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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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  • Oscar Jorda, "undated". "Random-Time Aggregation In Partial Ajustment Models," Department of Economics 97-32, California Davis - Department of Economics.
  • Handle: RePEc:fth:caldec:97-32
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    File URL: http://www.econ.ucdavis.edu/working_papers/97-32.pdf
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    3. Sappington, David E. M. & Weisman, Dennis L., 1996. "Potential pitfalls in empirical investigations of the effects of incentive regulation plans in the telecommunications industry," Information Economics and Policy, Elsevier, vol. 8(2), pages 125-140, June.
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    5. Cabral, Luis M B & Riordan, Michael H, 1989. "Incentives for Cost Reduction under Price Cap Regulation," Journal of Regulatory Economics, Springer, vol. 1(2), pages 93-102, June.
    6. Quandt, Richard E., 1983. "Computational problems and methods," Handbook of Econometrics,in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 1, chapter 12, pages 699-764 Elsevier.
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    8. Barten, Anton P., 1997. "Annual report 1996," European Economic Review, Elsevier, vol. 41(3-5), pages 971-973, April.
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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. 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.
    3. Claudia Foroni & Massimiliano Marcellino, 2013. "Mixed frequency structural models: estimation, and policy analysis," Working Paper 2013/15, Norges Bank.
    4. David Berger & Ricardo J. Caballero & Eduardo Engel, 2003. "Missing Aggregate Dynamics: On the Slow Convergence of Lumpy Adjustment Models," NBER Working Papers 9898, National Bureau of Economic Research, Inc.
    5. Oscar JordĂ  & Massimiliano Marcellino, 2004. "Time-scale transformations of discrete time processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(6), pages 873-894, November.
    6. Rafal Raciborski, 2008. "Searching for additional sources of inflation persistence : the micro-price panel data approach," Working Paper Research 132, National Bank of Belgium.
    7. 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.
    8. 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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