The main objective of this study was to evaluate the effects of the changes in total nondurables expenditures, prices, and demographics on the U.S. aggregate demand for clothing categories and shoes. In particular, this study focused on identifying and parameterizing the effects of such changes. To this purpose, a demand system for two clothing categories, shoes, and other nondurable commodities for the U.S. was estimated using aggregate time-series data sets (1929-1994), and a second-stage budgeting model was developed and estimated. The basis for the demand model was the Almost Ideal Demand System model, which was modified to account for the demographic effects. Demographic variables included in the final model were age distribution of the U.S. population (median age and variance), proportion of non-White population in the total U.S. population, and labor force participation rate of U.S. women. The main data sources were documents published by the Bureau of the Census, Bureau of Labor Statistics, and Bureau of Economic Analysis in the U.S. Department of Commerce. The results indicate that the total nondurables expenditures is a significant variable in determining consumers' nondurables expenditure allocation on clothing categories and shoes. The estimated total expenditure elasticities suggest that the clothing categories and shoes are expenditure elastic, ranging from 1.1019 to 1.4944. Most own and cross prices appear to be significant variables in determining the consumer budget allocations for clothing categories and shoes. The median age and non-White population variables evidence as significant variables that affect the U.S. aggregate nondurables expenditure allocation on men’s and boys’ clothing and on shoes. Women’s labor force participation rate evidences as a significant variable that affects the U.S. aggregate nondurables expenditure allocation on women’s and children’s clothing. The estimated own-price elasticities of demand for clothing categories and shoes indicate that all the clothing categories and shoes are inelastic (i.e., -0.3908 to -0.9175). The estimated crossprice elasticities of clothing categories, shoes and other nondurable goods show substitution and complementary relationships between the categories. The demand elasticities with respect to the demographic variables were also estimated. / Ph. D.

This report presents a top-down methodology to forecast annual international flight operations at sixty-six U.S. airports, whose combined operations accounted for 99.8% of the total international passenger flight operations in National Airspace System (NAS) in 2004. The forecast of international flight operations at each airport is derived from the combination of passenger flight operations at the airport to ten World Regions. The regions include: Europe, Asia, Africa, South America, Mexico, Canada, Caribbean and Central America, Middle East, Oceania and U.S. International. In the forecast, a "top-down" methodology is applied in three steps. In the fist step, individual linear regression models are developed to forecast the total annual international passenger enplanements from the U.S. to each of nine World Regions. The resulting regression models are statistically valid and have parameters that are credible in terms of signs and magnitude. In the second step, the forecasted passenger enplanements are distributed among international airports in the U.S. using individual airport market share factors. The airport market share analysis conducted in this step concludes that the airline business is the critical factor explaining the changes associated with airport market share. In the third and final step, the international passenger enplanements at each airport are converted to flight operations required for transporting the passengers. In this process, average load factor and average seats per aircraft are used. The model has been integrated into the Transportation Systems Analysis Model (TSAM), a comprehensive intercity transportation planning tool. Through a simple graphic user interface implemented in the TSAM model, the user can test different future scenarios by defining a series of scaling factors for GDP, load factor and average seats per aircraft. The default values for the latter two variables are predefined in the model using 2004 historical data derived from Department of Transportation T100 international segment data. / Master of Science

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<p> In this thesis we study three problems of joint pricing and inventory in a retail setting.</p> <p> The first problem deals with pricing and ordering for a retailer facing uncertain supply as well as price-sensitive uncertain demand. We first formulate the problem as two cases of pricing: a simultaneous pricing strategy where the price and the order quantity are simultaneously determined and a postponed pricing strategy where the price and the order quantity are sequentially determined. We provide a solution procedure to find the optimal price and order quantity that maximizes the retailer's profit. By conducting sensitivity analysis, we find that if the supplier is very unreliable, then the retailer is better off postponing the pricing decision in order to maximize profit. Reducing supply variability does not have the same impact on retailer's profit as much as increasing the expected supply amount. Most importantly we find that the difference between the expected profits in the two cases is not due to higher expected revenue, but due to lower expected salvage and shortage losses when the pricing decision is postponed.</p> <p> Next, we study a price setting retailer selling two substitutable goods to consumers. The retailer must decide on the optimal price and inventory that maximize the expected profit. Aside from making these decisions under demand uncertainty, the retailer must also account for the substitution that occurs upon stock out of one of the two products. Furthermore, we also take into account the related cannibalization of the available stock due to customers substituting. We formulate the problem and find the optimal prices analytically as well as conduct sensitivity analysis. We compare our findings to a model that does not consider substitution and the resultant cannibalization of inventory and find that the model that does not consider substitution tends to overestimate the expected profit for low degrees of substitution and tends to underestimate the expected profit for high degrees of substitution. Furthermore, the prices charged and the inventory held at the retailer for each product, tend to be suboptimal. The total quantity stocked in general, for both products, is lower when we account for substitution and cannibalization.</p> <p> Lastly, we study the problem of finding optimal order quantities and prices for the bundle (a collection of two or more goods sold jointly at one price) and individual items as well as how a supplier can use bundles to achieve coordination with its retailer. In a decentralized supply chain, we show that bundling is not always a feasible or a very profitable strategy. This is especially true if the products or the bundle are discounted beyond a certain point, because it may make the supplier worse off while making the retailer better off. This reduces the effectiveness of the bundling strategy in a supply chain setting. We find that the supplier, retailer and the supply chain can simultaneously improve their profits by offering bundled goods to the consumers and achieve performance of a coordinated supply chain when the supplier charges the retailer a bundling fee upfront and in exchange offering a bundling discount to the retailer.</p> <p> In the last chapter, we summarize our findings as well as provide direction for future research.</p> / Thesis / Doctor of Philosophy (PhD)

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