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Forecasting the duration of short‐term deflation episodes

Forecasting the duration of short‐term deflation episodes The paper proposes a simulation‐based approach to multistep probabilistic forecasting, applied for predicting the probability and duration of negative inflation. The essence of this approach is in counting runs simulated from a multivariate distribution representing the probabilistic forecasts, which enters the negative inflation regime. The marginal distributions of forecasts are estimated using the series of past forecast errors, and the joint distribution is obtained by a multivariate copula approach. This technique is applied for estimating the probability of negative inflation in China and its expected duration, with the marginal distributions computed by fitting weighted skew‐normal and two‐piece normal distributions to autoregressive moving average ex post forecast errors and using the multivariate Student t copula. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Forecasting Wiley

Forecasting the duration of short‐term deflation episodes

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Publisher
Wiley
Copyright
Copyright © 2018 John Wiley & Sons, Ltd.
ISSN
0277-6693
eISSN
1099-131X
DOI
10.1002/for.2514
Publisher site
See Article on Publisher Site

Abstract

The paper proposes a simulation‐based approach to multistep probabilistic forecasting, applied for predicting the probability and duration of negative inflation. The essence of this approach is in counting runs simulated from a multivariate distribution representing the probabilistic forecasts, which enters the negative inflation regime. The marginal distributions of forecasts are estimated using the series of past forecast errors, and the joint distribution is obtained by a multivariate copula approach. This technique is applied for estimating the probability of negative inflation in China and its expected duration, with the marginal distributions computed by fitting weighted skew‐normal and two‐piece normal distributions to autoregressive moving average ex post forecast errors and using the multivariate Student t copula.

Journal

Journal of Forecasting – Wiley

Published: Jan 1, 2018

Keywords: ; ; ; ; ;

References

  • Deflation and depression: Is there an empirical link?
    Atkeson, A.; Kehoe, P. J.
  • Why does higher inflation raise inflation uncertainty?
    Ball, L.
  • Moderate inflation and the deflation: Depression link
    Benhabib, J.; Spigel, M. M.
  • Unemployment duration, benefit duration, and the business cycle
    Bover, O.; Arellano, M.; Bentolila, S.
  • GLS‐based unit root tests with multiple structural breaks both under the null and the alternative hypotheses
    Carrion‐i‐Silvestre, J. L.; Kim, D.; Perron, P.
  • Making the most of high inflation
    Charemza, W.; Makarova, S.; Shah, I.
  • Forecasting distributions of inflation rates: The functional auto‐regressive approach
    Chaudhuri, K.; Kim, M.; Shin, Y.
  • Variance estimation for sample quantiles using the m out of n bootstrap
    Cheung, K. Y.; Lee, S. M. S.
  • Forecast uncertainty—ex ante and ex post: US inflation and output growth
    Clements, M. P.
  • Combining forecasts based on multiple encompassing tests in a macroeconomic core system
    Constantini, M.; Kunst, R. M.
  • The t copula and related copulas
    Demarta, S.; McNeil, A. J.
  • Non‐uniform random variate generation
    Devroye, L.
  • Comparing predictive accuracy
    Diebold, F. X.; Mariano, R. S.
  • The method of simulated quantiles
    Dominicy, Y.; Veredas, D.
  • Handbook of heavy tailed distributions in finance
    Embrechts, P.; Lindskog, F.; McNeil, A.
  • Deflation risk
    Fleckenstein, M.; Longstaff, F. A.; Lustig, H.
  • The test of Robinson (1994) for fractional integration: Time domain versus frequency domain
    Gil‐Alana, L. A.
  • An application of extreme value theory for measuring financial risk
    Gilli, M.; Këllezi, E.
  • Exact convergence rate of bootstrap quantile variance estimator
    Halt, P.; Martin, M. A.
  • Testing the equality of prediction mean squared errors
    Harvey, D.; Leybourne, S.; Newbold, P.
  • On the predictive content of autoregression residuals: A semiparameteric copula‐based approach to time series prediction
    Herwartz, H.
  • The three parameter two‐piece normal family of distributions and its fitting
    John, S.
  • Methods for the two‐piece normal distribution
    Kimber, A. C.
  • Extreme value distributions: Theory and applications
    Kotz, S.; Nadarajah, S.
  • Dependence Modeling: Vine copula handbook
  • Econometric models for the duration of unemployment
    Lancaster, T.
  • Bayesian analysis of asymmetric stochastic conditional duration model
    Men, Z.; Kolankiewicz, A. W.; Wirjanto, T. S.
  • Lag length selection and the construction of unit root tests with good size and power
    Ng, S.; Perron, P.
  • Efficient tests of nonstationary hypotheses
    Robinson, P. M.
  • Making room for China in the world economy
    Rodrik, D.
  • Fonctions de repartition a n dimensions et leurs marges
    Sklar, A.
  • Copula modelling of dependence in multivariate time series
    Smith, M. S.
  • An assessment of Bank of England and National Institute inflation forecast uncertainties
    Wallis, K. F.