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[A utility tree is specified yielding a complete set of demand equations that subsumes the linear expenditure system as a special case. In contrast to the Stone-Geary system, it is shown that our S-branch model allows for Hicks-Allen complements, and it does not restrict the own-price elasticity. Moreover, it is not costly in terms of the additional parameters required. Maximum-likelihood estimates of the S-branch system are presented; these are derived by means of the Bard version of the Gauss-Newton algorithm. In this application to food expenditure data in the United States the use of the S-branch system avoids a potential misspecification which would have resulted from the application of the linear expenditure system.]

Numerous studies have confirmed the importance of habit formation, as represented by a lagged dependent variable, in demand analysis. Although all work to date has been based on aggregate time series data, this study uses household level BLS Interview Panel data to test the habit hypothesis. An interrelated demand system for seventeen goods is estimated from cross-section data and compared with a similar system based on time series data. The results show that the habit component is significantly different between the two data sets and much smaller in the cross-section data. Habit effects, while not as large in cross-section data as time series, are still highly significant. Several explanations are offered concerning why the two sets of estimates differ.

A neoclassical model of investment behavior is developed wherein firms are assumed to maximize the expected present value of net revenue under Leontief technology. Prices and yields are assumed to be stochastic. This framework yields investment as a function of the expected present value of the output from one unit of capital and the variance of the expected value. This theory is applied to investment in the almond industry. The model outperforms both a model without uncertainty and one with neither uncertainty nor adjustment costs using in-sample and out-of-sample tests.