nep-for New Economics Papers
on Forecasting
Issue of 2018‒07‒16
ten papers chosen by
Rob J Hyndman
Monash University

  1. The contribution of jumps to forecasting the density of returns By Christophe Chorro; Florian Ielpo; Benoît Sévi
  2. Short term forecasts of economic activity: are fortnightly factors useful? By Libero Monteforte; Valentina Raponi
  3. Analyzing Business Conditions by Quantitative Text Analysis–Time Series Analysis Using Appearance Rate and Principal Component By Nariyasu YAMAZAWA
  4. Combining Survey Long-Run Forecasts and Nowcasts with BVAR Forecasts Using Relative Entropy By Tallman, Ellis W.; Zaman, Saeed
  5. On the Evolution of the United Kingdom Price Distributions By Ba M. Chu; Kim Huynh; David T. Jacho-Chávez; Oleksiy Kryvtsov
  6. Stochastic volatility models with ARMA innovations: An application to G7 inflation forecasts By Bo Zhang; Joshua C.C. Chan; Jamie L. Cross
  7. Forecasting French GDP with Dynamic Factor Models : a pseudo-real time experiment using Factor-augmented Error Correction Models By Stéphanie Combes; Catherine Doz
  8. Exogenous Drivers of Cryptocurrency Volatility - A Mixed Data Sampling Approach To Forecasting By Thomas Walther; Tony Klein; ;
  9. A Nowcasting Model for the Growth Rate of Real GDP of Ecuador : Implementing a Time-Varying Intercept By Manuel Gonzalez-Astudillo; Daniel Baquero
  10. Forecasting Imports with Information from Abroad By Christian Grimme; Robert Lehmann; Marvin Noeller

  1. By: Christophe Chorro (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique); Florian Ielpo (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, Unigestion SA - UNIGESTION , IPAG Business School); Benoît Sévi (LEMNA - Laboratoire d'économie et de management de Nantes Atlantique - UN - Université de Nantes)
    Abstract: The extraction of the jump component in dynamics of asset prices haw witnessed a considerably growing body of literature. Of particular interest is the decomposition of returns' quadratic variation between their continuous and jump components. Recent contributions highlight the importance of this component in forecasting volatility at different horizons. In this article, we extend a methodology developed in Maheu and McCurdy (2011) to exploit the information content of intraday data in forecasting the density of returns at horizons up to sixty days. We follow Boudt et al. (2011) to detect intraday returns that should be considered as jumps. The methodology is robust to intra-week periodicity and further delivers estimates of signed jumps in contrast to the rest of the literature where only the squared jump component can be estimated. Then, we estimate a bivariate model of returns and volatilities where the jump component is independently modeled using a jump distribution that fits the stylized facts of the estimated jumps. Our empirical results for S&P 500 futures, U.S. 10-year Treasury futures, USD/CAD exchange rate and WTI crude oil futures highlight the importance of considering the continuous/jump decomposition for density forecasting while this is not the case for volatility point forecast. In particular, we show that the model considering jumps apart from the continuous component consistenly deliver better density forecasts for forecasting horizons ranging from 1 to 30 days.
    Keywords: leverage effect,density forecasting,jumps,realized volatility,bipower variation,median realized volatility
    Date: 2017–01
  2. By: Libero Monteforte (Bank of Italy); Valentina Raponi (Imperial College London and Sapienza University of Rome)
    Abstract: A short term mixed-frequency model is proposed to estimate and forecast the Italian economic activity fortnightly. Building on Frale et al. (2011), we introduce a dynamic factor model with three frequencies (quarterly, monthly and fortnightly), by selecting indicators that show significant coincident and leading properties and are representative of both demand and supply. We find that high-frequency indicators improve the real time forecasts of Italian GDP. Moreover, the model provides a new fortnightly indicator of GDP, consistent with the official quarterly series. Our results emphasize the potential benefit of the high frequency series, providing forecasting gains beyond those based on monthly variables alone.
