Instituto Tecnológico Autónomo de México

About: Instituto Tecnológico Autónomo de México is a education organization based out in Mexico City, Mexico. It is known for research contribution in the topics: Politics & Population. The organization has 1098 authors who have published 2532 publications receiving 39083 citations. The organization is also known as: Instituto Tecnologico Autonomo de Mexico & Mexico Autonomous Institute of Technology.

A nonparametric test for i(0)

Abstract: There is frequently interest in testing that a scalar or vector time series is I(0), possibly after first- differencing or other detrending, while the I(0) assumption is also taken for granted in autocorrelation-consistent variance estimation. We propose a test for I(0) against fractional alternatives. The test is non-parametric, and indeed makes no assumptions on spectral behaviour away from zero frequency. It seems likely to have good efficiency against fractional alternatives, relative to other nonparametric tests. The test is given large samle justification, subjected to a Monte Carlo analysis of finite sample behaviour, and applied to various empirical data series.

Consistent Estimation of Models Defined by Conditional Moment Restrictions

Abstract: In econometrics, models stated as conditional moment restrictions are typically estimated by means of the generalized method of moments (GMM). The GMM estimation procedure can render inconsistent estimates since the number of arbitrarily chosen instruments is finite. In fact, consistency of the GMM estimators relies on additional assumptions that imply unclear restrictions on the data generating process. This article introduces a new, simple and consistent estimation procedure for these models that is directly based on the definition of the conditional moments. The main feature of our procedure is its simplicity, since its implementation does not require the selection of any user-chosen number, and statistical inference is straightforward since the proposed estimator is asymptotically normal. In addition, we suggest an asymptotically efficient estimator constructed by carrying out one Newton–Raphson step in the direction of the efficient GMM estimator.

Automatic Preconditioning by Limited Memory Quasi-Newton Updating

Abstract: This paper proposes a preconditioner for the conjugate gradient method (CG) that is designed for solving systems of equations Ax=bi with different right-hand-side vectors or for solving a sequence of slowly varying systems Ak x = bk. The preconditioner has the form of a limited memory quasi-Newton matrix and is generated using information from the CG iteration. The automatic preconditioner does not require explicit knowledge of the coefficient matrix A and is therefore suitable for problems where only products of A times a vector can be computed. Numerical experiments indicate that the preconditioner has most to offer when these matrix-vector products are expensive to compute and when low accuracy in the solution is required. The effectiveness of the preconditioner is tested within a Hessian-free Newton method for optimization and by solving certain linear systems arising in finite element models.