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Author: Jabonn Kim
Publisher:
ISBN:
Category : Equations, Simultaneous
Languages : en
Pages : 168
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Author: Jabonn Kim
Publisher:
ISBN:
Category : Equations, Simultaneous
Languages : en
Pages : 168
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Book Description
Author: Donald W. K. Andrews
Publisher: Cambridge University Press
ISBN: 1139444603
Category : Business & Economics
Languages : en
Pages : 589
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Book Description
This 2005 volume contains the papers presented in honor of the lifelong achievements of Thomas J. Rothenberg on the occasion of his retirement. The authors of the chapters include many of the leading econometricians of our day, and the chapters address topics of current research significance in econometric theory. The chapters cover four themes: identification and efficient estimation in econometrics, asymptotic approximations to the distributions of econometric estimators and tests, inference involving potentially nonstationary time series, such as processes that might have a unit autoregressive root, and nonparametric and semiparametric inference. Several of the chapters provide overviews and treatments of basic conceptual issues, while others advance our understanding of the properties of existing econometric procedures and/or propose others. Specific topics include identification in nonlinear models, inference with weak instruments, tests for nonstationary in time series and panel data, generalized empirical likelihood estimation, and the bootstrap.
Author: D. S. Poskitt
Publisher:
ISBN: 9781601986948
Category : Business & Economics
Languages : en
Pages : 0
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Book Description
Inference in the Presence of Weak Instruments is concerned with inference in the linear simultaneous equations model. The ideas developed for this model have remained central to econometric practice, with the use of instrumental variables estimation having served as a unifying paradigm in econometrics for decades. The literature could be viewed as belonging to one of two strands, either large-sample asymptotic or finite-sample analysis. Of these two strands, the former matured more quickly and has had far greater impact on empirical practice than the latter. In contrast, the finite-sample literature took some twenty years longer to develop, by which time empirical practice was largely entrenched. The consensus view was that the asymptotic results are considerably simpler to interpret than the exact results that are obtained, and are notionally more general as they are predicated on weaker distributional assumptions. Towards the end of the 1980s, both strands of the literature focused attention on models that were either unidentified or close to unidentified. First, there was a growing understanding of the empirical consequences of using weak instruments. Second, the finite-sample results developed throughout the 1980s invariably involved multiple infinite series of invariant polynomials of matrix argument which were typically not very revealing. Consequently, simplifying special cases were explored to illustrate the results contained within the more general expressions. It was observed that the leading terms of these series expansions corresponded to totally unidentified models, and therefore the analyses of these models became a commonly used expository device in this literature. These totally unidentified models can be thought of as limiting cases of weak instruments. Finally, it was becoming clear that the existing large-sample asymptotic results were providing very poor approximations to the true sampling behavior of various statistical procedures. More recently, the literature has been devoted to analyzing potential remedies to the problem of weak instruments Inference in the Presence of Weak Instruments presents a selected survey that examines this growing literature into issues of estimation, hypothesis testing, and confidence interval construction. This survey indicates some of the links between the different traditions by using the small concentration results from an earlier publication of the authors. These results can be used to characterize various special cases when instruments are weak.
Author: Badi H. Baltagi
Publisher: Springer Nature
ISBN: 3030801497
Category : Business & Economics
Languages : en
Pages : 496
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Book Description
This textbook teaches some of the basic econometric methods and the underlying assumptions behind them. It also includes a simple and concise treatment of more advanced topics in spatial correlation, panel data, limited dependent variables, regression diagnostics, specification testing and time series analysis. Each chapter has a set of theoretical exercises as well as empirical illustrations using real economic applications. These empirical exercises usually replicate a published article using Stata, Eviews as well as SAS. This new sixth edition has been fully revised and updated, and includes new material on limited dependent variables and panel data as well as revision of basic topics like heteroskedasticity, endogeneity, over-identification and specification testing. The author also provides more exercises and empirical examples based on published economic applications.
