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230120 ||| eng |
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|a 9781789739596
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|a 9781789739572
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| 050 |
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4 |
|a HB139
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| 100 |
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|a Terrell, Dek
|e [editor]
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| 245 |
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|a Essays in honor of Cheng Hsiao
|h Elektronische Ressource
|c edited by Dek Terrell (Louisiana State University, USA), Tong Li (Vanderbilt University, USA) and M. Hashem Pesaran (University of Southern California, USA)
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| 260 |
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|a Bingley, U.K.
|b Emerald Publishing Limited
|c 2020
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| 300 |
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|a 472 pages
|c cm
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| 505 |
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|a Chapter 7: Growth empirics: a Bayesian semiparametric model with random coefficients for a panel of OECD Countries / Badi H. Baltagi, Georges Bresson and Jean-Michel Etienne -- Chapter 8: Robust estimation and inference for importance sampling estimators with infinite variance / Joshua Chan, Chenghan Hou and Thomas Tao Yang -- Chapter 9: Econometrics of scoring auctions / Jean-Jacques Laffont, Isabelle Perrigne, Michel Simioni, and Quang Vuong -- Chapter 10: Bayesian estimation of linear sum assignment problems / Yu-Wei Hsieh and Matthew Shum -- Chapter 11: The mode is the message: using predata as exclusion restrictions to evaluate survey design / Heng Chen, Geoffrey Dunbar, and Q. Rallye Shen -- Chapter 12: Estimating peer effects on career choice: a spatial multinomial logit approach / Bolun Li, Robin Sickles and Jenny Williams --
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| 505 |
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|a Chapter 13: Mortgage portfolio diversification in the presence of cross-sectional and spatial dependence / Timothy Dombrowski, R. Kelley Pace and Rajesh Narayanan -- Chapter 14: An econometrician's perspective on big data / Cheng Hsiao Hsiao -- Chapter 15: Comments on 'an econometrician's perspective on big data' / Thomas Fomby -- Chapter 16: Comments on 'An Econometrician's Perspective on Big Data' by Cheng Hsiao / Georges Bresson
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| 505 |
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|a Introduction / Dek Terrell, Tong Li and M. Hashem Pesaran -- Chapter 1: Correction for the asymptotical bias of the Arellano-Bond type GMM estimation of dynamic panel models / Yonghui Zhang and Qiankun Zhou -- Chapter 2: Testing convergence using HAR inference / Peter Phillips, Jianning Kong and Donggyu Sul -- Chapter 3: Bayesian estimation of linear sum assignment problems / Yubo Tao and Jun Yu -- Chapter 4: A VAR approach to forecasting multivariate long memory processes subject to structural breaks / Cindy S.H. Wang and Shui Ki Wan -- Chapter 5: identifying global and national output and fiscal policy shocks using a GVAR / Alexander Chudik, M. Hashem Pesaran and Kamiar Mohaddes -- Chapter 6: The determinents of health care expenditure and trends: a semiparametric panal data analysis of OECD countries / Ming Kong, Jiti Gao and Xueyan Zhao --
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| 505 |
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|a Includes bibliographical references
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| 653 |
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|a Business & Economics / Econometrics / bisacsh
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| 653 |
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|a Econometrics / bicssc
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| 653 |
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|a Econometrics
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| 700 |
1 |
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|a Li, Tong
|e [editor]
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| 700 |
1 |
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|a Pesaran, M. Hashem
|e [editor]
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| 700 |
1 |
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|a Hsiao, Cheng
|e [honoree]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b EMBAR
|a Emerald Business, Management and Economics eBook Collection Archive
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| 490 |
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|a Advances in econometrics
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| 500 |
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|a Includes index
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| 856 |
4 |
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|u https://doi.org/10.1108/s0731-9053202041
|x Verlag
|3 Volltext
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| 082 |
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|a 330.015195
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| 520 |
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|a Including contributions spanning a variety of theoretical and applied topics in econometrics, this volume of Advances in Econometrics is published in honour of Cheng Hsiao. In the first few chapters of this book, new theoretical panel and time series results are presented, exploring JIVE estimators, HAC, HAR and various sandwich estimators, as well as asymptotic distributions for using information criteria to distinguish between the unit root model and explosive models. Other chapters address topics such as structural breaks or growth empirics; auction models; and semiparametric methods testing for common vs. individual trends. Three chapters provide novel empirical approaches to applied problems, such as estimating the impact of survey mode on responses, or investigating how cross-sectional and spatial dependence of mortgages varies by default rates and geography. In the final chapters, Cheng Hsiao offers a forward-focused discussion of the role of big data in economics. For any researcher of econometrics, this is an unmissable volume of the most current and engaging research in the field
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