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Lewbel and Pendakur (2009) developed the idea of implicit Marshallian demands. Implicit Marshallian demand systems allow the incorporation of both unobserved preference heterogeneity and complex Engel curves into consumer demand analysis, circumventing the standard problems associated with combining rationality with either unobserved heterogeneity or high rank in demand (or both). They also developed the exact affine Stone index (EASI) implicit Marshallian demand system wherein much of the demand system is linearised and thus relatively easy to implement and estimate. This chapter offers a less technical introduction to implicit Marshallian demands in general and to the EASI demand system in particular. I show how to implement the EASI demand system, paying special attention to tricks that allow the investigator to further simplify the problem without sacrificing too much in terms of model flexibility. STATA code to implement the simplified models is included throughout the text and in an appendix.

This chapter presents the indirect preferences for all full rank Gorman and Lewbel demand systems. Each member in this class of demand models is a generalized quadratic expenditure system (GQES). This representation allows applied researchers to choose a small number of price indices and a function of income to specify any exactly aggregable demand system, without the need to revisit the questions of integrability of the demand equations or the implied form and structure of indirect preferences. This characterization also allows for the calculation of exact welfare measures for consumers, either in the aggregate or for specific classes of individuals, and other valuations of interest to applied researchers.

This chapter is an up-to-date survey of the state-of-the art in consumer demand analysis. We review (and evaluate) advances in a number of related areas, in the spirit of the recent survey paper by Barnett and Serletis (2008). In doing so, we only deal with consumer choice in a static framework, ignoring a number of important issues, such as, the effects of demographic or other variables that affect demand, welfare comparisons across households (equivalence scales), and the many issues concerning aggregation across consumers.

To fully accommodate the correlations between semiconductor product demands and external information such as the end market trends or regional economy growth, a linear dynamic system is introduced in this chapter to improve forecasting performance in supply chain operations. In conjunction with the generic Gaussian noise assumptions, the proposed state-space model leads to an expectation-maximization (EM) algorithm to estimate model parameters and predict production demands. Since the set of external indicators is of high dimensionality, principal component analysis (PCA) is applied to reduce the model order and corresponding computational complexity without loss of substantial statistical information. Experimental study on certain real electronic products demonstrates that this forecasting methodology produces more accurate predictions than other conventional approaches, which thereby helps improve the production planning and the quality of semiconductor supply chain management.