Three Essays in Econometrics and Financial Economics PDF Download
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Author: Xiaokang Zhu
Publisher:
ISBN:
Category : Economics
Languages : en
Pages : 422
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Book Description
Author: Xiaokang Zhu
Publisher:
ISBN:
Category : Economics
Languages : en
Pages : 422
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Author: Jiang Liang
Publisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 0
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"This dissertation develops several econometric techniques to address three issues in financial economics, namely, constructing a real estate price index, estimating structural break points, and estimating integrated variance in the presence of market microstructure noise and the corresponding microstructure noise function. Chapter 2 develops a new methodology for constructing a real estate price index that utilizes all transaction price information, encompassing both single-sales and repeat-sales. The method is less susceptible to specification error than standard hedonic methods and is not subject to the sample selection bias involved in indexes that rely only on repeat sales. The methodology employs a model design that uses a sale pairing process based on the individual building level, rather than the individual house level as is used in the repeat-sales method. The approach extends ideas from repeat-sales methodology in a way that accommodates much wider datasets. In an empirical analysis of the methodology, we fit the model to the private residential property market in Singapore between Q1 1995 and Q2 2014, covering several periods of major price fluctuation and changes in government macroprudential policy ..."--Author's abstract.
Author: Jianxun Li
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Biplab K. Ghosh
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Category :
Languages : en
Pages : 0
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Author: Jose Vicente Martinez
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Category :
Languages : en
Pages : 294
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Author: Andrey Dmitrievich Ukhov
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Category :
Languages : en
Pages : 0
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Author: Rajiv Sobti
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ISBN:
Category :
Languages : en
Pages : 190
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Author: Adrien Alvero
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ISBN:
Category :
Languages : en
Pages :
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In the third chapter, "Fuzzy Bunching", we introduce a new fuzzy bunching approach that is robust to noise. The existing bunching approach identifies the extent of bunching from a sharp spike in the probability density function. In many finance settings, however, the sharp spike could be diffused by data noise. The key idea behind our fuzzy bunching estimator is to identify bunching from the area of a bulge in the cumulative distribution function. The fuzzy bunching approach also avoids density estimation, which makes it easy to implement in sparse data. We provide the theoretical foundation of this approach and illustrate the advantages by using simulated and real data.
Author: Canlin Li
Publisher:
ISBN:
Category :
Languages : en
Pages : 143
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Author: Kap-Soo Oh
Publisher:
ISBN:
Category :
Languages : en
Pages : 164
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