[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/h/spr/adspcp/978-3-662-05617-2_13.html
   My bibliography  Save this book chapter

Prediction in the Panel Data Model with Spatial Correlation

In: Advances in Spatial Econometrics

Author

Listed:
  • Badi H. Baltagi

    (Texas A&M University)

  • Dong Li

    (Kansas State University)

Abstract
The econometrics of spatial models have focused mainly on estimation and testing of hypotheses, see Anselin (1988b), Anselin et al. (1996) and Anselin and Bera (1998) to mention a few. In this chapter we focus on prediction in spatial models based on panel data. In particular, we consider a simple demand equation for cigarettes based on a panel of 46 states over the period 1963–1992. The spatial autocorrelation due to neighboring states and the individual heterogeneity across states is taken explicitly into account. In order to explain how spatial autocorrelation may arise in the demand for cigarettes, we note that cigarette prices vary among states, primarily due to variation in state taxes on cigarettes. For example, in 1988, state excise taxes ranged from 2 cents per pack in a producing state like North Carolina, to 38 cents per pack in the state of Minnesota. In 1997, these state taxes varied from a low of 2.5 cents per pack for Virginia to $1.00 per pack in Alaska and Hawaii. Since cigarettes can be stored and are easy to transport, these varying taxes result in casual smuggling across neighboring states. For example, while New Hampshire had a 12 cents per pack tax on cigarettes in 1988, neighboring Massachusetts and Maine had a 26 and 28 cents per pack tax. Border effect purchases not explained in the demand equation can cause spatial autocorrelation among the disturbances.1

Suggested Citation

  • Badi H. Baltagi & Dong Li, 2004. "Prediction in the Panel Data Model with Spatial Correlation," Advances in Spatial Science, in: Luc Anselin & Raymond J. G. M. Florax & Sergio J. Rey (ed.), Advances in Spatial Econometrics, chapter 13, pages 283-295, Springer.
    • Handle: RePEc:spr:adspcp:978-3-662-05617-2_13
    DOI: 10.1007/978-3-662-05617-2_13
    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:adspcp:978-3-662-05617-2_13. 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.