Dementia Problem Behavior and the Production of Informal Caregiving Services

Informal caregivers of individuals with Alzheimer's disease spend a considerable amount of time providing care. In this paper, we use Grossman's health production and Becker's time allocation models to develop a model of informal care provision to elderly dementia patients. In our model, time inputs produce caregiving services, which provides utility to the caregiver, but reduces leisure. We assume that time is less productive of services on the margin as the disease progresses. In this framework, an increase in patients' disease severity does not necessarily increase informal caregiver time input. The cost of formal care establishes a reservation price for informal caregiving. When the costs of informal caregiving rise above this reservation level, the patient is institutionalized. We test empirically the effect of deterioration in the patients' condition, proxied by both disease severity and dementia problem behavior, on informal caregiving time. We find that dementia-related problem behaviors and functional limitations significantly increase inputs of informal caregiving time. Patients' problem behavior exerts a modifying effect on functional limitations, and patients' comorbidities have no effect on informal caregiving time.     

On the Evolution of Competitive Balance: The Impact of an Increasing Global Search

The Coase theorem maintains that where free-market precepts exist, the allocation of property rights does not impact the distribution of resources. An application to Major League Baseball suggests that institutions such as free agency and the reverse-order amateur draft would not impact player distributions and therefore would not impact competitive balance. The present study finds that the distribution of wins is generally consistent with the precepts of the Coase theorem and therefore suggests a course for those who wish to alter the level of competitive balance: Major League Baseball should increase its focus on expanding the size of its labor pool. (JEL O15 , L83 , C22 )     

私人妨害原则与相邻权、地役权

文章剖析了英美物权法上“使用权”的概念 ,并解释了财产权就像“权利集束”这个常用比喻 ,并对美国普通法上财产法中的有关相邻方之间土地纠纷的法规进行了比较分析 ,其中特别比较了普通法上的私人妨害原则和中国物权法草案第 9章所规定的相邻权的概念 ,并将普通法体系中的各种私人土地使用协议和地役权与中国物权法草案第 16章的内容进行了对比。     

Expectations, Capital Gains, and Income

A theoretical framework for the measurement of income under uncertainty is developed that addresses some long-standing controversies about the treatment of capital gains. The consequences for economic analysis and policy making are potentially serious, because the treatment of capital gains can significantly affect some major macroeconomic aggregates, including national income and savings, balance of payments deficits, government deficits, and depreciation. (JEL O47 , P44 , Q32 )     

Choice of the primary analysis in longitudinal clinical trials

Missing data, and the bias they can cause, are an almost ever‐present concern in clinical trials. The last observation carried forward (LOCF) approach has been frequently utilized to handle missing data in clinical trials, and is often specified in conjunction with analysis of variance (LOCF ANOVA) for the primary analysis. Considerable advances in statistical methodology, and in our ability to implement these methods, have been made in recent years. Likelihood‐based, mixed‐effects model approaches implemented under the missing at random (MAR) framework are now easy to implement, and are commonly used to analyse clinical trial data. Furthermore, such approaches are more robust to the biases from missing data, and provide better control of Type I and Type II errors than LOCF ANOVA. Empirical research and analytic proof have demonstrated that the behaviour of LOCF is uncertain, and in many situations it has not been conservative. Using LOCF as a composite measure of safety, tolerability and efficacy can lead to erroneous conclusions regarding the effectiveness of a drug. This approach also violates the fundamental basis of statistics as it involves testing an outcome that is not a physical parameter of the population, but rather a quantity that can be influenced by investigator behaviour, trial design, etc. Practice should shift away from using LOCF ANOVA as the primary analysis and focus on likelihood‐based, mixed‐effects model approaches developed under the MAR framework, with missing not at random methods used to assess robustness of the primary analysis. Copyright © 2004 John Wiley & Sons, Ltd.     

