k,i] are realizations of the model error and observation error distributions (Gaussians with

covariances [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] and [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], respectively).

Correlated uniquenesses (CUs) for Items 7 and 8 and Items 4 and 15 were included following inspection of modification index values and determination that error

covariance was due to item redundancy (Byrne, 2001).

Because Table 1 pools married and unmarried heads of household, the

covariances of head and spouse income are muted as it contains zeros for those with no spouse.

Unfortunately, the dataset did not permit the computation of

covariances between boar and sow effects (pair-wise analyses failed to converge with ASREML).

A debt manager's goal is thus to choose a currency for denomination of foreign debt for which the exchange rate vis-a-vis the domestic currency is relatively stable and the

covariance of the exchange rate and the primary balance is strongly positive, all subject to cost considerations.

Concerning the longitudinal

covariances of real fibres and simulated cylinders (in the xOy planes), the 4 curves in Fig.

The first approach explains the risk premium using

covariances with the current market return and with news about future market returns; this might be called "CAPM+," as it generalizes the insight about risk that was first formalized in the CAPM.

This leads to a number of about 5000 daily observations within the

covariance time series.

To enable a comparison of the properties of the estimates from an EM and a SM, it is helpful to partition the expected values of the parameter estimates and their

covariance matrix from the EM as follows:

Chan, Extended Tables of Means, Variances, and

Covariances of Order Statistics From the Extreme Value Distribution for Sample Sizes up to 30, Report, Department of Mathematics and Statistics, McMaster University, Hamilton, Canada (1992).

conditional stock market volatility is a linear function of lagged state variables), I (Guo, 2002) show that conditional stock market return is still a linear function of its

covariances with state variables, but the risk prices are complicated functions of the underlying structural parameters.

This source can be understood by looking at the formal definition of the correlation of growth rates, which is the

covariance of the growth rates divided by the product of the standard deviations of each of the two growth rates (see box "Correlation as a Measure of Co-movement").