-tested, our estimation approach does not understate house price declines in any state over the 1987 to 2001 housing cycle and only understates declines in three states during the most recent financial crisis. This latter result is particularly noteworthy given that the post-2001 estimates are performed out-of-sample. Our measure of a conservative lower bound is attractive because it (1) provides a leading indicator of the severity of future downturns and (2) allows trough estimates to dynamically adjust as markets conditions change. This estimation technique could prove particularly helpful in measuring the credit risk associated with portfolios of mortgage assets as part of evaluating static stress tests or designing dynamic stress tests.
    Keywords: house prices, trough, lower bound, trend, financial stress testing
    JEL: G21 C58 R31
    Date: 2015–05
  10. By: Hannes Mueller (Institut d’Anà lisi Econòmica (IAE-CSIC), Barcelona GSE,); Christopher Rauh
    Abstract: This article provides a new methodology to predict conflict by using newspaper text. Through machine learning, vast quantities of newspaper text are reduced to interpretable topic shares. We use changes in topic shares to predict conflict one and two years before it occurs. In our predictions we distinguish between predicting the likelihood of conflict across countries and the timing of conflict within each country. Most factors identified by the literature, though performing well at predicting the location of conflict, add little to the prediction of timing. We show that news topics indeed can predict the timing of conflict onset. We also use the estimated topic shares to document how reporting changes before conflict breaks out.
    Keywords: Violence, Public Opinion
    JEL: D74 Z18 F51
    Date: 2016–06
  11. By: Monika Hadas-Dyduch (University of Economics in Katowice)
    Abstract: The aim of this article is the prediction of GDP Polish and other selected European countries. For this purpose integrated into one algorithm econometric methods and wavelet analysis. Econometric methods and wavelet transform are combined goal of constructing a copyright model for predicting macroeconomic indicators. In the article, for estimating the macroeconomic indicators on the example of GDP proposed authorial algorithm that combines the following methods: a method trend creep method of alignment exponential and analysis multiresolution. Used econometric methods, this is a trend crawling and alignment exponential have been modified in several major stages. The aim of the merger of these methods is the construction of algorithm to predict short-term time series. In the copyright algorithm was applied wavelet continuous compactly supported. wavelet used Daubechies. The Daubechies wavelets, are a family of orthogonal wavelets and characterized by a maximal number of vanishing moments for some given support. With each wavelet type of this class, there is a scaling function which generates an orthogonal multiresolution analysis.
    Keywords: prediction, wavelets, wavelet transform
    JEL: F37 C13 G15
    Date: 2016–06

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