I would like to thank Janet Wagner for her generous comments and suggestions. I also would like to thank Carole Makela and two anonymous referees for helpful comments and Pam Norum for providing me with her data set. This paper is dedicated to Professor Rachel Dardis. This research was supported by a grant from HUEC/DRIF and the computing facilities of the Computer Science Center at the University of Maryland at College Park.
An Alternative Model of U.S. Clothing Expenditures: Application of Cointegration Techniques
Article first published online: 4 MAR 2005
Journal of Consumer Affairs
Volume 26, Issue 2, pages 305–323, Winter 1992
How to Cite
MOKHTARI, M. (1992), An Alternative Model of U.S. Clothing Expenditures: Application of Cointegration Techniques. Journal of Consumer Affairs, 26: 305–323. doi: 10.1111/j.1745-6606.1992.tb00030.x
- Issue published online: 4 MAR 2005
- Article first published online: 4 MAR 2005
In this paper, an Error Correction Mechanism model of U.S. clothing expenditures for the period 1929–1987 is estimated using recent developments in modeling nonstationary variables. Using clothing expenditures as an example, the pitfalls of conventional modeling of nonstationary variables and the advantages of a new modeling procedure that takes into account the properties of data for valid inference about population parameters are pointed out. The basic findings obtained by estimating an Error Correction Mechanism model of clothing expenditures are (1) the demand for clothing is income inelastic both in the short run and in the long run; (2) the price elasticity of demand is unitary in the long run but greater than unity in the short run; (3) an increase in the unemployment rate reduces U.S. clothing expenditures both in the short run and the long run; and (4) an increase in the number of elderly (above the age of 65) increases clothing expenditures in the short run and reduces expenditures in the long run. However, the shortrun impact of an increase in the elderly population on clothing expenditures is statistically insignificant.