The Rouwenhorst method of approximating stationary AR(1) processes has been overlooked by much of the literature despite having many desirable properties unmatched by other methods. In particular, we prove that it can match the conditional and unconditional mean and variance, and the first-order autocorrelation of any stationary AR(1) process. These properties makes the Rouwenhorst method more reliable than others in approximating highly persistent processes and generating accurate model solutions. To illustrate this, we compare the performances of the Rouwenhorst method and four others in solving the stochastic growth model and an income fluctuation problem. We find that (i) the choice of approximation method can have a large impact on the computed model solutions, and (ii) the Rouwenhorst method is more robust than others with respect to variation in the persistence of the process, the number of points used in the discrete approximation and the procedure used to generate model statistics.
We consider a life-cycle model with idiosyncratic risk in labor earnings, out-of-pocket medical and nursing home expenses, and survival. Partial insurance is available through welfare, Medicaid, and social security. Calibrating the model to the U.S., we find that 12 percent of aggregate savings is accumulated to finance and self-insure against old-age health expenses given the absence of complete public health care for the elderly, and that nursing home expenses play an important role in the savings of the wealthy and on aggregate. Moreover, we find that the aggregate and distributional effects of public health care provision are highly dependent on the availability of other programs comprising the social insurance system.
The welfare gain to consumers from the introduction of personal computers is estimated here. A simple model of consumer demand is formulated that uses a slightly modified version of standard preferences. The modification permits marginal utility, and hence total utility, to be finite when the consumption of computers is zero. This implies that the good won’t be consumed at a high enough price. It also bounds the consumer surplus derived from the product. The model is calibrated/estimated using standard national income and product account data. The welfare gain from the introduction of personal computers is in the range of 2 to 14 percent of consumption expenditure.
Macro models are developed to explore the impact of technological progress on the household. Chapter 1 focuses on the impact of technological progress in transportation on suburbanization. In Chapter 2, a model with leisure production and endogenous retirement is used to explain the declining labor-force participation rates of elderly males. Finally, in Chapter 3, the welfare gain from technological progress in personal computer production is measured by constructing a simple model of computer demand.
A model with leisure production and endogenous retirement is used to explain the declining labor force participation rates of elderly males. The model is calibrated to cross-sectional data on the labor force participation rates of elderly US males by age, their median drop in market consumption and leisure good expenditure share in the year 2000. Running the calibrated model for the period 1850 to 2000, a prediction of the evolution of the cross-section is obtained. The model is able to predict more than 87 percent of the increase in retirement of men over 65. The increase in retirement is driven by rising real wages and a falling price of leisure goods over time.
Suburbanization in the U.S. between 1910 and 1970 was concurrent with the rapid diffusion of the automobile. A circular city model is developed in order to access quantitatively the contribution of automobiles and rising incomes to suburbanization. The model incorporates a number of driving forces of suburbanization and car adoption, including falling automobile prices, rising real incomes, changing costs of traveling by car and with public transportation, and urban population growth. According to the model, 60 percent of postwar (1940-1970) suburbanization can be explained by these factors. Rising real incomes and falling automobile prices are shown to be the key drivers of suburbanization.
