Volume 2 Issue 3

In various surveys, presence of measurement errors has led to misleading results in estimation of various population parameters. This study indicates the effects of measurement errors on estimates of population total and population variance when samples are drawn using systematic sampling technique from a stratified population. A finite population was generated through simulation. The population was then stratified into four strata followed by generation of ten samples in each of them using systematic sampling technique. In each stratum a sample was picked at random. The findings of this work indicated that systematic errors affected the accuracy of the estimates by overestimating both the population total and the population variance. Random errors only added variability to the data but their effect on the estimates of the population total and population variance was not that profound.

When income growth under price dispersion reduces the time of search and raises prices of purchases, the increase in purchase price can be presented as the increase in the willingness to pay for insurance or the willingness to pay for consumer credit. The optimal consumer decision represents the trade-off between the propensity to search for beneficial insurance or consumer credit, and marginal savings on insurance policy or consumer credit. Under price dispersion the indirect utility function takes the form of cubic parabola, where the risk aversion behavior ends at the saddle point of the comprehensive insurance or the complete consumer credit. The comparative static analysis of the saddle point of the utility function discovers the ambiguity of the departure from risk-neutrality. This ambiguity can produce the ordinary risk seeking behavior as well as mathematical catastrophes of Veblen-effect's imprudence and over prudence of family altruism. The comeback to risk aversion is also ambiguous and it results either in increasing or in decreasing relative risk aversion. The paper argues that the decreasing relative risk aversion comes to the optimum quantity of money.

Since Lėon Walras neoclassical economists hold an inalterable belief in a unique and stable equilibrium for the economic system which however remains to this day unobservable. Yet that belief is the corner stone of other theories such as the ‘Effi-cient Market Hypothesis' as well as the philosophy of neo-liberalism, whose out-comes are also shown to be flawed by recent events. A modern market economy is obviously an input/output nonlinear controllable construct. However, this paper examines four such models of increasing complexity, including the affine nonlinear feedback H∞-control, to show that the ‘data requirement' precludes all attempts at the empirical verification of the existence of a stable equilibrium. If equilibria of complex nonlinear deterministic systems are most likely unstable, multiple or deterministically chaotic depending on their parameter values and uncertainties, then society should impose limits on the state space and focus on endurable patterns thrown-off by such systems.

This paper investigates the effect of the nonzero autocorrelation coefficients on the sampling distributions of the Durbin-Watson test estimator in three time-series models that have different variance-covariance matrix assumption, separately. We show that the expected values and variances of the Durbin-Watson test estimator are slightly different, but the skewed and kurtosis coefficients are considerably different among three models. The shapes of four coefficients are similar between the Durbin-Watson model and our benchmark model, but are not the same with the autoregressive model cut by one-lagged period. Second, the large sample case shows that the three models have the same expected values, however, the autoregressive model cut by one-lagged period explores different shapes of variance, skewed and kurtosis coefficients from the other two models. This implies that the large samples lead to the same expected values, 2(1 – ρ0), whatever the variance-covariance matrix of the errors is assumed. Finally, comparing with the two sample cases, the shape of each coefficient is almost the same, moreover, the autocorrelation coefficients are negatively related with expected values, are inverted-U related with variances, are cubic related with skewed coefficients, and are U related with kurtosis coefficients.