Semi-nonparametric IV estimation of shape-invariant Engel curves

This paper studies a shape-invariant Engel curve system with endogenous total expenditure, in which the shape-invariant specification involves a common shift parameter for each demographic group in a pooled system of nonparametric Engel curves. We focus on the identification and estimation of both the nonparametric shapes of the Engel curves and the parametric specification of the demographic scaling parameters. The identification condition relates to the bounded completeness and the estimation procedure applies the sieve minimum distance estimation of conditional moment restrictions, allowing for endogeneity. We establish a new root mean squared convergence rate for the nonparametric instrumental variable regression when the endogenous regressor could have unbounded support. Root-n asymptotic normality and semiparametric efficiency of the parametric components are also given under a set of "low-level" sufficient conditions. Our empirical application using the U.K. Family Expenditure Survey shows the importance of adjusting for endogeneity in terms of both the nonparametric curvatures and the demographic parameters of systems of Engel curves

Accelerating Reinforcement Learning by Composing Solutions of Automatically Identified Subtasks

This paper discusses a system that accelerates reinforcement learning by using transfer from related tasks. Without such transfer, even if two tasks are very similar at some abstract level, an extensive re-learning effort is required. The system achieves much of its power by transferring parts of previously learned solutions rather than a single complete solution. The system exploits strong features in the multi-dimensional function produced by reinforcement learning in solving a particular task. These features are stable and easy to recognize early in the learning process. They generate a partitioning of the state space and thus the function. The partition is represented as a graph. This is used to index and compose functions stored in a case base to form a close approximation to the solution of the new task. Experiments demonstrate that function composition often produces more than an order of magnitude increase in learning rate compared to a basic reinforcement learning algorithm

Human Capital, Age Structure and Growth Fluctuation

This paper assesses empirically the relationship between GDP per capita growth fluctuations and the age structure and intensity of human capital across developed and developing countries. We estimate a spatial vector autoregressive model of income dynamics where the economic distance between countries is defined on their similarity in measures of human capital and its distribution across age groups. These distances are computed using a newly developed human capital data set. Spatial effects on growth volatility and complemetarity in national growth processes are explored with respect to the proposed distance metrics. Our results imply that significant growth interdependence based on human capital distances exists among countries, with highly non-linear effects

On photon correlation measurements of colloidal size distributions using Bayesian strategies

AbstractIn this paper the evaluation of particle size distribution using photon correlation spectroscopy according to the method of regularization of first kind integral equation including Laplace transform by means of Bayesian strategy is presented. We shall convert the Laplace transform to first kind integral equation of convolution type, which is an ill-posed problem. Then we use the Bayesian regularization method to solve it. This type of problem plays an important role in the field of photon correlation spectroscopy, fluorescent decay, sedimentation equilibrium, system theory and in other areas of physics and applied mathematics. The method is applied to test problems taken from the literature and it gives a good approximation to the true solution

Experienced Utility versus Decision Utility: Putting the 'S' in Satisfaction

Recent research distinguishes an individual's decision utility, inferred from her observed choices, from her experienced utility, which more closely matches the notion of happiness. Using various estimation techniques with a unique experimental data set, we test whether post-choice satisfaction (experienced utility), like decision utility, is S-shaped with loss aversion around a given reference point. We also present a model which estimates the satisfaction function and reference point simultaneously. When pooling the data across individuals, we find an S-shaped satisfaction function in which the reference point depends on past payments, social comparisons, and subjective expectations. There is mixed evidence of loss aversion. At the individual level, there is substantial variation in satisfaction function shapes, although the S-shape is common. Though the two notions of utility are distinct, our findings imply that the two are related at a fundamental level.Happiness; Utility; Experiment; Value function; Prospect theory

Construction of ECT-B-splines, a survey

s-dimensional generalized polynomials are linear combinations of functions forming an ECT-system on a compact interval with coefficients from R . ECT-spline curves in R are constructed by glueing together at interval endpoints generalized polynomials generated from different local ECT-systems via connection matrices. If they are nonsingular, lower triangular and totally positive there is a basis of the space of 1-dimensional ECT-splines consisting of functions having minimal compact supports normalized to form a nonnegative partition of unity. Its functions are called ECT-B-splines. One way (which is semiconstructional) to prove existence of such a basis is based upon zero bounds for ECT-splines. A constructional proof is based upon a definition of ECT-B-splines by generalized divided differences extending Schoenberg’s classical construction of ordinary polynomial B-splines. This fact eplains why ECT-B-splines share many properties with ordinary polynomial B-splines. In this paper we survey such constructional aspects of ECT-splines which in particular situations reduce to classical results. s Key Words: ECT-systems, ECT-B-splines, ECT-spline curves, de-Boor algorithm AMS Classification Number: 41A15, 41A0

Quadratic vs cubic spline-wavelets for image representations and compression

The Wavelet Transform generates a sparse multi-scale signal representation which may be readily compressed. To implement such a scheme in hardware, one must have a computationally cheap method of computing the necessary transform data. The use of semi-orthogonal quadrat