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Discrete games of complete information have been used to analyze a variety of contexts such as market entry, technology adoption and peer effects. They are extensions of discrete choice models, where payoffs of each player are dependent on actions of other players, and each outcome is modeled as...
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shapes such as multimodality and long tails. Our simulation-based Bayesian inference treats the density features as random …
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statistical inference, and the simulation uses the Hamiltonian Monte Carlo (HMC) algorithm of Markov Chain Monte Carlo (MCMC) to …
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This paper provides a structural empirical analysis of Dutch auctions of houseplants at the flower auction in Aalsmeer, the Netherlands. The data set is unique for Dutch auctions in the sense that it includes observations of all losing bids in an interval adjacent to the winning bid. The size of...
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-1414.Important choices for efficient and accurate evaluation of marginal likelihoods by means of Monte Carlo simulation methods are … presented of possible advantages and limitations of different simulation techniques; of possible choices of candidate …
Persistent link: https://www.econbiz.de/10011377602
This note presents the R package bayesGARCH (Ardia, 2007) which provides functions for the Bayesian estimation of the parsimonious and effective GARCH(1,1) model with Student-t innovations. The estimation procedure is fully automatic and thus avoids the tedious task of tuning a MCMC sampling...
Persistent link: https://www.econbiz.de/10011380176
We propose a generic Markov Chain Monte Carlo (MCMC) algorithm to speed up computations for datasets with many observations. A key feature of our approach is the use of the highly efficient difference estimator from the survey sampling literature to estimate the log-likelihood accurately using...
Persistent link: https://www.econbiz.de/10011300362