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Random-Time Aggregation In Partial Ajustment Models

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  • Oscar Jorda

Abstract

How is econometric analysis (of partial adjustment models) affected by the fact that, while data collection is done at regular, fixed intervals of time, economic decisions are made at random intervals of time? This paper addresses this question by modelling 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 non-constant 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.

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Paper provided by California Davis - Department of Economics in its series Department of Economics with number 97-32.

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Handle: RePEc:fth:caldec:97-32

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Postal: University of California Davis - Department of Economics. One Shields Ave., California 95616-8578
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Cited by:
  1. Oscar Jorda & Massimiliano Marcellino, 2003. "Stochastic Processes Subject To Time Scale Transformations: An Application To High-Frequency Fx Data," Working Papers 02, University of California, Davis, Department of Economics.
  2. Oscar Jorda & Massimiliano Marcellino, 2003. "Time-Scale Transformations of Discrete-Time Processes," Working Papers 32, University of California, Davis, Department of Economics.
  3. 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.
  4. 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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