Bias Correction in the Dynamic Panel Data Model with a Nonscalar Disturbance Covariance Matrix

Approximation formulae are developed for the bias of ordinary and generalized Least Squares Dummy Variable (LSDV) estimators in dynamic panel data models. Results from Kiviet [Kiviet, J. F. (1995), on bias, inconsistency, and efficiency of various estimators in dynamic panel data models, J. Econometrics68:53-78; Kiviet, J. F. (1999), Expectations of expansions for estimators in a dynamic panel data model: some results for weakly exogenous regressors, In: Hsiao, C., Lahiri, K., Lee, L-F., Pesaran, M. H., eds., Analysis of Panels and Limited Dependent Variables, Cambridge: Cambridge University Press, pp. 199-225] are extended to higher-order dynamic panel data models with general covariance structure. The focus is on estimation of both short- and long-run coefficients. The results show that proper modelling of the disturbance covariance structure is indispensable. The bias approximations are used to construct bias corrected estimators which are then applied to quarterly data from 14 European Union countries. Money demand functions for M1, M2 and M3 are estimated for the EU area as a whole for the period 1991: I-1995: IV. Significant spillovers between countries are found reflecting the dependence of domestic money demand on foreign developments. The empirical results show that in general plausible long-run effects are obtained by the bias corrected estimators. Moreover, finite sample bias, although of moderate magnitude, is present underlining the importance of more refined estimation techniques. Also the efficiency gains by exploiting the heteroscedasticity and cross-correlation patterns between countries are sometimes considerable.     

Markov chain Monte Carlo methods for high dimensional inversion in remote sensing

Summary.  We discuss the inversion of the gas profiles (ozone, NO₃, NO₂, aerosols and neutral density) in the upper atmosphere from the spectral occultation measurements. The data are produced by the 'Global ozone monitoring of occultation of stars' instrument on board the Envisat satellite that was launched in March 2002. The instrument measures the attenuation of light spectra at various horizontal paths from about 100 km down to 10–20 km. The new feature is that these data allow the inversion of the gas concentration height profiles. A short introduction is given to the present operational data management procedure with examples of the first real data inversion. Several solution options for a more comprehensive statistical inversion are presented. A direct inversion leads to a non-linear model with hundreds of parameters to be estimated. The problem is solved with an adaptive single-step Markov chain Monte Carlo algorithm. Another approach is to divide the problem into several non-linear smaller dimensional problems, to run parallel adaptive Markov chain Monte Carlo chains for them and to solve the gas profiles in repetitive linear steps. The effect of grid size is discussed, and we present how the prior regularization takes the grid size into account in a way that effectively leads to a grid-independent inversion.     

Interactionism,Social Organization,and Social Processes: Looking Back and Moving Ahead

With its roots in American pragmatism, symbolic interactionism has created a distinctive perspective and produced numerous important contributions and now offers significant prospects for the future. In this article, I review my intellectual journey with this perspective over forty years. This journey was initiated within the American society, sociology, and symbolic interaction of circa 1960. I note many of the contributions made by interactionists since that time, with particular focus on those who have contributed to the study of social organization and social process. I offer an agenda for the future based on currently underdeveloped areas that have potential. These are inequality orders, institutional analysis, collective action across space and time, and the integration of temporal and spatial orders. The article concludes with calls for further efforts at cross‐perspective dialogues, more attention to feminist scholars, and an elaborated critical pragmatism.     

Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances

We study the properties of the quasi-maximum likelihood estimator (QMLE) and related test statistics in dynamic models that jointly parameterize conditional means and conditional covariances, when a normal log-likelihood os maximized but the assumption of normality is violated. Because the score of the normal log-likelihood has the martingale difference property when the forst two conditional moments are correctly specified, the QMLE is generally Consistent and has a limiting normal destribution. We provide easily computable formulas for asymptotic standard errors that are valid under nonnormality. Further, we show how robust LM tests for the adequacy of the jointly parameterized mean and variance can be computed from simple auxiliary regressions. An appealing feature of these robyst inference procedures is that only first derivatives of the conditional mean and variance functions are needed. A monte Carlo study indicates that the asymptotic results carry over to finite samples. Estimation of several AR and AR-GARCH time series models reveals that in most sotuations the robust test statistics compare favorably to the two standard (nonrobust) formulations of the Wald and IM tests. Also, for the GARCH models and the sample sizes analyzed here, the bias in the QMLE appears to be relatively small. An empirical application to stock return volatility illustrates the potential imprtance of computing robust statistics in practice.