Econometrics of first-price auctions with entry and binding reservation prices

Li, Tong

doi:10.1016/j.jeconom.2004.06.002

Cited by 43 publications

(2 citation statements)

References 28 publications

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“…If you want to build the model, you should first ensure the value p in Eq.3. Every variable has the same value p which can be got by statistics-( ) AIC p and ( ) BIC p [10]: reach their minimums at the same time, the p can be ensured [11] . The choice of explanatory variables.…”

Section: Adl Modelmentioning

confidence: 99%

Application of Autoregressive Distributed Lag (ADL) Model to Thermal Error Modeling on NC Machine Tools

Miao¹,

Wang

Fei

et al. 2011

AMM

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Thermal error modeling method is an important field of thermal error compensation on NC machine tools, it is also a key for improving the machining accuracy of machine tools. The accuracy of the model directly affects the quality of thermal error compensation. On the basis of multiple linear regression (MLR) model, this paper proposes an autoregressive distributed lag (ADL) model of thermal error and establishes an accurate ADL model by stepwise regression analysis. The ADL model of thermal error is established with measured data, it proved the ADL model is available and has a high accuracy on predicting thermal error by comparing with MLR models.

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Section: Adl Modelmentioning

confidence: 99%

Application of Autoregressive Distributed Lag (ADL) Model to Thermal Error Modeling on NC Machine Tools

Miao¹,

Wang

Fei

et al. 2011

AMM

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“…See, for example, Guerre et al (2000Guerre et al ( , 2009 and Li and Liu (2022) for such identification results; and Liu and Vuong (2021) and Liu and Luo (2017) for testing the restrictions of exogenous participation and monotone strategies in auctions. 2 Under selective entry, Li (2005) introduced a method of simulated moments estimator to estimate the value distribution parametrically using the known bids and number of bidders. Marmer et al (2013) proposed a general framework of the AS model to characterize selective entry in auctions and designed a nonparametric test to discriminate its two polar cases given by Levin and Smith (1994) and Samuelson (1985), respectively, from the general model.…”

Section: Introductionmentioning

confidence: 99%

Nonparametric estimation of English auctions with selective entry: An application to online judicial auctions

Liu,

2023

International Studies of Economics

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This paper proposes an estimation approach following the constructive identification strategy of Athey and Haile, and Gentry and Li with adaption in the context of ascending auctions with selective entry. Our estimators are shown to be consistent in a large sample and to perform well in a finite sample by a simulation study. We apply our estimation approach to the Alibaba online judicial auctions of used cars to recover the bounds of conditional value distribution and the entry cost. The bounds estimates of both conditional value distribution and entry cost are quite tight (resp., relatively wide) for middle‐valued (resp., low‐valued or high‐valued) signal, and the cumulative distribution functions of conditional value distribution given signal comply with the law of ordered dominance. Finally, our counterfactual analysis indicates that (i) the ascending auction yields a higher revenue than the first‐price sealed bid auction, and (ii) the revenue can be improved significantly when the entry cost is cut by half.

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Harry J. Paarsch and Han Hong, An Introduction to the Structural Econometrics of Auction Data (The MIT Press 2006)

Perrigne

2010

J of Applied Econometrics

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Econometrics of first-price auctions with entry and binding reservation prices

Cited by 43 publications

References 28 publications

Application of Autoregressive Distributed Lag (ADL) Model to Thermal Error Modeling on NC Machine Tools

Application of Autoregressive Distributed Lag (ADL) Model to Thermal Error Modeling on NC Machine Tools

Nonparametric estimation of English auctions with selective entry: An application to online judicial auctions

Harry J. Paarsch and Han Hong, An Introduction to the Structural Econometrics of Auction Data (The MIT Press 2006)

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