Implementation issues for consumer-directed programs: comparing views of policy experts, consumers, and representatives

An increasing number of aging community providers and consumers support consumer-direction (CD) in long-term care services. In regard to devolution, consumer-direction goes beyond the usual approach of shifting responsibilities from the federal government to state governments to bring programs "closer to the people." Consumer-direction goes even further by placing resources directly in the hands of consumers. Yet, many questions remain unanswered regarding how to implement CD personal assistance services in general, and especially for older persons. This article describes the importance of examining views from multiple key stakeholders involved in implementing CD programs. We report on three background studies that have informed the Cash and Counseling Demonstration and Evaluation (CCDE) design and implementation-policy expert interviews as well as surveys and focus groups with consumers and representatives. As a fourth data source, we drew upon experiences in designing the CCDE and initial results from the first year of implementation. Each of the three studies on its own provided essential information for planning the CCDE. However, when we examined the studies together, and added CCDE design and implementation experiences, views expressed by the different stakeholders formed a type of multi-perspective "dialogue" that expanded our knowledge about implementing CD services. We hope this increased knowledge will help expand the availability of such services for consumers of any age who want to direct their own care.     

Portmanteau tests for linearity of stationary time series

This article considers the problem of testing for linearity of stationary time series. Portmanteau tests are discussed which are based on generalized correlations of residuals from a linear model (that is, autocorrelations and cross-correlations of different powers of the residuals). The finite-sample properties of the tests are assessed by means of Monte Carlo experiments. The tests are applied to 100 time series of stock returns.     

A note on super exogeneity in linear regression models

This note considers how hypotheses of invariance and super exogeneity may be formulated and tested in elliptical linear regression models. It is demonstrated that for jointly elliptical random variables super exogeneity will only hold under normality.     

Real option games with R&D and learning spillovers

We demonstrate to decision makers how to optimally make costly strategic pre-investment R&D decisions in the presence of spillover effects in an option pricing framework with analytic tractability. Decisions are modeled as impulse-type controls with random outcome. Two firms face two decisions that are solved interdependently in a two-stage game. The first-stage decision is: What is the optimal level of coordination (optimal policy/technology choice)? The second-stage decision is: What is the optimal effort for a given level of the spillover effects and the cost of information acquisition? The framework is extended to a two-period closed-loop stochastic game with (path-dependency inducing) switching costs that make strategy revisions harder. When conditions of learning-by-doing exist, we find that strategy shifts are easier to observe in market environments of high growth and high volatility.     

On regression-based tests for persistence in logarithmic volatility models

Building on the work of Pantula (1986), this paper discusses how the hypothesis of conditional variance nonstationarity in the logarithmic family of generalized autoregressive conditional heteroskedasticity (GARCH) and stochastic volatility processes may be tested using regression-based tests. The latter are easy to implement, have well-defined large-sample distributions, and are less sensitive to structural changes than tests based on the quasimaximum likelihood estimator.     

Household wealth and the measurement of economic well-being in the United States

We analyze the level and distribution of economic well-being in the United States during the 1980s and 1990s based on the standard measure of money income and a measure in which income from wealth is calculated as the sum of lifetime annuity from nonhome wealth and imputed rental-equivalent for owner-occupied homes. Over the 1982–2000 period, median well-being increases faster when these adjustments are made than when standard money income is used. This adjustment also widens the income gap between African-Americans and whites but increases the relative well-being of the elderly. Adding imputed rent and annuities from household wealth to household income considerably increases measured inequality and the share of income from wealth in inequality. However, both measures show about the same rise in inequality over the period. We also find an increasing share of wage and salary income in our expanded definition of income among the richest 1% over the period but do not find that the “working rich” have largely replaced rentiers at the top of the economic ladder.      

Using the Bootstrap to Test for Symmetry Under Unknown Dependence

This article considers tests for symmetry of the one-dimensional marginal distribution of fractionally integrated processes. The tests are implemented by using an autoregressive sieve bootstrap approximation to the null sampling distribution of the relevant test statistics. The sieve bootstrap allows inference on symmetry to be carried out without knowledge of either the memory parameter of the data or of the appropriate norming factor for the test statistic and its asymptotic distribution. The small-sample properties of the proposed method are examined by means of Monte Carlo experiments, and applications to real-world data are also presented.     

Semiparametric Sieve-Type Generalized Least Squares Inference

This article considers the problem of statistical inference in linear regression models with dependent errors. A sieve-type generalized least squares (GLS) procedure is proposed based on an autoregressive approximation to the generating mechanism of the errors. The asymptotic properties of the sieve-type GLS estimator are established under general conditions, including mixingale-type conditions as well as conditions which allow for long-range dependence in the stochastic regressors and/or the errors. A Monte Carlo study examines the finite-sample properties of the method for testing regression hypotheses.     

Bootstrap-assisted tests of symmetry for dependent data*

The paper considers the problem of testing for symmetry (about an unknown centre) of the marginal distribution of a strictly stationary and weakly dependent stochastic process. The possibility of using the autoregressive sieve bootstrap and stationary bootstrap procedures to obtain critical values and P-values for symmetry tests is explored. Bootstrap-assisted tests for symmetry are straightforward to implement and require no prior estimation of asymptotic variances. The small-sample properties of a wide variety of tests are investigated using Monte Carlo experiments. A bootstrap-assisted version of the triples test is found to have the best overall performance.