IDEAS home Printed from https://ideas.repec.org/p/cpb/discus/41.html
   My bibliography  Save this paper

Refinement of the partial adjustment model using continuous-time econometrics

Author

Listed:
  • Arie ten Cate

Abstract

This paper presents some suggestions for the specification of dynamic models. These suggestions are based on the supposed continuous-time nature of most economic processes. In particular, the partial adjustment model -or Koyck lag model- is discussed. The refinement of this model is derived from the continuous-time econometric literature. We find three alternative formulas for this refinement, depending on the particular econometric literature which is used. Two of these formulas agree with an intuitive example. In passing, it is shown that that the continuous-time models of Sims and Bergstrom are closely related. Also the inverse of Bergstrom's approximate analog has been introduced, making use of engineering mathematics. Followed by Error-correction modelling in discrete and continuous time, Economics Letters 101 (2008), pp.140-141 [with Philip Hans Franses]

Suggested Citation

  • Arie ten Cate, 2004. "Refinement of the partial adjustment model using continuous-time econometrics," CPB Discussion Paper 41, CPB Netherlands Bureau for Economic Policy Analysis.
  • Handle: RePEc:cpb:discus:41
    as

    Download full text from publisher

    File URL: https://www.cpb.nl/sites/default/files/publicaties/download/refinement-partial-adjustment-model-using-continuous-time-econometrics.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. McCrorie, J. Roderick & Chambers, Marcus J., 2006. "Granger causality and the sampling of economic processes," Journal of Econometrics, Elsevier, vol. 132(2), pages 311-336, June.
    2. Arie ten Cate, 2002. "Continuous-time modelling in econometrics and engineering - juli 2002," CPB Memorandum 42, CPB Netherlands Bureau for Economic Policy Analysis.
    3. Bergstrom, A. R., 1988. "The History of Continuous-Time Econometric Models," Econometric Theory, Cambridge University Press, vol. 4(3), pages 365-383, December.
    4. Teles, Paulo & Wei, William W. S., 2000. "The effects of temporal aggregation on tests of linearity of a time series," Computational Statistics & Data Analysis, Elsevier, vol. 34(1), pages 91-103, July.
    5. ten Cate, Arie, 1993. "The current period coefficient of polynomial lag distributions," Economic Modelling, Elsevier, vol. 10(4), pages 408-416, October.
    6. Sims, Christopher A, 1971. "Discrete Approximations to Continuous Time Distributed Lags in Econometrics," Econometrica, Econometric Society, vol. 39(3), pages 545-563, May.
    7. Chambers, Marcus J., 1999. "Discrete time representation of stationary and non-stationary continuous time systems," Journal of Economic Dynamics and Control, Elsevier, vol. 23(4), pages 619-639, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. David E. Bloom & David Canning & Günther Fink, 2014. "Disease and Development Revisited," Journal of Political Economy, University of Chicago Press, vol. 122(6), pages 1355-1366.
    2. ten Cate, Arie & Franses, Philip Hans, 2008. "Error-correction modelling in discrete and continuous time," Economics Letters, Elsevier, vol. 101(2), pages 140-141, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mamingi Nlandu, 2017. "Beauty and Ugliness of Aggregation over Time: A Survey," Review of Economics, De Gruyter, vol. 68(3), pages 205-227, December.
    2. Peter C. B. Phillips & Jun Yu, 2005. "Comments on “A selective overview of nonparametric methods in financial econometricsâ€Â," Finance Working Papers 22469, East Asian Bureau of Economic Research.
    3. McCrorie, J. Roderick & Chambers, Marcus J., 2006. "Granger causality and the sampling of economic processes," Journal of Econometrics, Elsevier, vol. 132(2), pages 311-336, June.
    4. Chambers, MJ & McCrorie, JR & Thornton, MA, 2017. "Continuous Time Modelling Based on an Exact Discrete Time Representation," Economics Discussion Papers 20497, University of Essex, Department of Economics.
    5. Chih‐Nan Chen & Tsutomu Watanabe & Tomoyoshi Yabu, 2012. "A New Method for Identifying the Effects of Foreign Exchange Interventions," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(8), pages 1507-1533, December.
    6. Michael A. Thornton & Marcus J. Chambers, 2013. "Temporal aggregation in macroeconomics," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 13, pages 289-310, Edward Elgar Publishing.
    7. Peter C. B. Phillips & Jun Yu, 2005. "Comments on “A Selective Overview of Nonparametric Methods in Financial Econometrics” by Jianqing Fan," Working Papers 08-2005, Singapore Management University, School of Economics.
    8. Alexandre Petkovic & David Veredas, 2009. "Aggregation of linear models for panel data," Working Papers ECARES 2009-012, ULB -- Universite Libre de Bruxelles.
    9. Lawrence J. Christiano, 1980. "The term structure of interest rates and the aliasing identification problem," Working Papers 165, Federal Reserve Bank of Minneapolis.
    10. Thornton, Michael A. & Chambers, Marcus J., 2017. "Continuous time ARMA processes: Discrete time representation and likelihood evaluation," Journal of Economic Dynamics and Control, Elsevier, vol. 79(C), pages 48-65.
    11. Michael A. Thornton & Marcus J. Chambers, 2013. "Continuous-time autoregressive moving average processes in discrete time: representation and embeddability," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(5), pages 552-561, September.
    12. Das, Sanjiv R., 2002. "The surprise element: jumps in interest rates," Journal of Econometrics, Elsevier, vol. 106(1), pages 27-65, January.
    13. Piergiorgio Alessandri & Andrea Gazzani & Alejandro Vicondoa, 2021. "The real effects of financial uncertainty shocks: A daily identification approach," Working Papers 61, Red Nacional de Investigadores en Economía (RedNIE).
    14. Jean-Marie Dufour, 2003. "Identification, weak instruments, and statistical inference in econometrics," Canadian Journal of Economics, Canadian Economics Association, vol. 36(4), pages 767-808, November.
    15. Alvaro Escribano & J. Ignacio Peña & Pablo Villaplana, 2011. "Modelling Electricity Prices: International Evidence," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 73(5), pages 622-650, October.
    16. Mercenier, Jean & Michel, Philippe, 2001. "Temporal aggregation in a multi-sector economy with endogenous growth," Journal of Economic Dynamics and Control, Elsevier, vol. 25(8), pages 1179-1191, August.
    17. Cimadomo, Jacopo & Giannone, Domenico & Lenza, Michele & Monti, Francesca & Sokol, Andrej, 2022. "Nowcasting with large Bayesian vector autoregressions," Journal of Econometrics, Elsevier, vol. 231(2), pages 500-519.
    18. Aadland, David, 2005. "Detrending time-aggregated data," Economics Letters, Elsevier, vol. 89(3), pages 287-293, December.
    19. Hermann Singer, 2011. "Continuous-discrete state-space modeling of panel data with nonlinear filter algorithms," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(4), pages 375-413, December.
    20. Palm, Franz C & Nijman, Theo E, 1984. "Missing Observations in the Dynamic Regression Model," Econometrica, Econometric Society, vol. 52(6), pages 1415-1435, November.

    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cpb:discus:41. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/cpbgvnl.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.