original: IO and spatial information as Bayesian priors in an employment forecasting model
Interindustry input-output (IO) relationships were incorporated into a local labor market forecasting model for the Toledo, OH MSA by Magura (1990); he found that the use of the IO information as a Bayesian prior reduced forecast errors. LeSage and Magura (1991) found similar results using national labor market data. This paper likewise uses IO information as a Bayesian prior in forecasting employment in four industries in five states but also adds spatial information. The purpose of adding the spatial information is to determine if it further reduces forecast errors. Using a mixture of a spatial weight matrix similar to that proposed by LeSage (1993) in addition to the IO information, it is found that forecast errors are reduced beyond that achieved with only the IO information.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 32 (1998)
Issue (Month): 4 ()
|Note:||Received: October 1996 / Accepted in revised form: August 1997|
|Contact details of provider:|| Web page: http://www.springer.com|
|Order Information:||Web: http://link.springer.com/journal/168|