nep-ets New Economics Papers
on Econometric Time Series
Issue of 2011‒03‒19
three papers chosen by
Yong Yin
SUNY at Buffalo

  1. The estimation uncertainty of permanent-transitory decompositions in cointegrated systems By Sven Schreiber
  2. Forecasting the Intermittent Demand for Slow-Moving Items By Ralph D. Snyder; J. Keith Ord; Adrian Beaumont
  3. Joint Detection of Structural Change and Nonstationarity in Autoregressions By Pitarakis, J

  1. By: Sven Schreiber (Macroeconomic Policy Institute (IMK) in the Hans Boeckler Foundation)
    Abstract: The topic of this paper is the estimation uncertainty of the Stock-Watsonand Gonzalo-Granger permanent-transitory decompositions in the frameworkof the cointegrated vector-autoregression. Specifically, we suggest an approach to construct the confidence interval of the transitory component in agiven period (e.g. the latest observation) by conditioning on the observed datain that period. To calculate asymptotically valid confidence intervals we usethe delta method and two bootstrap variants. As an illustration we analyze theuncertainty of (US) output gap estimates in a system of output, consumption, and investment.
    Keywords: transitory components, VECM, delta method, bootstrap
    JEL: C32 C15 E32
    Date: 2011
  2. By: Ralph D. Snyder (Department of Econometrics and Business Statistics, Monash University); J. Keith Ord (McDonough School of Business, Georgetown University); Adrian Beaumont (Department of Econometrics and Business Statistics, Monash University)
    Abstract: Organizations with large-scale inventory systems typically have a large proportion of items for which demand is intermittent and low volume. We examine different approaches to forecasting for such products, paying particular attention to the need for inventory planning over a multi-period lead-time when the underlying process may be nonstationary. This emphasis leads to consideration of prediction distributions for processes with time-dependent parameters. A wide range of possible distributions could be considered but we focus upon the Poisson (as a widely used benchmark), the negative binomial (as a popular extension of the Poisson) and a hurdle shifted Poisson (which retains Croston’s notion of a Bernoulli process for times between orders). We also develop performance measures related to the entire predictive distribution, rather than focusing exclusively upon point predictions. The three models are compared using data on the monthly demand for 1,046 automobile parts, provided by a US automobile manufacturer. We conclude that inventory planning should be based upon dynamic models using distributions that are more flexible than the traditional Poisson scheme.
    Keywords: Croston's method; Exponential smoothing; Hurdle shifted Poisson distribution; Intermittent demand; Inventory control; Prediction likelihood; State space models
    JEL: C25 C53 M21
    Date: 2010–05
  3. By: Pitarakis, J
    Abstract: In this paper we develop a test of the joint null hypothesis of parameter stability and a unit root within an ADF style autoregressive specification whose entire parameter structure is potentially subject to a structural break at an unknown time period. The maintained underlying null model is a linear autoregression with a unit root, stationary regressors and a constant term. As a byproduct we also obtain the limiting behaviour of a related Wald statistic designed to solely test the null of parameter stability in an environment with a unit root. These distributions are free of nuisance parameters and easily tabulated. The finite sample properties of our tests are subsequently assessed through a series of simulations.
    Keywords: Structural Breaks; Unit Roots; Nonlinear Dynamics
    JEL: C10 C22
    Date: 2011–02

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