    Keywords: factor models, Kalman filter, temporal disaggregation, mixed frequency data, forecasting
    JEL: C53 E17 E32 E37
    Date: 2018–06
  3. By: Nariyasu YAMAZAWA
    Abstract: We present a procedure for analyzing the current business conditions and forecasting GDP growth rate by quantitative text analysis. We use text data of Economy Watcher Survey conducted by Cabinet Office. We extract words from 190 thousands sentence, and construct time series data by counting appearance rate every month. The analyses consist of four parts: (1) visualizing appearance rate by drawing graphs, (2) correlation analysis, (3) principal component analysis, and (4) forecasting GDP growth rate. First, we draw graphs of the appearance rate of words which are influenced by business conditions. We find that the graphs show the effect of policy on business conditions clearly. Second, we construct word lists which correlate business conditions by computing correlation coefficients. And we also construct lists which reversely correlate business conditions. Third, we extract principal component from 150 frequent words. We find that the 1st principal component move together with business conditions. The last, we forecast quarterly real GDP growth rate by text data. We find that forecast accuracy improved by adding the text data. It shows that text data have useful information about GDP forecasting.
    Date: 2018–03
  4. By: Tallman, Ellis W. (Federal Reserve Bank of Cleveland); Zaman, Saeed (Federal Reserve Bank of Cleveland)
    Abstract: This paper constructs hybrid forecasts that combine both short- and long-term conditioning information from external surveys with forecasts from a standard fixed-coefficient vector autoregression (VAR) model. Specifically, we use relative entropy to tilt one-step ahead and long-horizon VAR forecasts to match the nowcast and long-horizon forecast from the Survey of Professional Forecasters. The results indicate meaningful gains in multi-horizon forecast accuracy relative to model forecasts that do not incorporate long-term survey conditions. The accuracy gains are achieved for a range of variables, including those that are not directly tilted but are affected through spillover effects from tilted variables. The forecast accuracy gains for inflation are substantial, statistically significant, and are competitive with the forecast accuracy from both time-varying VARs and univariate benchmarks. We view our proposal as an indirect approach to accommodating structural change and moving end points.
    Keywords: Bayesian analysis; relative entropy; survey forecasts; nowcasts; density forecasts; real-time data;
    JEL: C11 C32 C53 E17
    Date: 2018–06–22
  5. By: Ba M. Chu; Kim Huynh; David T. Jacho-Chávez; Oleksiy Kryvtsov
    Abstract: We propose a functional principal components method that accounts for stratified random sample weighting and time dependence in the observations to understand the evolution of distributions of monthly micro-level consumer prices for the United Kingdom (UK). We apply the method to publicly available monthly data on individual-good prices collected in retail stores by the UK Office for National Statistics for the construction of the UK Consumer Price Index from March 1996 to September 2015. In addition, we conduct Monte Carlo simulations to demonstrate the effectiveness of our methodology. Our method allows us to visualize the dynamics of the price distribution and uncovers interesting patterns during the sample period. Further, we demonstrate the efficacy of our methodology with an out-of-sample forecasting algorithm that exploits the time dependence of distributions. Our out-of-sample forecast compares favorably with the random walk forecast.
    Keywords: Econometric and statistical methods, Inflation and prices
    JEL: C14 C83 E31 E37
    Date: 2018
  6. By: Bo Zhang; Joshua C.C. Chan; Jamie L. Cross
    Abstract: We introduce a new class of stochastic volatility models with autoregressive moving average (ARMA) innovations. The conditional mean process has a flexible form that can accommodate both a state space representation and a conventional dynamic regression. The ARMA component introduces serial dependence which renders standard Kalman filter techniques not directly applicable. To overcome this hurdle we develop an efficient posterior simulator that builds on recently developed precision based algorithms. We assess the usefulness of these new models in an inflation forecasting exercise across all G7 economies. We find that the new models generally provide competitive point and density forecasts compared to standard benchmarks, and are especially useful for Canada, France, Italy and the US.
