nep-for New Economics Papers
on Forecasting
Issue of 2011‒01‒16
nine papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting damped trend exponential smoothing: an algebraic viewpoint. By Giacomo Sbrana
  2. Using Large Data Sets to Forecast Sectoral Employment By Rangan Gupta; Alain Kabundi; Stephen M. Miller; Josine Uwilingiye
  3. Should Macroeconomic Forecasters Use Daily Financial Data and How? By Elena Andreou; Eric Ghysels; Andros Kourtellos
  4. Corporate bond spreads and real activity in the euro area - Least Angle Regression forecasting and the probability of the recession By Marco Buchmann
  5. Microstructure order flow: statistical and economic evaluation of nonlinear forecasts By Mario Cerrato; Hyunsok Kim; Ronald MacDonald
  6. Combining predictive densities using Bayesian filtering with applications to US economics data By Monica Billio; Roberto Casarin; Francesco Ravazzolo; Herman K. van Dijk
  7. Time Varying Dimension Models By Joshua C.C. Chan; Gary Koop; Roberto Leon-Gonzalez; Rodney W. Strachan
  8. Forecasting migrant remittances during the global financial crisis By Mohapatra, Sanket; Ratha, Dilip
  9. Euro area labour markets: different reaction to shocks? By Jan Brůha; Beatrice Pierluigi; Roberta Serafini

  1. By: Giacomo Sbrana (BETA/CNRS, Université de Strasbourg, France.)
    Date: 2010
  2. By: Rangan Gupta (Department of Economics, University of Pretoria); Alain Kabundi (Department of Economics and Econometrics, University of Johannesburg); Stephen M. Miller (College of Business, University of Las Vegas, Nevada); Josine Uwilingiye (Department of Economics and Econometrics, University of Johannesburg)
    Abstract: We implement several Bayesian and classical models to forecast employment for eight sectors of the US economy. In addition to standard vector-autoregressive and Bayesian vector autoregressive models, we also include the information content of 143 additional monthly series in some models. Several approaches exist for incorporating information from a large number of series. We consider two approaches – extracting common factors (principle components) in a factor-augmented vector autoregressive or vector error-correction, Bayesian factor-augmented vector autoregressive or vector error-correction models, or Bayesian shrinkage in a large-scale Bayesian vector autoregressive models. Using the period of January 1972 to December 1999 as the in-sample period and January 2000 to March 2009 as the out-of-sample horizon, we compare the forecast performance of the alternative models. Finally, we forecast out-of sample from April 2009 through March 2010, using the best forecasting model for each employment series. We find that factor augmented models, especially error-correction versions, generally prove the best in out-of-sample forecast performance, implying that in addition to macroeconomic variables, incorporating long-run relationships along with short-run dynamics play an important role in forecasting employment.
    Keywords: Sectoral Employment, Forecasting, Factor Augmented Models, Large-Scale BVAR models
    JEL: C32 R31
    Date: 2011–01
  3. By: Elena Andreou (Department of Economics, University of Cyprus, Nicosia, Cyprus); Eric Ghysels (Department of Economics, University of North Carolina, Chapel Hill, NC, USA; Department of Finance, Kenan-Flagler Business School, University of North Carolina, Chapel Hill, NC, USA); Andros Kourtellos (Department of Economics, University of Cyprus, Nicosia, Cyprus; The Rimini Centre for Economic Analysis (RCEA), Rimini, Italy)
    Abstract: We introduce easy to implement regression-based methods for predicting quarterly real economic activity that use daily financial data and rely on forecast combinations of MIDAS regressions. Our analysis is designed to elucidate the value of daily information and provide real-time forecast updates of the current (nowcasting) and future quarters. Our findings show that while on average the predictive ability of all models worsens substantially following the financial crisis, the models we propose suffer relatively less losses than the traditional ones. Moreover, these predictive gains are primarily driven by the classes of government securities, equities, and especially corporate risk.
    Keywords: MIDAS; macro forecasting, leads; daily financial information; daily factors
    JEL: C22 C53 G10
    Date: 2010–01
  4. By: Marco Buchmann (European Central Bank, DG Financial Stability, Financial Stability Assessment Division, Kaiserstrasse 29, D-60311 Frankfurt am Main, Germany.)
    Abstract: This paper aims at providing a detailed analysis of the leading indicator properties of corporate bond spreads for real economic activity in the euro area. In- and out-of-sample predictive content of corporate bond spreads are examined along three dimensions: the bonds’ quality, their term to maturity, as well as the forecast horizon at which one intends to predict a change in real activity. Numerous alternative leading indicators capturing macroeconomic and financial conditions are included in the analysis. Along with standard time series forecast models, the Least Angle Regression (LAR) technique is used to build multivariate models recursively. Models built via LAR can be used to produce forecasts and allow one to analyze how the composition and the number of relevant model variables evolve over time. Corporate bond spreads turn out to be valuable predictors for real activity, in particular at forecast horizons beyond one year; Medium risk bond spreads with maturities between 5 and 10 years appear particularly rich in content. The spreads also belong to the group of indicators that implied the highest probability of a recession occurring from a pre-crisis perspective. JEL Classification: E32, E37, E44, G32.
