GLS Detrending, Efficient Unit Root Tests and Structural Change PDF Download
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Author: Perron, Pierre
Publisher: Montréal : Université de Montréal, Dép. de sciences économiques
ISBN: 9782893823751
Category :
Languages : en
Pages : 45
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Book Description
Author: Perron, Pierre
Publisher: Montréal : Université de Montréal, Dép. de sciences économiques
ISBN: 9782893823751
Category :
Languages : en
Pages : 45
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Book Description
Author: Jaeyoun Hwang
Publisher:
ISBN:
Category : Time-series analysis
Languages : en
Pages : 256
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Book Description
Author: Pierre Perron
Publisher: MDPI
ISBN: 3038428116
Category : Business & Economics
Languages : en
Pages : 167
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Book Description
This book is a printed edition of the Special Issue "Unit Roots and Structural Breaks" that was published in Econometrics
Author: G. Kapetanios
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: G. S. Maddala
Publisher: Cambridge University Press
ISBN: 9780521587822
Category : Business & Economics
Languages : en
Pages : 528
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Book Description
A comprehensive review of unit roots, cointegration and structural change from a best-selling author.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: K. Patterson
Publisher: Springer
ISBN: 1137003316
Category : Business & Economics
Languages : en
Pages : 586
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Book Description
Testing for a Unit Root is now an essential part of time series analysis but the literature on the topic is so large that knowing where to start is difficult even for the specialist. This book provides a way into the techniques of unit root testing, explaining the pitfalls and nonstandard cases, using practical examples and simulation analysis.
Author: Joakim Westerlund
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
First-differencing is generally taken to imply the loss of one observation, the first, or at least that the effect of ignoring this observation is asymptotically negligible. However, this is not always true, as in the case of generalized least squares (GLS) detrending. In order to illustrate this, the current article considers as an example the use of GLS detrended data when testing for a unit root. The results show that the treatment of the first observation is absolutely crucial for test performance, and that ignorance causes test break-down.
Author: Mehmet Balcilar
Publisher:
ISBN:
Category :
Languages : en
Pages : 286
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Book Description
Author: Vance Martin
Publisher: Cambridge University Press
ISBN: 0521139813
Category : Business & Economics
Languages : en
Pages : 925
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Book Description
"Maximum likelihood estimation is a general method for estimating the parameters of econometric models from observed data. The principle of maximum likelihood plays a central role in the exposition of this book, since a number of estimators used in econometrics can be derived within this framework. Examples include ordinary least squares, generalized least squares and full-information maximum likelihood. In deriving the maximum likelihood estimator, a key concept is the joint probability density function (pdf) of the observed random variables, yt. Maximum likelihood estimation requires that the following conditions are satisfied. (1) The form of the joint pdf of yt is known. (2) The specification of the moments of the joint pdf are known. (3) The joint pdf can be evaluated for all values of the parameters, 9. Parts ONE and TWO of this book deal with models in which all these conditions are satisfied. Part THREE investigates models in which these conditions are not satisfied and considers four important cases. First, if the distribution of yt is misspecified, resulting in both conditions 1 and 2 being violated, estimation is by quasi-maximum likelihood (Chapter 9). Second, if condition 1 is not satisfied, a generalized method of moments estimator (Chapter 10) is required. Third, if condition 2 is not satisfied, estimation relies on nonparametric methods (Chapter 11). Fourth, if condition 3 is violated, simulation-based estimation methods are used (Chapter 12). 1.2 Motivating Examples To highlight the role of probability distributions in maximum likelihood estimation, this section emphasizes the link between observed sample data and 4 The Maximum Likelihood Principle the probability distribution from which they are drawn"-- publisher.
