This paper describes a low variance simulator of the normal distribution function. The probability integral is evaluated exactly at an initial point specified with a factor analytic covariance structure, so that the integral can be derived using dimension reduction techniques. The line integral between the initial point and the desired point is evaluated using Plackett's identity. A Monte Carlo simulation of this line integral simulator (LIS) with the Geweke-Hajivassiliou-Keane (GHK) simulator demonstrates that for dimensions of ten or less, the LIS outperformed the GHK simulator, typically by an order of magnitude and in some cases by two orders of magnitude.

The authors extend Daniel McFadden's (1989) method of simulated moments to approximating efficient estimators in general estimation problems. The general approach applies a simulated bias correction to an approximation of the efficient score. They discuss a general trade-off in estimator inefficiency between bias correction and moment approximation.

Thomas J. Prusa, James A. Schmitz, Jr., Can Companies Maintain their Initial Innovative Thrust? A Study of the PC Software Industry, The Review of Economics and Statistics, Vol. 76, No. 3 (Aug., 1994), pp. 523-540

John A. Bishop, John P. Formby, Paul D. Thistle, Convergence and Divergence of Regional Income Distributions and Welfare, The Review of Economics and Statistics, Vol. 76, No. 2 (May, 1994), pp. 228-235

This study employs a stochastic model developed by G. Udny Yule and Herbert A. Simon as the probability mechanism underlying the consumer's choice of artistic products and predicts that artistic outputs will be concentrated among a few lucky individuals. We find that the probability distribution implied by the stochastic model provides an excellent description of the empirical data in the popular music industry, suggesting that the stochastic model may represent the process generating the superstar phenomenon. Because the stochastic model does not require differential talents among individuals, our empirical results support the notion that the superstar phenomenon could exist among individuals with equal talent.

This proposed theoretical model is based on new models of Barro and Becker and Becker Murphy and Tamura and explains the interaction of family decisions about fertility and the macroeconomy in a growth situation. The proposed model captures a dynamic interaction between labor/leisure and fertility choice and a structural fertility preference shock. Endogenous factor are consumption labor/leisure and fertility while exogenous factors are production and utility parameters. The aim was to develop a general equilibrium model which expresses short- and long-term dynamics to test the impact of economic disturbances on fertility and to explain the US baby boom and subsequent fertility patterns. Savings in capital accumulation and in labor supply were expected to have ambiguous effects while improved productivity was expected to increase steady state consumption. The methodology a structural Vector Auto Regression (VAR) model was developed by Blanchard and Quah and Ihmed Ickes Wang and Yoo. Structural impacts include disturbances in employment fertility (theoretical preference shift) and output. Long-term restrictions are based on theory rather than on ad hoc causal orderings (Sims method) or current responses (Bernanke method). The structural VAR model is estimated using the logged differences of labor fertility rate and output. The empirical results are based on analysis of US data (1949-88) on fertility weekly hours worked and real gross national product. The model revealed that fertility choice should not be considered exogenous to the labor market or to economic growth. Variance of the forecast error for the fertility rate was significantly explained by employment shocks; the effect was reduced fertility and increased labor force effort. Output responses to fertility and technology shocks were similar to those reported by Shapiro and Watson. In the variance decomposition analysis output shocks explained about 33% of output variance. Fertility shocks explained about 33% of labor growth and 25% of output growth after the first year. With a lag of one year about 37% of fertility variance was explained by employment shocks. Concluding remarks underscore the importance of knowing which shock initiated the motion and causal ordering.

Julian M. Alston, Kenneth A. Foster, Richard D. Gree, Estimating Elasticities with the Linear Approximate Almost Ideal Demand System: Some Monte Carlo Results, The Review of Economics and Statistics, Vol. 76, No. 2 (May, 1994), pp. 351-356