Author: Haitian Xie
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
This dissertation is a collection of three essays on the econometric analysis of causal inference methods. Chapter 1 examines the identification and estimation of the structural function in fuzzy RD designs with a continuous treatment variable. We show that the nonlinear and nonseparable structural function can be nonparametrically identified at the RD cutoff under shape restrictions, including monotonicity and smoothness conditions. Based on the nonparametric identification equation, we propose a three-step semiparametric estimation procedure and establish the asymptotic normality of the estimator. The semiparametric estimator achieves the same convergence rate as in the case of a binary treatment variable. As an application of the method, we estimate the causal effect of sleep time on health status by using the discontinuity in natural light timing at time zone boundaries. Chapter 2 examines the local linear regression (LLR) estimate of the conditional distribution function F(y|x). We derive three uniform convergence results: the uniform bias expansion, the uniform convergence rate, and the uniform asymptotic linear representation. The uniformity in the above results is with respect to both x and y and therefore has not previously been addressed in the literature on local polynomial regression. Such uniform convergence results are especially useful when the conditional distribution estimator is the first stage of a semiparametric estimator. Chapter 3 studies the estimation of causal parameters in the generalized local average treatment effect model, a generalization of the classical LATE model encompassing multi-valued treatment and instrument. We derive the efficient influence function (EIF) and the semiparametric efficiency bound for two types of parameters: local average structural function (LASF) and local average structural function for the treated (LASF-T). The moment condition generated by the EIF satisfies two robustness properties: double robustness and Neyman orthogonality. Based on the robust moment condition, we propose the double/debiased machine learning (DML) estimators for LASF and LASF-T. We also propose null-restricted inference methods that are robust against weak identification issues. As an empirical application, we study the effects across different sources of health insurance by applying the developed methods to the Oregon Health Insurance Experiment.
Author: Xin Liang
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
"This Ph.D. thesis consists of three essays on identification theory in econometrics. In view of achieving reliable inference methods when some parameters are not identifiable (or weakly identifiable), we establish necessary and sufficient conditions for identification of linear and nonlinear parameter transformations, when the full parameter vector is not identifiable. The first essay considers a class of generalized linear models (deemed "partially linear models") where parameters of interest determine the distribution of the data through multiplication by a known matrix. This setup not only covers linear regression models with collinearity (such as cases where the number of explanatory variables is potentially very large or the number observations is inferior to the number of variables) and a general error covariance matrix, but a wide spectrum of other models used in econometrics, such as linear median regressions and quantile regressions, generalized linear mixed models, probit and Tobit models, multinomial logit models and other discrete choice models, exponential models, index models, etc. We first provide a general necessary and sufficient condition for the global identification of a general transformation of model parameters (when the full parameter vector is not typically identified) based on a new separability condition. The general result is then applied to partially linear models. Even though none of the original individual parameters of the model may be identified, we describe the class of linear transformations which can be identified. To get usable conditions, different equivalent characterizations are derived. The effect of adding restrictions is also considered, and the corresponding identification conditions are supplied.The second essay reconsiders the problem of characterizing identifiable parameters in linear IV regressions and simultaneous equations models (SEMs), using methods based on the first essay. The recent econometric literature on weak instruments mainly deals with this basic setup, and the appropriate statistical methods depend on whether the parameters of interest are identifiable. We study the general case where some model parameters are not identifiable, without any restriction on the rank of the instrument matrix, and we characterize which linear transformations of the structural parameters are identifiable. An important observation is that identifiable parameters may depend on the instrument matrix (in addition to the parameters of the reduced form), and a number of alternative characterizations are provided. These results are also applicable to partially linear IV-type models where the linear IV structure is embedded in a nonlinear structure, such as a quantile specification or a discrete choice model.The third essay takes up the problem of characterizing the identification of nonlinear functions of parameters in nonlinear models. The setup is fundamentally semiparametric, and the basic assumption is that structural parameters of interest determine a number of identifiable parameters through a nonlinear equation. Again, we consider the general case where not all model parameters are identifiable, with the purpose of characterizing nonlinear parameter transformations which are identifiable. The literature on this problem is thin, and focuses on the identification of the full parameter vector in the equation of interest. In view of the fact global identification is extremely difficult to achieve, this paper looks at the problem from a local identification viewpoint. Both sufficient conditions, as well as necessary and sufficient conditions are derived under assumptions of differentiability of the relevant moment equations and parameter transformations. Some classical results on identification in likelihood models are also derived and extended. Finally, the results are applied to identification problems in DSGE models." --
