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Random-Time Aggregation in Partial Adjustment Models

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  • Jorda, Oscar

Abstract

How is econometric analysis (of partial adjustment models) affected by the fact that, although data collection is done at regular, fixed intervals of time, economic decisions are made at random intervals of time? This article addresses this question by modeling the economic decision-making process as a general point process. Under random-time aggregation, (1) inference on the speed of adjustment is biased-adjustments are a function of the intensity of the point process and the proportion of adjustment; 2) inference on the correlation with exogenous variables is generally downward biased; and (3) a nonconstant intensity of the point process gives rise to a general class of regime-dependent time series models. An empirical application to test the production-smoothing-buffer-stock model of inventory behavior illustrates, in practice, the effects of random-time aggregation.

Suggested Citation

  • Jorda, Oscar, 1999. "Random-Time Aggregation in Partial Adjustment Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(3), pages 382-395, July.
    • Handle: RePEc:bes:jnlbes:v:17:y:1999:i:3:p:382-95
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    Cited by:

    1. Massimiliano Marcellino & Oscar Jorda, "undated". "Stochastic Processes Subject to Time-Scale Transformations: An Application to High-Frequency FX Data," Working Papers 164, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    2. Massimiliano Marcellino & Oscar Jorda, "undated". "Stochastic Processes Subject to Time-Scale Transformations: An Application to High-Frequency FX Data," Working Papers 164, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    3. Ricardo J. Caballero & Eduardo M.R.A. Engel, 2003. "Missing Aggregate Dynamics: On the Slow Convergence of Lumpy Adjustment Models," Cowles Foundation Discussion Papers 1430, Cowles Foundation for Research in Economics, Yale University, revised Apr 2008.
    4. Òscar Jordà & Massimiliano Marcellino, 2004. "Time‐scale transformations of discrete time processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(6), pages 873-894, November.
    5. Claudia Foroni & Massimiliano Marcellino, 2013. "Mixed frequency structural models: estimation, and policy analysis," Working Paper 2013/15, Norges Bank.
    6. Lin, Winston T. & Chen, Yueh H. & Hung, TingShu, 2019. "A partial adjustment valuation approach with stochastic and dynamic speeds of partial adjustment to measuring and evaluating the business value of information technology," European Journal of Operational Research, Elsevier, vol. 272(2), pages 766-779.
    7. Oscar Jordà & Massimiliano Marcellino, 2004. "Time-scale transformations of discrete time processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(6), pages 873-894, November.
    8. Rafal Raciborski, 2008. "Searching for additional sources of inflation persistence : the micro-price panel data approach," Working Paper Research 132, National Bank of Belgium.
    9. Artem Prokhorov & Peter Radchenko & Alexander Semenov & Anton Skrobotov, 2024. "Change-Point Detection in Time Series Using Mixed Integer Programming," Papers 2408.05665, arXiv.org.
    10. Ramey, Garey & Shigeru Fujita, 2006. "The Cyclicality of Job Loss and Hiring," University of California at San Diego, Economics Working Paper Series qt4nz8p839, Department of Economics, UC San Diego.
    11. Lin, Winston T. & Kao, Ta-Wei (Daniel), 2014. "The partial adjustment valuation approach with dynamic and variable speeds of adjustment to evaluating and measuring the business value of information technology," European Journal of Operational Research, Elsevier, vol. 238(1), pages 208-220.

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