IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-319-52827-4_5.html
   My bibliography  Save this book chapter

Econometric Analysis

In: Statistical Tools for Program Evaluation

Author

Listed:
  • Jean-Michel Josselin

    (University of Rennes 1)

  • Benoît Le Maux

    (University of Rennes 1)

Abstract

Econometrics encompasses several multivariate tools for testing a theory or hypothesis, quantifying it and providing indications about the evolution of an outcome of interest. This chapter gives the basic knowledge on how those tools work, with examples and the corresponding R-CRAN codes. The first step is dedicated to simple and multiple linear regression models and their estimation by the method of ordinary least squares (Sects. 5.1 and 5.2). The classical assumptions (e.g., linearity, normality of residuals, homoscedasticity, non-autocorrelation) underlying the method are exposed (Sect. 5.3). An important step in conducting an econometric analysis is model specification as it determines the validity of the regression analysis (Sect. 5.4). Another issue is the choice of the functional form that best fits the data, i.e. whether the variables are expressed or not in a non-linear form (Sect. 5.5). Several tests also exist to detect potential misspecifications (Jarque-Bera, Breusch-Pagan, and Durbin-Watson tests) which are fully detailed (Sect. 5.6). Last, the selection of the final model relies on a meticulous examination of regression outputs, e.g., whether the variables sufficiently explain the outcome of interest (Sect. 5.7). Methods are then extended to the case where the latter is binary (Sect. 5.8), the so-called logit and probit models.

Suggested Citation

  • Jean-Michel Josselin & Benoît Le Maux, 2017. "Econometric Analysis," Springer Books, in: Statistical Tools for Program Evaluation, chapter 5, pages 137-187, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-52827-4_5
    DOI: 10.1007/978-3-319-52827-4_5
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:sprchp:978-3-319-52827-4_5. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.