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Author: Youngki Shin
Publisher:
ISBN:
Category : Econometric models
Languages : en
Pages : 232
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Author: Youngki Shin
Publisher:
ISBN:
Category : Econometric models
Languages : en
Pages : 232
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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: Marc Nerlove
Publisher: Cambridge University Press
ISBN: 9780521022460
Category : Business & Economics
Languages : en
Pages : 388
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Book Description
This volume collects seven classic essays on panel data econometrics, and a cogent essay on the history of the subject.
Author: Yinchu Zhu
Publisher:
ISBN:
Category :
Languages : en
Pages : 263
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Book Description
Economic modeling in a data-rich environment is often challenging. To allow for enough flexibility and to model heterogeneity, models might have parameters with dimensionality growing with (or even much larger than) the sample size of the data. Learning these high-dimensional parameters requires new methodologies and theories. We consider three important high-dimensional models and propose novel methods for estimation and inference. Empirical applications in economics and finance are also studied. In Chapter 1, we consider high-dimensional panel data models (large cross sections and long time horizons) with interactive fixed effects and allow the covariate/slope coefficients to vary over time without any restrictions. The parameter of interest is the vector that contains all the covariate effects across time. This vector has dimensionality tending to infinity, potentially much faster than the cross-sectional sample size. We develop methods for the estimation and inference of this high-dimensional vector, i.e., the entire trajectory of time variation in covariate effects. We show that both the consistency of our estimator and the asymptotic accuracy of the proposed inference procedure hold uniformly in time. Our methodology can be applied to several important issues in econometrics, such as constructing confidence bands for the entire path of covariate coefficients across time, testing the time-invariance of slope coefficients and estimation and inference of patterns of time variations, including structural breaks and regime switching. An important feature of our method is that it provides inference procedures for the time variation in pre-specified components of slope coefficients while allowing for arbitrary time variation in other components. Computationally, our procedures do not require any numerical optimization and are very simple to implement. Monte Carlo simulations demonstrate favorable properties of our methods in finite samples. We illustrate our methods through empirical applications in finance and economics. In Chapter 2, we consider large factor models with unobserved factors. We formalize the notion of common factors between different groups of variables and propose to use it as a general approach to study the structure of factors, i.e., which factors drive which variables. The spanning hypothesis, which states that factors driving one group are spanned by those driving another group, can be studied as a special case under our framework. We develop a statistical procedure for testing the number of common factors. Our inference procedure is built upon recent results on high-dimensional bootstrap and is shown to be valid under the asymptotic framework of large $n$ and large $T$. In Monte Carlo simulations, our procedure performs well in finite samples. As an empirical application, we construct confidence sets for the number of common factors between the macroeconomy and the financial markets. Chapter 3 is joint work with Jelena Bradic. We propose a methodology for testing linear hypothesis in high-dimensional linear models. The proposed test does not impose any restriction on the size of the model, i.e. model sparsity or the loading vector representing the hypothesis. Providing asymptotically valid methods for testing general linear functions of the regression parameters in high-dimensions is extremely challenging--especially without making restrictive or unverifiable assumptions on the number of non-zero elements. We propose to test the moment conditions related to the newly designed restructured regression, where the inputs are transformed and augmented features. These new features incorporate the structure of the null hypothesis directly. The test statistics are constructed in such a way that lack of sparsity in the original model parameter does not present a problem for the theoretical justification of our procedures. We establish asymptotically exact control on Type I error without imposing any sparsity assumptions on model parameter or the vector representing the linear hypothesis. Our method is also shown to achieve certain optimality in detecting deviations from the null hypothesis. We demonstrate the favorable finite-sample performance of the proposed methods, via a number of numerical and a real data example.
Author: Thomas B. Fomby
Publisher: Emerald Group Publishing
ISBN: 1784411825
Category : Political Science
Languages : en
Pages : 772
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Book Description
This volume honors Professor Peter C.B. Phillips' many contributions to the field of econometrics. The topics include non-stationary time series, panel models, financial econometrics, predictive tests, IV estimation and inference, difference-in-difference regressions, stochastic dominance techniques, and information matrix testing.
Author: Moyu Liao
Publisher:
ISBN:
Category :
Languages : en
Pages :
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I study identification, estimation, and hypothesis testing in complete and incomplete economic models with testable assumptions. Testable assumptions ($A$) give strong and interpretable empirical content to the model but they also carry the possibility that some distribution of observed outcomes may reject these assumptions. A natural way to avoid this is to find a set of relaxed assumptions ($\tilde{A}$) that cannot be rejected by any distribution of observed outcomes and such that the identified set for the parameter of interest is not changed when the original assumption holds. The main contribution of this thesis is to characterize the properties of such a relaxed assumption $\tilde{A}$ using notions of refutability and confirmability. In Chapter 1, I establish the theoretical framework for analyzing econometric structures and econometric assumptions. This framework unifies the theory of identification of complete economic structures and the theory of refutability. I propose a general method to construct such $\tilde{A}$. A general estimation and inference procedure is proposed and can be applied to a large class of incomplete economic models. I apply my methodology to the instrument monotonicity assumption in Local Average Treatment Effect (LATE) estimation and to the sector selection assumption in a binary outcome Roy model of employment sector choice. In the LATE application, I use my general method to construct a set of relaxed assumptions $\tilde{A}$ that can never be rejected, and the identified set for LATE is unchanged when $A$ holds. LATE is point identified under my extension $\tilde{A}$ in the application. I also provide an estimation and inference method on the LATE value. In Chapter 2, I generalize the framework to incomplete economic structures. I show that the general method for constructing a relaxed assumption in Chapter 1 may fail to work in incomplete economic structures. Therefore, I propose a completion procedure that is without loss of generality. With this completion procedure, we can get completed economic structures, and the method in Chapter 1 can be applied. I then look at the application to a binary outcome Roy model. I use my method to relax Roy's sector selection assumption and characterize the identified set for the binary potential outcomes as a polyhedron. In Chapter 3, I propose a dilation estimation and inference method that can be applied to a wide class of complete and incomplete economic structures. My method can easily deal with an observed variable that is of dimension greater than two.
Author: Karun Adusumilli
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Jingjing Yang
Publisher:
ISBN: 9781124380599
Category : Electronic dissertations
Languages : en
Pages : 141
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Author: Halbert White
Publisher: Cambridge University Press
ISBN: 9780521574464
Category : Business & Economics
Languages : en
Pages : 396
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This book examines the consequences of misspecifications for the interpretation of likelihood-based methods of statistical estimation and interference. The analysis concludes with an examination of methods by which the possibility of misspecification can be empirically investigated.
Author: Ekaterini Kyriazidou
Publisher:
ISBN:
Category :
Languages : en
Pages :
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