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One theoretical measure of the value of a security is its option-adjusted spread (OAS). OAS analysis provides a uniform, rational approach to valuing a security net of embedded options. This in turn allows direct comparison to other instruments. OAS analysis uses information from the Treasury or swap yield curve to derive statistically an interest rate process that can be used to value CMOs. This process observes no-arbitrage conditions across the yield curve, and accurately prices other xed-income instruments, such as corporate bonds, swaps, and options. For mortgage-backed securities, however, OAS analysis also involves application of a prepayment model that attempts to capture the effects of interest rates on the mortgagee s option to prepay. The end result is the security s expected return, stated in terms of its return spread over the forward yield curve, adjusted for the prepayment option and any other embedded options (e.g., caps and oors). OAS analysis allows a portfolio manager to screen a large number of securities quickly. The size and diversity of the CMO market makes speed and ef ciency of analysis critical. Purchase decisions require some judgment and subjective analysis, which are time-consuming; OAS analysis can be used to screen out securities that are clearly overpriced, thus allowing the manager to focus on bonds that meet predetermined return criteria. OAS analysis can also provide effective duration and convexity measures, useful in hedging, and can pick up hidden risks (such as whipsaw risk), which traditional static analysis does not capture.

r(x) =

created a gender role attitude score by coding the responses to each of the six items, in such a way that a higher score indicates a more liberal gender role attitude, and then summing them. The score ranges from 6 to 30, with a mean in 1991 of 21.2 and a standard deviation of 3.7. One advantage of graphical chain models is their ability to handle response variables of different types: continuous, binary and multicategory. When the response is continuous, as is the case for the attitude score, we use linear regression models. When the response is binary, as is the case for whether or not the respondent becomes a parent, or multicategory, as is the case for the respondent s change in labour force status, we use binary and multinomial logistic regression models, respectively. Furthermore, sample weights, clustered samples and item nonresponse can be handled using the standard techniques developed for the component models. A disadvantage in the GCM approach considered here is that it does not use the information in the repeated measures or model the reciprocal relationship simultaneously. Hence, it is not easy to assess the overall goodness of fit of the model to the data. Also, it is difficult to explicitly adjust for measurement error without introducing a separate stage of latent variable modelling.

interviewer or the centralized tracking team while the case is in tracking status) and the sum of these two (total calls). Positive outliers were truncated at three standard deviations above the mean. The HRS has a large disparity in effort according to whether or not tracking was required, similar to that found in the PSID. Households not requiring tracking in 2004 needed an average of 5.23 field calls, compared to 9.40 for households needing tracking. In addition, households needing tracking required an additional 7.36 tracking calls, yielding an overall differential of 11.53 total calls (16.76 for tracking households vs. 5.23 for nontracking households).7 This differential is reduced only slightly when examined in a multivariate context, and tracking status remains a significant (P 5 0.001) predictor of overall effort. Thus, as with the PSID, tracking in the HRS adds significant costs to data collection. Also presented in Table 11.7 are means for number of calls by participation in the 2003 supplements. In addition to being a strong predictor of need for tracking, this indicator is also highly predictive of effort, controlling for tracking status. Such factors can be effectively used to identify households or respondents who are likely to be difficult to track. Those cases can then be targeted for centralized tracking prior to or at the start of the field period, reducing effort on the part of interviewers during the field period.

, where the cap or oor premium is paid in installments) may be amortized by treating the contract as if it provided for an upfront payment equal to the present value of the nonperiodic payment and a loan between the counterparties As a result, a cap or oor premium paid in level annual installments over the term of the contract is taken into account in accordance with the level payment method [Treas Reg 1446-3(f)(2)(v)(B)]30 Under the NPC Regulations, a taxpayer may also treat a cap and a oor that comprise a collar as a single NPC and may amortize the net nonperiodic payment to enter into the cap and oor over the term of the collar, in accordance with the other methods that apply to caps and oors.

SEBTsect39 (0 2E17)

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