IDEAS home Printed from https://ideas.repec.org/p/asu/wpaper/2133301.html

How Fast Can the New Economy Grow? A Bayesian Analysis of the Evolution of Trend Growth

Author

Abstract

This paper uses consumption data to estimate the trend growth rate for the “new economy.'' The analysis starts with the assumption that a trend break in GDP should be accompanied by a trend break in consumption. But because consumption is forward looking and smoother than GDP, it should be easier to detect a trend break in the former. The forward looking nature of consumption allows us to incorporate the private expectations of U.S. households about the new economy. The relative smoothness makes it easier to separate changes in trend growth from ordinary cyclical movements. The evidence confirms that there has been an increase in trend growth over the last 5 years, but the increase seems rather modest. The new economy is likely to grow more rapidly than in the 1970s, but not as fast as in the 1950s or early 1960s.

Suggested Citation

  • Timothy Cogley, "undated". "How Fast Can the New Economy Grow? A Bayesian Analysis of the Evolution of Trend Growth," Working Papers 2133301, Department of Economics, W. P. Carey School of Business, Arizona State University.
  • Handle: RePEc:asu:wpaper:2133301
    as

    Download full text from publisher

    File URL: http://wpcarey.asu.edu/tools/mytools/pubs_admin/FILES/d1.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    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:asu:wpaper:2133301. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Steve Salik The email address of this maintainer does not seem to be valid anymore. Please ask Steve Salik to update the entry or send us the correct address (email available below). General contact details of provider: https://edirc.repec.org/data/deasuus.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.