| dc.contributor.author |
ZANIAS, GP |
en |
| dc.date.accessioned |
2014-06-06T06:42:36Z |
|
| dc.date.available |
2014-06-06T06:42:36Z |
|
| dc.date.issued |
1994 |
en |
| dc.identifier.issn |
0277-6693 |
en |
| dc.identifier.uri |
http://62.217.125.90/xmlui/handle/123456789/713 |
|
| dc.subject |
ADVERTISING-SALES |
en |
| dc.subject |
COINTEGRATION |
en |
| dc.subject |
CAUSALITY |
en |
| dc.subject |
FORECASTING |
en |
| dc.subject.classification |
Management |
en |
| dc.subject.classification |
Planning & Development |
en |
| dc.subject.other |
AUTOREGRESSIVE TIME-SERIES |
en |
| dc.subject.other |
UNIT-ROOT |
en |
| dc.subject.other |
ECONOMETRICS |
en |
| dc.subject.other |
ESTIMATORS |
en |
| dc.subject.other |
VECTORS |
en |
| dc.subject.other |
TESTS |
en |
| dc.title |
THE LONG-RUN, CAUSALITY, AND FORECASTING IN THE ADVERTISING SALES RELATIONSHIP |
en |
| heal.type |
journalArticle |
en |
| heal.language |
English |
en |
| heal.publicationDate |
1994 |
en |
| heal.abstract |
Co-integration analysis is used in a study of the advertising and sales relationship using the Lydia Pinkham data set. The series are shown to have a valid long-run relationship while Granger-causality runs in both directions. The latter is found by using a causality test involving the co-integrations restrictions which seem to constitute a crucial part of such tests in the case of co-integrated variables. A comparison with previous models shows that forecasting co-integrated series is more accurate with error-correction systems, especially in the case of multi-step forecasting. |
en |
| heal.publisher |
JOHN WILEY & SONS LTD |
en |
| heal.journalName |
JOURNAL OF FORECASTING |
en |
| dc.identifier.issue |
7 |
en |
| dc.identifier.volume |
13 |
en |
| dc.identifier.isi |
ISI:A1994QF66600003 |
en |
| dc.identifier.spage |
601 |
en |
| dc.identifier.epage |
610 |
en |