Is the Permanent Income Hypothesis Really Well-Suited for Forecasting?
This paper first tests the restrictions implied by Hall’s (1978) version of the permanent income hypothesis (PIH) obtained from a bivariate system of labor income and savings, using quarterly data over the period of 1947:01 to 2008:03 for the US economy, and then uses the model to forecast changes in labor income over the period of 1991:01 to 2008:03. First, our results indicate the overwhelming rejection of the restrictions on the data implied by the PIH. Second, we found that, when compared to univariate and bivariate versions of classical and Bayesian Vector Autoregressive (VAR) models, the PIH model, in general, is outperformed by all other models in terms of the average RMSEs for one- to eight-quarters-ahead forecasts for the changes in labor income. Finally, as far as forecasting is concerned, we found the most tight Gibbs sampled univarite Bayesian VAR to perform the best. In sum, we do not find evidence for the US data to be consistent with the PIH, neither does the PIH model perform better relative to alternative atheoretical models in forecasting changes in labor income over an out of sample horizon that was characterized by high degree of volatility for the variable of interest.
To our knowledge, this item is not available for
download. To find whether it is available, there are three
1. Check below under "Related research" 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.
|Date of creation:||Mar 2009|
|Contact details of provider:|| Postal: PRETORIA, 0002|
Phone: (+2712) 420 2413
Fax: (+2712) 362-5207
Web page: http://www.up.ac.za/economics
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:pre:wpaper:200909. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Rangan Gupta)
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 references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.