Access

You are not currently logged in.

Access your personal account or get JSTOR access through your library or other institution:

login

Log in to your personal account or through your institution.

If You Use a Screen Reader

This content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.

Large Sample Properties of Posterior Densities, Bayesian Information Criterion and the Likelihood Principle in Nonstationary Time Series Models

Jae-Young Kim
Econometrica
Vol. 66, No. 2 (Mar., 1998), pp. 359-380
Published by: The Econometric Society
DOI: 10.2307/2998562
Stable URL: http://www.jstor.org/stable/2998562
Page Count: 22
  • Read Online (Free)
  • Download ($10.00)
  • Subscribe ($19.50)
  • Cite this Item
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Large Sample Properties of Posterior Densities, Bayesian Information Criterion and the Likelihood Principle in Nonstationary Time Series Models
Preview not available

Abstract

Asymptotic normality of the posterior is a well understood result for dynamic as well as nondynamic models based on sets of abstract conditions whose actual applicability is hardly known especially for the case of nonstationarity. In this paper we provide a set of conditions by which we can relatively easily prove the asymptotic posterior normality under quite general situations of possible nonstationarity. This result reinforces and generalizes the point of Sims and Uhlig (1991) that inference based on the likelihood principle, explained by Berger and Wolpert (1988), will be unchanged regardless of whether the data are generated by a stationary process or by a unit root process. On the other hand, our conditions allow us to generalize the Bayesian information criterion known as the Schwarz criterion to the case of possible nonstationarity. In addition, we have shown that consistency of the maximum likelihood estimator, not the asymptotic normality of the estimator, with some minor additional assumptions is sufficient for asymptotic posterior normality.

Page Thumbnails

  • Thumbnail: Page 
359
    359
  • Thumbnail: Page 
360
    360
  • Thumbnail: Page 
361
    361
  • Thumbnail: Page 
362
    362
  • Thumbnail: Page 
363
    363
  • Thumbnail: Page 
364
    364
  • Thumbnail: Page 
365
    365
  • Thumbnail: Page 
366
    366
  • Thumbnail: Page 
367
    367
  • Thumbnail: Page 
368
    368
  • Thumbnail: Page 
369
    369
  • Thumbnail: Page 
370
    370
  • Thumbnail: Page 
371
    371
  • Thumbnail: Page 
372
    372
  • Thumbnail: Page 
373
    373
  • Thumbnail: Page 
374
    374
  • Thumbnail: Page 
375
    375
  • Thumbnail: Page 
376
    376
  • Thumbnail: Page 
377
    377
  • Thumbnail: Page 
378
    378
  • Thumbnail: Page 
379
    379
  • Thumbnail: Page 
380
    380