    Keywords: Corporate bond spreads, point and density forecasting, automatic model building, least angle regression.
    Date: 2011–01
  5. By: Mario Cerrato; Hyunsok Kim; Ronald MacDonald
    Abstract: In this paper we propose a novel empirical extension of the standard market microstructure order flow model. The main idea is that heterogeneity of beliefs in the foreign exchange market can cause model instability and such instability has not been fully accounted for in the existing empirical literature. We investigate this issue using two different data sets and focusing on out- of-sample forecasts. Forecasting power is measured using standard statistical tests and, additionally, using an alternative approach based on measuring the economic value of forecasts after building a portfolio of assets. We …nd there is a substantial economic value on conditioning on the proposed models.
    Keywords: microstructure, order flow, forecasting
    JEL: F31 F41 G10
    Date: 2010–12
  6. By: Monica Billio (University of Venice, GRETA Assoc. and School for Advanced Studies in Venice); Roberto Casarin (University of Breccia and GRETA Assoc); Francesco Ravazzolo (Norges Bank (Central Bank of Norway)); Herman K. van Dijk (Econometrics and Tinbergen Institutes, Erasmus University Rotterdam)
    Abstract: Using a Bayesian framework this paper provides a multivariate combination approach to prediction based on a distributional state space representation of predictive densities from alternative models. In the proposed approach the model set can be incomplete. Several multivariate time-varying combination strategies are introduced. In particular, a weight dynamics driven by the past performance of the predictive densities is considered and the use of learning mechanisms. The approach is assessed using statistical and utility-based performance measures forevaluating density forecasts of US macroeconomic time series and of surveys of stock market prices.
    Keywords: Density Forecast Combination, Survey Forecast, Bayesian Filtering, Sequential Monte Carlo
    JEL: C11 C15 C53 E37
    Date: 2010–12–21
  7. By: Joshua C.C. Chan (Australian National University); Gary Koop (University of Strathclyde; The Rimini Centre for Economic Analysis (RCEA)); Roberto Leon-Gonzalez (National Graduate Institute for Policy Studies; The Rimini Centre for Economic Analysis (RCEA)); Rodney W. Strachan (Australian National University; The Rimini Centre for Economic Analysis (RCEA))
    Abstract: Time varying parameter (TVP) models have enjoyed an increasing popularity in empirical macroeconomics. However, TVP models are parameter-rich and risk over-fi?tting unless the dimension of the model is small. Motivated by this worry, this paper proposes several Time Varying dimension (TVD) models where the dimension of the model can change over time, allowing for the model to automatically choose a more parsimonious TVP representation, or to switch between different parsimonious representations. Our TVD models all fall in the category of dynamic mixture models. We discuss the properties of these models and present methods for Bayesian inference. An application involving US inflation forecasting illustrates and compares the different TVD models. We ?find our TVD approaches exhibit better forecasting performance than several standard benchmarks and shrink towards parsimonious speci?cations.
    Date: 2010–01
  8. By: Mohapatra, Sanket; Ratha, Dilip
    Abstract: The financial crisis has highlighted the need for forecasts of remittance flows in many developing countries where these flows have proved to be a lifeline to the poor people and the economy. This note describes a simple methodology for forecasting country-level remittance flows in a manner consistent with the medium-term outlook for the global economy. Remittances are assumed to depend on bilateral migration stocks and income levels in the host country and the origin country. Changes in remittance costs, shifts in remittance channels, global exchange rate movements, and unpredictable immigration controls in the migrant-destination countries pose risks to the forecasts. Much remains to be done to improve the forecast methodology, data on bilateral flows, and high-frequency monitoring of migration and remittance flows.
    Keywords: Remittances,Debt Markets,Currencies and Exchange Rates,Agriculture&Farming Systems,Emerging Markets
    Date: 2010–12–01
  9. By: Jan Brůha (Czech National Bank, Na P?íkop? 28, 115 03 Praha 1, Czech Republic.); Beatrice Pierluigi (European Central Bank, Kaiserstrasse 29, D-60311 Frankfurt am Main, Germany.); Roberta Serafini (European Central Bank, Kaiserstrasse 29, D-60311 Frankfurt am Main, Germany.)
    Abstract: A small labour market model for the six largest euro area countries (Germany, France, Italy, Spain, the Netherlands, Belgium) is estimated in a state-space framework. The model entails, in the long run, four driving forces: a trend labour force component, a trend labour productivity component, a long-run inflation rate and a trend hours worked component. The short run dynamics is governed by a VAR model including six shocks. The state-space framework is convenient for the decomposition of endogenous variables in trends and cycles, for shock decomposition, for incorporating external judgement, and for running conditional projections. The forecast performance of the model is rather satisfactory. The model is used to carry out a policy experiment with the objective of investigating whether euro area countries differ in the labour market adjustment to a reduction in labour costs. Results suggest that, following the 2008-09 recession, moderate wage growth would significantly help delivering a more job-intense recovery. JEL Classification: C51, C53, E17, J21.
    Keywords: Labor market, Forecasting, Kalman filter.
    Date: 2011–01

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