Asymptotic Bayesian analysis based on a limited information estimator

Kwan, Yum K.

doi:10.1016/s0304-4076(98)00024-4

Cited by 32 publications

(25 citation statements)

References 28 publications

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“…Proofs of these results, with careful specification Ž . of regularity conditions, are in Kwan 1998 . The mappings from A coefficients to C coefficients can be analytically j j differentiated, so that normal asymptotic distributions for the A coefficients can be translated into normal asymptotic distributions for the C coefficients. Details Ž .…”

Section: Asymptotics Approximation the Bootstrapmentioning

confidence: 99%

Error Bands for Impulse Responses

1999

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may

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Section: Asymptotics Approximation the Bootstrapmentioning

confidence: 99%

Error Bands for Impulse Responses

1999

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“…On the other hand, a simulation-oriented Bayesian approach would have been relatively straightforward if the posterior distribution of the underlying parameters were easy to sample from. We apply the asymptotic theory in Kwan (1999) to interpret the asymptotic normal distribution of the GMM estimator as an approximate posterior distribution, which in turn allows us to use simulation method to compute the posterior distribution of various impact measures and their spatial partitioning. More specifically, a random draw from the approximate posterior distribution of the parameter vector ( , , , )…”

Section: Resultsmentioning

confidence: 99%

FDI technology spillovers, geography, and spatial diffusion

Lin

Kwan

2016

International Review of Economics & Finance

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This paper investigates the geographic extent of FDI technology spillovers and associated spatial diffusion. By adopting a spatiotemporal autoregressive panel model as the platform of our study, the complex impact resulting from FDI penetration is separated into spatial direct and indirect effect while accounting for feedback loops among regions. A set of spatially partitioned summary measures is produced to identify and to quantify FDI spillovers from different channels with distinct geographic scopes. Empirical results based on data from China document that the direct impacts of FDI presence to a specific location itself are likely to be negative. Domestic firms mainly benefit from FDI presence in their neighboring regions through knowledge spillovers that have wider geographic scope. Negative market stealing effect nevertheless has no spatial boundary. Policy implications of these findings are discussed.

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“…In a series of papers Gallant and Hong (2007), Gallant et al (2014) and Gallant (2015) developed methods which devise a likelihood by using fiducial arguments from moment conditions. Related work includes Jaynes (2003) and Kwan (1998). Florens and Simoni (2015) have used Gaussian processes in combination with moment constraints to carry out Bayesian inference.…”

Section: Literature On Bayesian Analysis Of Momentsmentioning

confidence: 99%

Moment Conditions and Bayesian Non-Parametrics

Bornn

Shephard

Solgi

2018

Journal of the Royal Statistical Society Series B: Statistical Methodology

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Summary Models phrased through moment conditions are central to much of modern inference. Here these moment conditions are embedded within a non‐parametric Bayesian set‐up. Handling such a model is not probabilistically straightforward as the posterior has support on a manifold. We solve the relevant issues, building new probability and computational tools by using Hausdorff measures to analyse them on real and simulated data. These new methods, which involve simulating on a manifold, can be applied widely, including providing Bayesian analysis of quasi‐likelihoods, linear and non‐linear regression, missing data and hierarchical models.

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Asymptotic Bayesian analysis based on a limited information estimator

Cited by 32 publications

References 28 publications

Error Bands for Impulse Responses

Error Bands for Impulse Responses

FDI technology spillovers, geography, and spatial diffusion

Moment Conditions and Bayesian Non-Parametrics

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