Estimation for Single-index and Partially Linear Single-index Nonstationary Time Series Models PDF Download
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Author: Chaohua Dong
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Chaohua Dong
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Yingcun Xia
Publisher:
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Category :
Languages : en
Pages :
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Author: Yichen Gao
Publisher:
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Category :
Languages : en
Pages :
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Author: Yoosoon Chang
Publisher: Emerald Group Publishing
ISBN: 1837532087
Category : Business & Economics
Languages : en
Pages : 406
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Book Description
Volumes 45a and 45b of Advances in Econometrics honor Professor Joon Y. Park, who has made numerous and substantive contributions to the field of econometrics over a career spanning four decades since the 1980s and counting.
Author: Jiti Gao
Publisher: CRC Press
ISBN: 1420011219
Category : Mathematics
Languages : en
Pages : 249
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Useful in the theoretical and empirical analysis of nonlinear time series data, semiparametric methods have received extensive attention in the economics and statistics communities over the past twenty years. Recent studies show that semiparametric methods and models may be applied to solve dimensionality reduction problems arising from using fully
Author: Jia Chen
Publisher:
ISBN:
Category : Econometrics
Languages : en
Pages :
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In this paper, we study semiparametric estimation for a single index panel data model where the nonlinear link function varies among the individuals. We propose using the so called refined minimum average variance estimation based on a local linear smoothing method to estimate both the parameters in the single index and the average link function. As the cross section dimension N and the time series dimension T tend to infinity simultaneously, we establish asymptotic distributions for the proposed parametric and nonparametric estimates. In addition, we provide two real data examples to illustrate the finite sample behavior of the proposed estimation method in this paper.
Author: Bruce Hansen
Publisher: Princeton University Press
ISBN: 0691236151
Category : Business & Economics
Languages : en
Pages : 1081
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The most authoritative and up-to-date core econometrics textbook available Econometrics is the quantitative language of economic theory, analysis, and empirical work, and it has become a cornerstone of graduate economics programs. Econometrics provides graduate and PhD students with an essential introduction to this foundational subject in economics and serves as an invaluable reference for researchers and practitioners. This comprehensive textbook teaches fundamental concepts, emphasizes modern, real-world applications, and gives students an intuitive understanding of econometrics. Covers the full breadth of econometric theory and methods with mathematical rigor while emphasizing intuitive explanations that are accessible to students of all backgrounds Draws on integrated, research-level datasets, provided on an accompanying website Discusses linear econometrics, time series, panel data, nonparametric methods, nonlinear econometric models, and modern machine learning Features hundreds of exercises that enable students to learn by doing Includes in-depth appendices on matrix algebra and useful inequalities and a wealth of real-world examples Can serve as a core textbook for a first-year PhD course in econometrics and as a follow-up to Bruce E. Hansen’s Probability and Statistics for Economists
Author: Jia Chen
Publisher:
ISBN:
Category : Asymptotic distribution (Probability theory)
Languages : en
Pages :
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Estimation theory in a nonstationary environment has been very popular in recent years. Existing studies focus on nonstationarity in parametric linear, parametric nonlinear and nonparametric nonlinear models. In this paper, we consider a partially linear model of the form Yt = X t +g(Vt)+ t, t = 1, · · ·, n, where {Vt} is a sequence of -null recurrent Markov chains, {Xt} is a sequence of either strictly stationary or nonstationary regressors and { t} is a stationary sequence. We propose to estimate both a and g(·) semiparametrically. We then show that the proposed estimator of is still asymptotically normal with the same rate as for the case of stationary time series. We also establish the asymptotic normality for the nonparametric estimator of the function g(·) and the uniform consistency of the nonparametric estimator. The simulated example is given to show that our theory and method work well in practice.
Author: Chaohua Dong
Publisher:
ISBN:
Category :
Languages : en
Pages : 40
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In this paper, a semiparametric single-index model is investigated. The link function is allowed to be unbounded and has unbounded support that answers a pending issue in the literature. Meanwhile, the link function is treated as a point in an infinitely many dimensional function space which enables us to derive the estimates for the index parameter and the link function simultaneously. This approach is different from the profile method commonly used in the literature. The estimator is derived from an optimization with the constraint of identification condition for index parameter, which is a natural way but ignored in the literature.
Author: Hidehiko Ichimura
Publisher:
ISBN:
Category :
Languages : en
Pages : 86
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