Comment on “modeling asset returns with alternative stable distributions”

This paper considers a modified CUSUM test, suggested by Dufour (1982) for parameter instability and structural change with an unknown change point in a linear model with serially correlated disturbances, in which a preliminary estimate of the autoregressive coefficient for the error process is obtained, and used to transform the data. Then the standard CUSUM statistic is calculated on the transformed data. This paper derives the asymptotic distribution of the modified CUSUM test. We show that the modified CUSUM test retains its asymptotic significance level, i.e., the modified CUSUM test has the same asymptotic distribution as the CUSUM test with serially uncorrelated errors.     

Canadian Leapfrog: From Regulating Charitable Fundraising to Co-Regulating Good Governance

The regulation of charitable fundraising is no longer just about the regulation of fundraising but about good governance, and increasingly involves co-regulatory regimes which blend elements of self- and state regulation. Canada??s charitable sector has undertaken a bold experiment in creating a comprehensive certification system for good governance, including fundraising, which reframes the target of regulation from the informed donor to the well-performing charity and has the ambitious goal of building a community of practice for self-improvement. At the same time, the federal government has introduced new guidance on fundraising that not only outlines accepted cost to revenue ratios but also specifies standards of good governance. It is an open question as to whether this new self- and state regulation will remain as dual systems or evolve into a hybrid co-regulatory regime in which government integrates sector certification into its own risk management.     

Inspire Confidence

With only a few contact hours a week the amount of knowledge foreign teachers can impart on their students is limited.Some people stick to the book while others follow in the footsteps of their predecessors.     

Nonstationary panel data analysis: an overview of some recent developments

This paper overviews some recent developments in panel data asymptotics, concentrating on the nonstationary panel case and gives a new result for models with individual effects. Underlying recent theory are asymptotics for multi-indexed processes in which both indexes may pass to infinity. We review some of the new limit theory that has been developed, show how it can be applied and give a new interpretation of individual effects in nonstationary panel data. Fundamental to the interpretation of much of the asymptotics is the concept of a panel regression coefficient which measures the long run average relation across a section of the panel. This concept is analogous to the statistical interpretation of the coefficient in a classical regression relation. A variety of nonstationary panel data models are discussed and the paper reviews the asymptotic properties of estimators in these various models. Some recent developments in panel unit root tests and stationary dynamic panel regression models are also reviewed.     

Partially adaptive estimation of nonlinear models via a normal mixture

This paper extends the partially adaptive method Phillips (1994) provided for linear models to nonlinear models. Asymptotic results are established under conditions general enough they cover both cross-sectional and time series applications. The sampling efficiency of the new estimator is illustrated in a small Monte Carlo study in which the parameters of an autoregressive moving average are estimated. The study indicates that, for non-normal distributions, the new estimator improves on the nonlinear least squares estimator in terms of efficiency.     

The persuasive effect of source credibility: a situational analysis.

The interactive effects of source credibility and other variables which affect the communication process are reviewed, and the extent to which these data are ordered by cognitive response and attribution theories is examined. On the basis of this review (1) situations where a credible source facilities, inhibits, and has no systematic persuasive effect are identified; (2) the explanatory power of cognitive response and attribution theory is demonstrated; and (3) a common language linking these theoretical formulations is advanced, providing a framework for investigating the persuasive mass communication process.     

The new labor market: Immigrants and wages after IRCA

We examine the effect of the Immigration Reform and Control Act (IRCA) on migrants' wages using data gathered in 39 Mexican communities and their U.S. destination areas. We examine changes in the determinants of wages before and after the passage of IRCA, as well as the effects of its massive legalization program. Migrants' wages deteriorated steadily between 1970 and 1995, but IRCA did not foment discrimination against Mexican workers per se. Rather, it appears to have encouraged greater discrimination against undocumented migrants, with employers passing the costs and risks of unauthorized hiring on to the workers. Although available data do not permit us to eliminate competing explanations entirely, limited controls suggest that the post-IRCA wage penalty against undocumented migrants did not stem from an expansion of the immigrant labor supply, an increase in the use of labor subcontracting, or a deterioration of the U.S. labor market.     

Historical Analysis of the Adoption and Safe Families Act of 1997

Congress enacted the Adoption and Safe Families Act (ASFA) in 1997 in an attempt to provide safety, stability, and permanency for maltreated children. To help provide a historical context, child welfare legislation preceding ASFA is reviewed. In this historical analysis, the precipitating events that led to the creation of ASFA are assessed. In addition, a detailed review of congressional hearings related to this Act is given. The key components and goals of this policy are outlined. Finally, revisions made to ASFA are discussed, and its influence on proceeding legislation is presented.     

Composite Forecasting: An Integrated Approach and Optimality Reconsidered

This article shows when the theoretical Lagrange multiplier solution for combining forecasts has a regression representation. This solution is not optimal in general because it imposes a restriction on an otherwise more general linear form. The optimal linear predictor based on N forecasts is presented. This predictor is or is not a regression function depending on whether the latter function is linear. I also show that the Lagrange multiplier solution may often be nearly optimal. Hence, when estimating a composite forecast, the restriction imposed by this solution may prove useful. This observation is supported in an empirical example.