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Author: Katarzyna Bech
Publisher:
ISBN:
Category :
Languages : en
Pages : 38
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Book Description
This paper presents new approaches to testing for exogeneity in non-parametric models with discrete regressors and instruments. Our interest is in learning about an unknown structural (conditional mean) function. An interesting feature of these models is that under endogeneity the identifying power of a discrete instrument depends on the number of support points of the instruments relative to that of the regressors, a result driven by the discreteness of the variables. Observing that the simple nonparametric additive error model can be interpreted as a linear regression, we present two test-statistics. For the point identifying model, the test is an adapted version of the standard Wu-Hausman approach. This extends the work of Blundell and Horowitz (2007) to the case of discrete regressors and instruments. For the set identifying model, the Wu-Hausman approach is not available. In this case the test-statistic is derived from a constrained minimization problem. The asymptotic distributions of the test-statistics are derived under the null and fixed and local alternatives. The tests are shown to be consistent, and a simulation study reveals that the proposed tests have satisfactory finite-sample properties.
Author: Katarzyna Bech
Publisher:
ISBN:
Category :
Languages : en
Pages : 38
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Book Description
This paper presents new approaches to testing for exogeneity in non-parametric models with discrete regressors and instruments. Our interest is in learning about an unknown structural (conditional mean) function. An interesting feature of these models is that under endogeneity the identifying power of a discrete instrument depends on the number of support points of the instruments relative to that of the regressors, a result driven by the discreteness of the variables. Observing that the simple nonparametric additive error model can be interpreted as a linear regression, we present two test-statistics. For the point identifying model, the test is an adapted version of the standard Wu-Hausman approach. This extends the work of Blundell and Horowitz (2007) to the case of discrete regressors and instruments. For the set identifying model, the Wu-Hausman approach is not available. In this case the test-statistic is derived from a constrained minimization problem. The asymptotic distributions of the test-statistics are derived under the null and fixed and local alternatives. The tests are shown to be consistent, and a simulation study reveals that the proposed tests have satisfactory finite-sample properties.
Author: Qi Li
Publisher: Princeton University Press
ISBN: 0691248087
Category : Business & Economics
Languages : en
Pages : 768
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Book Description
A comprehensive, up-to-date textbook on nonparametric methods for students and researchers Until now, students and researchers in nonparametric and semiparametric statistics and econometrics have had to turn to the latest journal articles to keep pace with these emerging methods of economic analysis. Nonparametric Econometrics fills a major gap by gathering together the most up-to-date theory and techniques and presenting them in a remarkably straightforward and accessible format. The empirical tests, data, and exercises included in this textbook help make it the ideal introduction for graduate students and an indispensable resource for researchers. Nonparametric and semiparametric methods have attracted a great deal of attention from statisticians in recent decades. While the majority of existing books on the subject operate from the presumption that the underlying data is strictly continuous in nature, more often than not social scientists deal with categorical data—nominal and ordinal—in applied settings. The conventional nonparametric approach to dealing with the presence of discrete variables is acknowledged to be unsatisfactory. This book is tailored to the needs of applied econometricians and social scientists. Qi Li and Jeffrey Racine emphasize nonparametric techniques suited to the rich array of data types—continuous, nominal, and ordinal—within one coherent framework. They also emphasize the properties of nonparametric estimators in the presence of potentially irrelevant variables. Nonparametric Econometrics covers all the material necessary to understand and apply nonparametric methods for real-world problems.
Author: Xavier de Luna
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Qi Li
Publisher: Emerald Group Publishing
ISBN: 1849506248
Category : Business & Economics
Languages : en
Pages : 576
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Book Description
Contains a selection of papers presented initially at the 7th Annual Advances in Econometrics Conference held on the LSU campus in Baton Rouge, Louisiana during November 14-16, 2008. This work is suitable for those who wish to familiarize themselves with nonparametric methodology.
Author: Christoph Breunig
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
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: Neil R. Ericsson
Publisher: Oxford University Press, USA
ISBN:
Category : Business & Economics
Languages : en
Pages : 440
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Book Description
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 thenecessary 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.
Author: Mehmet Caner
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Currently, the commonly employed instrumental variables strategy relies on the knife-edge assumption of perfect exogeneity for valid inference. To make reliable inferences on the structural parameters under violations of exogeneity one must know the true correlation between the structural error and the instruments. The main innovation in this paper is to identify an appropriate test in this context: a joint null hypothesis of the structural parameters with the correlation between the instruments and the structural error term. We introduce a new endogeneity accounted test by combining the structural parameter inference while correcting the bias associated with non-exogeneity of the instrument. To address inference under violations of exogeneity, significant contributions have been made in the recent literature by assuming some degree of non-exogeneity. A key advantage of our approach over that of the previous literature is that we do not need to make any assumptions about the degree of violation of exogeneity either as possible values or prior distributions. In particular, our method is not a form of sensitivity analysis. Since our test statistic is continuous and monotonic in correlation, one can conduct inference for the structural parameters by a simple grid search over correlation values. We can make accurate inferences on the structural parameters because of a feature of the grid search over correlation values. One can also build joint confidence intervals for the structural parameters and the correlation parameter by inverting the test statistic. In the inversion, the null values of these parameters are used. We also propose a new way of testing exclusion restrictions, even in the just identified case.
Author: Yingyao Hu
Publisher:
ISBN:
Category :
Languages : en
Pages :
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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: William Greene
Publisher: Emerald Group Publishing
ISBN: 0857241494
Category : Business & Economics
Languages : en
Pages : 371
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Book Description
This collection of methodological developments and applications of simulation-based methods were presented at a workshop at Louisiana State University in November, 2009. Topics include: extensions of the GHK simulator; maximum-simulated likelihood; composite marginal likelihood; and modelling and forecasting volatility in a bayesian approach.
Author: Carl David August Green
Publisher:
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Category :
Languages : en
Pages :
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This dissertation contains three essays on nonparametric and semiparametric regression methods. In the first essay, we consider the problem of nonparametric regression with mixed discrete and continuous covariates using the k-nearest neighbor (k-nn) method. We derive the asymptotic normality of the proposed estimator and use Monte Carlo simulations to demonstrate its finite sample performance. We apply the method to estimate corn yields in Iowa as a function of agricultural district, temperature, and precipitation. In the second essay, we consider the problem of testing error serial correlation in fixed effects panel data models in a nonparametric framework. We show that our test statistic has a standard normal distribution under the null hypothesis of zero serial correlation. The test statistic diverges to infinity at the rate of √N under the alternative hypothesis that errors are serially correlated, where N is the cross-sectional sample size. We propose a bootstrap version of the test which we show to perform well in finite sample applications. In the third essay, we consider estimation of varying-coefficient single-index models with an endogenous regressor. We propose a multi-step instrumental variables procedure to estimate the coefficient function and the corresponding index parameters. We prove the consistency of the estimators, and we present Monte Carlo simulations demonstrating their finite sample performance. We then apply the proposed method to examine the determinants of aggregate illiquidity in the U.S. stock market. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/155089
