Unobserved-Components Models for Seasonal Adjustment Filters

Time series models are presented, for which the seasonal-component estimates delivered by linear least squares signal extraction closely approximate those of the standard option of the widely-used Census X-11 program. Earlier work is extended by consideration of a broader class of models and by examination of asymmetric filters, in addition to the symmetric filter implicit in the adjustment of historical data. Various criteria that guide the specification of unobserved- components models are discussed, and a new preferred model is presented. Some nonstandard options in X-11 are considered in the Appendix.     

Protecting children in Scotland: examining the impact of the Children (Scotland) Act 1995

This paper discusses findings from a small‐scale study of the impact on child protection practice following implementation of the Children (Scotland) Act 1995. The Act introduced three new measures to allow the state to intervene in families to protect a child where there is a risk of significant harm. These include the child protection order, the child assessment order and the exclusion order. The child protection order provides for the removal of a child to (or his or her retention in) a place of safety. In the first two years of the operation of the 1995 Act fewer applications were made for this order compared with similar provisions under the previous legislation. This reduction in applications appears to be related to unfamiliarity with new legislation; greater scrutiny resulting from the more formal application to the sheriff court; and the introduction of a new legal criterion for intervention, the presence or likelihood of ‘significant harm’. The introduction of the ‘no order’ principle into Scottish child care law is also likely to be a factor.     

Empirical assessment of present value relations

This paper summarizes the literature on estimation and testing of present value relations. Twenty-four test statistics are illustrated and compared in a simulation experiment utilizing six different data generation models. The test statistics are calculated for actual Standard and Poor's 500 annual stock price and dividend data, and the results are interpreted in light of the Monte Carlo experiments.     

Evaluating the Evaluators: Should Investors Trust Corporate Governance Metrics Ratings?

Companies are under increasing pressure to have their corporate governance rated by an independent corporate governance metrics firm, such as Institutional Shareholder Services (ISS) or Governance Metrics International (GMI). These rating firms claim to be able to determine how effective and responsive a company’s board is. Institutional investors have begun using these board governance ratings when deciding which firms to include in their stock portfolios. This paper considers whether investors, many of whom claim to be socially responsible, should be relying upon board governance metrics. We find that these metrics are not good indicators of either the quality of a firm’s earnings or of its ethics.