Author: Donald W. K. Andrews
Publisher:
ISBN: 9780511122125
Category : Econometric models
Languages : en
Pages : 573
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Author: Byunguk Kang
Publisher:
ISBN:
Category :
Languages : en
Pages :
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"This thesis contributes to finite-sample inference in econometrics. The first two essays develop identification-robust (IR) inference in dynamic structural models and measurement error models.The third essay extends the standard finite-sample distributional theory of test statistics in univariateand multivariate regression settings. The first essay considers dynamic structural models involving endogeneity and a lagged dependent variable. We start by observing that usual IR tests, such as Anderson and Rubin's (1949) test (AR), Kleibergen's (2002) Lagrange multiplier test (KLM), and Moreira's (2003) conditional likelihood ratio test (CLR), are unreliable when model variables are nonstationary or nearly nonstationary. We propose IR methods which are also robust to nonstationarity: two Anderson-Rubin type procedures and two split-sample procedures. Our procedures are also robust to missing instruments. For distributional theory, three different sets of assumptions are considered. First, on assuming Gaussian structural errors, we show that three of the proposed statistics follow the standard F distribution. Second, for more general cases, we assume that the distribution of errors is completely specified up to an unknown scale factor, allowing the Monte Carlo test method to be applied. This assumption enables one to deal with non-Gaussian error distributions. For example, even when errors follow heavy-tailed distribution, such as the Cauchy distribution or more generally the family of stable distributions - which may not have moments and thus make inference difficult - our procedures provide simple and exact solutions. Third, we establish the asymptotic validity of our procedures under quite general distributional assumptions. We present simulation results showing that our procedures control their level correctly and have good power properties. The methods are applied to an empirical example, the New Keynesian Phillips curve, in which both weak identification and nonstationarity present challenges. The results of this empirical study suggest forward-looking behavior of U.S. inflation. The second essay deals with measurement error models. In econometrics, measurement error problems are often interpreted as a special case of simultaneity, so instrumental variables (IVs) methods are widely used as solutions. The validity and the power of IV-based tests are sensitive to the quality of IVs. First, if the exogeneity of IVs is violated, test levels may not be controlled. Second, when IVs are weakly correlated with the mismeasured variables, the IR procedures guarantee correct level but power of the procedures may be arbitrarily low. To overcome these problems, we introduce an IV-free inference which exploits orthogonality properties between transformations of model variables: the "Reverse Anderson-Rubin" (RAR) method with both weak and strong instruments. When valid and informative IVs are available, the RAR procedure can be combined with the usual AR method, so the two approaches complement each to improve power properties. We call the hybrid procedure the "Combined RAR" (CRAR) method. In particular, this procedure can have power even when the instruments used do not allow one to identify model coefficients (totally weak instruments). After studying classical measurement error models - where measurement errors are independent of other model disturbances - we extend the proposed procedures to situations where measurement errors may be correlated with other model disturbances. Under a Gaussian distributional assumption, we show the proposed test statistics are pivotal or follow distributions which can be bounded in finite samples. Under more general assumptions, we establish their asymptotic validity. In a simulation study, we show that the new methods provide power improvements over standard IR procedures. " --
Author: R. Carter Hill
Publisher: John Wiley & Sons
ISBN: 1118452275
Category : Business & Economics
Languages : en
Pages : 1808
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Book Description
Principles of Econometrics, Fifth Edition, is an introductory book for undergraduate students in economics and finance, as well as first-year graduate students in a variety of fields that include economics, finance, accounting, marketing, public policy, sociology, law, and political science. Students will gain a working knowledge of basic econometrics so they can apply modeling, estimation, inference, and forecasting techniques when working with real-world economic problems. Readers will also gain an understanding of econometrics that allows them to critically evaluate the results of others’ economic research and modeling, and that will serve as a foundation for further study of the field. This new edition of the highly-regarded econometrics text includes major revisions that both reorganize the content and present students with plentiful opportunities to practice what they have read in the form of chapter-end exercises.
Author: Walter Katzenbeisser
Publisher:
ISBN:
Category : Econometric models
Languages : en
Pages : 146
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