1,894 research outputs found
What Really Happened To Consumption Inequality in the United States?
No full textThis paper considers data quality issues for the analysis of consumption inequality exploiting two complementary datasets from the Consumer Expenditure Survey for the United States. The Interview sample follows survey households over four calendar quarters and consists of retrospectively collected information about monthly expenditures on durable and non-durable goods. The Diary sample interviews household for two consecutive weeks and includes detailed information about frequently purchased items (food, personal cares and household supplies). Most reliable information from each sample is exploited to derive a correction for the measurement error affecting observed measures of consumption inequality in the two surveys. We find that consumption inequality, as measured by the standard deviation of log non-durable consumption, has increased by roughly 5% points during the 1990s
What Really Happened To Consumption Inequality in the United States?
No full textThis paper considers data quality issues for the analysis of consumption inequality exploiting two complementary datasets from the Consumer Expenditure Survey for the United States. The Interview sample follows survey households over four calendar quarters and consists of retrospectively collected information about monthly expenditures on durable and non-durable goods. The Diary sample interviews household for two consecutive weeks and includes detailed information about frequently purchased items (food, personal cares and household supplies). Most reliable information from each sample is exploited to derive a correction for the measurement error affecting observed measures of consumption inequality in the two surveys. We find that consumption inequality, as measured by the standard deviation of log non-durable consumption, has increased by roughly 5% points during the 1990s
Semiparametric reduced-form estimation of tuition subsidies
No full textThe goal of this paper is to use a semiparametric reduced form model to estimate the effects of various tuition subsidies. This approach expands on the tuition subsidy example in Ichimura and Taber (2000) in a number of dimensions. It has become common practice in the empirical literature to refer to any nonstructural empirical analysis as reduced form. This is not the traditional sense of the phrase. A classic reduced form analysis (see e.g. Marschak, 1953) first specifies a structural model and then derives the reduced form parameters in terms of the structural parameters. While many recent studies have asserted to taking a reduced form approach, the structural parameters. While many recent studies have asserted to taking a reduced form approach, the structural model which the reduced form model should correspond is rarely specified. We explicitly specify a structural model and use the implied reduced form structure to estimate the effect of tuition subsidy policies. Specifying the underlying model has the advantage of being explicit about the assumptions that justify the analysis. This avoids Rosenzweig and Wolpin's (2000) criticism of work on natural 'natural experiments' that often leaves these conditions implicit. Our structural model is based on the model studied by Keane and Wolpin (1999). It is highly nonlinear and allows for more unobserved heterogeneity than the typical simultaneous equations framework that most previous work has used in reduced form estimation. Using hte specified structural model, we examine the assumptions discussed in Ichimura and Taber (2000) to justify reduced form estimation of the policy effect
Characterization of the asymptotic distribution of semiparametric M-estimators
No full textThis paper develops a concrete formula for the asymptotic distribution of two-step, possibly non-smooth semiparametric M-estimators under general misspecification. Our regularity conditions are relatively straightforward to verify and also weaker than those available in the literature. The first-stage nonparametric estimation may depend on finite dimensional parameters. We characterize: (1) conditions under which the first-stage estimation of nonparametric components do not affect the asymptotic distribution, (2) conditions under which the asymptotic distribution is affected by the derivatives of the first-stage nonparametric estimator with respect to the finite-dimensional parameters, and (3) conditions under which one can allow non-smooth objective functions. Our framework is illustrated by applying it to three examples: (1) profiled estimation of a single index quantile regression model, (2) semiparametric least squares estimation under model misspecification, and (3) a smoothed matching estimator. © 2010 Elsevier B.V. All rights reserved
ITF1697, a Stable Lys-Pro-containing Peptide, Inhibits Weibel Palade Body Exocytosis Induced by Ischemia/Reperfusion and Pressure Elevation
No full text"Characterization of the Asymptotic Distribution of Semiparametric M-Estimators"
No full textThis paper develops a concrete formula for the asymptotic distribution of two-step, possibly non-smooth semiparametric M-estimators under general misspecification. Our regularity conditions are relatively straightforward to verify and also weaker than those available in the literature. The first-stage nonparametric estimation may depend on finite dimensional parameters. We characterize: (1) conditions under which the first-stage estimation of nonparametric components do not affect the asymptotic distribution, (2) conditions under which the asymptotic distribution is affected by the derivatives of the first-stage nonparametric estimator with respect to the finite-dimensional parameters, and (3) conditions under which one can allow non-smooth objective functions. Our framework is illustrated by applying it to three examples: (1) profiled estimation of a single index quantile regression model, (2) semiparametric least squares estimation under model misspecification, and (3) a smoothed matching estimator.
HILBERT-SPEISER NUMBER FIELDS AND STICKELBERGER IDEALS; THE CASE p = 2
No full textWe say that a number field F satisfies the condition (H′2m) when any abelian extension of exponent dividing 2m has a normal basis with respect to rings of 2-integers. We say that it satisfies (H′
2∞) when it satisfies (H′
2m) for all m. We give a condition for F to satisfy (H'2m), and show that the imaginary quadratic fields F = Q(√−1) and Q(√−2) satisfy the very strong condition (H′
2∞) if the conjecture that h+2m = 1 for all m is valid. Here, h+2m) is the class number of the maximal real abelian field of conductor 2m
Characterization of the asymptotic distribution of semiparametric M-estimators
No full textThis paper develops a concrete formula for the asymptotic distribution of two-step, possibly non-smooth semiparametric M-estimators under general misspecification. Our regularity conditions are relatively straightforward to verify and also weaker than those available in the literature. The first-stage nonparametric estimation may depend on finite dimensional parameters. We characterize: (1) conditions under which the first-stage estimation of nonparametric components do not affect the asymptotic distribution, (2) conditions under which the asymptotic distribution is affected by the derivatives of the first-stage nonparametric estimator with respect to the finite-dimensional parameters, and (3) conditions under which one can allow non-smooth objective functions. Our framework is illustrated by applying it to three examples: (1) profiled estimation of a single index quantile regression model, (2) semiparametric least squares estimation under model misspecification, and (3) a smoothed matching estimator.
A functional polymorphism in the NKG2D gene modulates NK-cell cytotoxicity and is associated with susceptibility to Human Papilloma Virus-related cancers
No full text金沢大学博士（医学）博士論文要旨Abstract 以下に掲載：SCIENTIFIC REPORTS Scientific reports 6 39231 pp.1-12 20-Dec-2016. J.Luis Espinoza & Viet H. Nguyen. 共著者：J. Luis Espinoza, Viet H. Nguyen, Hiroshi Ichimura, Trang T. T. Pham, Cuong H. Nguyen, Thuc V. Pham, Mahmoud I. Elbadry, Katsuji Yoshioka, Junji Tanaka, Ly Q. Trung, Akiyoshi Takami, Shinji Nakaothesi
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