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| Author | : Patrick Gagliardini |
| Publisher | : |
| Total Pages | : 40 |
| Release | : 2007 |
| Genre | : |
| ISBN | : |
| Author | : Christoph Breunig |
| Publisher | : |
| Total Pages | : |
| Release | : 2016 |
| Genre | : |
| ISBN | : |
There are many environments in econometrics which require nonseparable modeling of a structural disturbance. In a nonseparable model, key conditions are validity of instrumental variables and monotonicity of the model in a scalar unobservable. Under these conditions the nonseparable model is equivalent to an instrumental quantile regression model. A failure of the key conditions, however, makes instrumental quantile regression potentially inconsistent. This paper develops a methodology for testing the hypothesis whether the instrumental quantile regression model is correctly speci ed. Our test statistic is asymptotically normally distributed under correct speci cation and consistent against any alternative model. In addition, test statistics to justify model simpli cation are established. Finite sample properties are examined in a Monte Carlo study and an empirical illustration.
| Author | : Glenn Ellison |
| Publisher | : |
| Total Pages | : 56 |
| Release | : 1998 |
| Genre | : Forms, Quadratic |
| ISBN | : |
This paper presents a simple framework for testing the specification of parametric conditional means. The test statistics are based on quadratic forms in the residuals of the null model. Under general assumptions the test statistics are asymptotically normal under the null. With an appropriate choice of the weight matrix, the tests are shown to be consistent and to have good local power. Specific implementations involving matrices of bin and kernel weights are discussed. Finite sample properties are explored in simulations and an application to some parametric models of gasoline demand is presented.
| Author | : Yingyao Hu |
| Publisher | : |
| Total Pages | : |
| Release | : 2011 |
| Genre | : |
| ISBN | : |
This paper provides sufficient conditions for the nonparametric identification of the regression function m(.) in a regression model with an endogenous regressor x and an instrumental variable z. It has been shown that the identification of the regression function from the conditional expectation of the dependent variable on the instrument relies on the completeness of the distribution of the endogenous regressor conditional on the instrument, i.e., f(x/z). We provide sufficient conditions for the completeness of f(x/z) without imposing a specific functional form, such as the exponential family. We show that if the conditional density f(x/z) coincides with an existing complete density at a limit point in the support of z, then f(x/z) itself is complete, and therefore, the regression function m(.) is nonparametrically identified. We use this general result provide specific sufficient conditions for completeness in three different specifications of the relationship between the endogenous regressor x and the instrumental variable z.
| Author | : Christoph Breunig |
| Publisher | : |
| Total Pages | : |
| Release | : 2012 |
| Genre | : |
| ISBN | : |
This paper proposes several tests of restricted specification in nonparametric instrumental regression. Based on series estimators, test statistics are established that allow for tests of the general model against a parametric or nonparametric specification as well as a test of exogeneity of the vector of regressors. The tests are asymptotically normally distributed under correct specification and consistent against any alternative model. Under a sequence of local alternative hypotheses, the asymptotic distribution of the tests is derived. Moreover, uniform consistency is established over a class of alternatives whose distance to the null hypothesis shrinks appropriately as the sample size increases.
| Author | : Yingyao Hu |
| Publisher | : |
| Total Pages | : |
| Release | : 2021 |
| Genre | : |
| ISBN | : |
This paper provides a test for completeness in a class of nonparametric specification with an additive and independent error term. It is known that such a nonparametric location family of functions is complete if and only if the characteristic function of the error term has no zeros on the real line. Because a zero of the error characteristic function implies that of an observed marginal distribution, we propose a simple test for zeros of characteristic function of the observed distribution, in which rejection of the null hypothesis implies the completeness. This test is applicable to many popular setting, such as nonparametric regression models with instrumental variables, and nonclassical measurement error models. We describe the asymptotic behavior of the tests under the null and alternative hypotheses and investigate the finite sample properties of the proposed test through a Monte Carlo study. We illustrate our method empirically by estimating a measurement error model using the CPS/SSR 1978 exact match file.
| Author | : Serge Darolles |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2015 |
| Genre | : |
| ISBN | : |
The focus of the paper is the nonparametric estimation of an instrumental regression function f defined by conditional moment restrictions stemming from a structural econometric model: E [Y - f (Z) | W] = 0, and involving endogenous variables Y and Z and instruments W. The function f is the solution of an ill-posed inverse problem and we propose an estimation procedure based on Tikhonov regularization. The paper analyses identification and overidentification of this model and presents asymptotic properties of the estimated nonparametric instrumental regression function.
| Author | : Jinyong Hahn |
| Publisher | : |
| Total Pages | : 61 |
| Release | : 1999 |
| Genre | : |
| ISBN | : |
| Author | : Jeffrey Racine |
| Publisher | : Oxford University Press |
| Total Pages | : 562 |
| Release | : 2013-12-31 |
| Genre | : Business & Economics |
| ISBN | : 0199857954 |
This volume, edited by Jeffrey Racine, Liangjun Su, and Aman Ullah, contains the latest research on nonparametric and semiparametric econometrics and statistics. These data-driven models seek to replace the classical parametric models of the past, which were rigid and often linear. Chapters by leading international econometricians and statisticians highlight the interface between econometrics and statistical methods for nonparametric and semiparametric procedures. They provide a balanced view of new developments in the modeling of cross-section, time series, panel, and spatial data. Topics of the volume include: the methodology of semiparametric models and special regressor methods; inverse, ill-posed, and well-posed problems; methodologies related to additive models; sieve regression, nonparametric and semiparametric regression, and the true error of competing approximate models; support vector machines and their modeling of default probability; series estimation of stochastic processes and their application in Econometrics; identification, estimation, and specification problems in semilinear time series models; nonparametric and semiparametric techniques applied to nonstationary or near nonstationary variables; the estimation of a set of regression equations; and a new approach to the analysis of nonparametric models with exogenous treatment assignment.
| Author | : Neil R. Ericsson |
| Publisher | : |
| Total Pages | : 436 |
| Release | : 1994 |
| Genre | : Business & Economics |
| ISBN | : 9780198774044 |
This book discusses the nature of exogeneity, a central concept in standard econometrics texts, and shows how to test for it through numerous substantive empirical examples from around the world, including the UK, Argentina, Denmark, Finland, and Norway. Part I defines terms and provides the necessary background; Part II contains applications to models of expenditure, money demand, inflation, wages and prices, and exchange rates; and Part III extends various tests of constancy and forecast accuracy, which are central to testing super exogeneity. About the Series Advanced Texts in Econometrics is a distinguished and rapidly expanding series in which leading econometricians assess recent developments in such areas as stochastic probability, panel and time series data analysis, modeling, and cointegration. In both hardback and affordable paperback, each volume explains the nature and applicability of a topic in greater depth than possible in introductory textbooks or single journal articles. Each definitive work is formatted to be as accessible and convenient for those who are not familiar with the detailed primary literature.