    Keywords: autoregressive moving average errors, stochastic volatility, inflation forecast, state space models, unobserved components model
    JEL: C11 C52 C53 E37
    Date: 2018–06
  7. By: Stéphanie Combes (INSEE Paris - INSEE Paris); Catherine Doz (PJSE - Paris Jourdan Sciences Economiques - UP1 - Université Panthéon-Sorbonne - ENS Paris - École normale supérieure - Paris - INRA - Institut National de la Recherche Agronomique - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics)
    Abstract: Dynamic Factor Models (DFMs) allow to take advantage of the information provided by a large dataset, which is summarized by a small set of unobservable latent variables, and they have proved to be very useful for short-term forecasting. Since most of their properties rely on the stationarity of the series, these models have been mainly used on data which have been di_erenciated to achieve stationarity. However estimation procedures for DFMs with I(1) common factors have been proposed by Bai (2004) and Bai and Ng(2004). Further, Banerjee and Marcellino (2008) and Banerjee, Marcellino and Masten (2014) have proposed to extend stationary Factor Augmented VAR models to the non-stationary case, and introduced Factor augmented Error Correction Models (FECM). We rely on this approach and conduct a pseudoreal time forecasting experiment, in which we compare short term forecasts of French GDP based on stationary and non-stationary DFMs. We mimic the timeliness of data, and use in the non-stationary framework the 2-step estimator proposed by Doz, Giannone and Reichlin(2011). In our study, forecasts based on stationary or non-stationary DFMs have a similar precision.
    Date: 2018–06
  8. By: Thomas Walther; Tony Klein; ;
    Abstract: We apply the GARCH-MIDAS framework to forecast the daily, weekly, and monthly volatility of four highly capitalized Cryptocurrencies (Bitcoin, Etherium, Litecoin, and Ripple) as well as the Cryptocurrency index CRIX. Based on the prediction quality, we determine the most important exogenous drivers of volatility in Cryptocurrency markets. We ?nd that the Global Real Economic Activity outperforms all other economic and ?nancial drivers under investigation. Only the average forecast combination results in lower loss functions. This indicates that the information content of exogenous factors is time-varying and the model averaging approach diversi?es the impact of single drivers.
    Keywords: Bitcoin, Cryptocurrencies, GARCH, Mixed Data Sampling, Volatility
    JEL: C10 C58 G11
    Date: 2018–06
  9. By: Manuel Gonzalez-Astudillo; Daniel Baquero
    Abstract: This paper proposes a model to nowcast the annual growth rate of real GDP for Ecuador. The specification combines monthly information of 28 macroeconomic variables with quarterly information of real GDP in a mixed-frequency approach. Additionally, our setup includes a time-varying mean coefficient on the annual growth rate of real GDP to allow the model to incorporate prolonged periods of low growth, such as those experienced during secular stagnation episodes. The model produces reasonably good nowcasts of real GDP growth in pseudo out-of-sample exercises and is marginally more precise than a simple ARMA model.
    Keywords: Ecuador ; Secular stagnation ; Nowcasting model ; Time-varying coefficients
    JEL: C33 C53 E37
    Date: 2018–07–05
  10. By: Christian Grimme; Robert Lehmann; Marvin Noeller
    Abstract: Globalization has led to huge increases in import volumes, but the literature on import forecasting is still in its infancy. We introduce the first leading indicator especially constructed for total import growth, the so-called Import Climate. It builds on the idea that the import demand of the domestic country should be reflected in the expected export developments of its main trading partners. A foreign country’s expected exports are, in turn, determined by business and consumer confidence in the countries it trades with and its price competitiveness. In a pseudo out-of-sample, real-time forecasting experiment, the Import Climate outperforms standard business cycle indicators at short horizons for France, Germany, Italy, and the United States for the first release of import data. For Spain and the United Kingdom, our leading indicator works particularly well with the latest vintage of import data.
    Keywords: import climate, import forecasting, survey data, price competitiveness
    JEL: F01 F10 F17
    Date: 2018

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