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Conditions sufficient to infer causal relationships using instrumental variables and observational data
Bryant, Henry L.; Bessler, David A. - In: Computational economics 48 (2016) 1, pp. 29-57
Persistent link: https://www.econbiz.de/10011646588
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What to do about the "Cult of statistical significance"? : a renewable fuel application using the Neyman-Pearson protocol
Wojan, Timothy R.; Brown, Jason P.; Lambert, Dayton M. - In: Applied economic perspectives and policy 36 (2014) 4, pp. 674-695
Persistent link: https://www.econbiz.de/10010492775
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The Size and Power of the Variance Ratio Test in Finite Samples : A Monte Carlo Investigation
Lo, Andrew W. - 1988
, and an integrated AR( 1). By choosing the sampling frequency appropriately, the variance ratio test is shown to be as …
Persistent link: https://www.econbiz.de/10012476462
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Hypothesis testing based on a vector of statistics
King, Maxwell L.; Zhang, Xibin; Akram, Muhammad - In: Journal of econometrics 219 (2020) 2, pp. 425-455
Persistent link: https://www.econbiz.de/10012483400
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The size and power of the variance ratio test in finite samples : a Monte Carlo investigation
Lo, Andrew W.; MacKinlay, Archie Craig - 1988
Persistent link: https://www.econbiz.de/10013452056
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The Size and Power of the Variance Ratio Test in Finite Samples : a Monte Carlo Investigation
Lo, Andrew W.; Mackinlay, A. Craig - 2021
, and an integrated AR( 1). By choosing the sampling frequency appropriately, the variance ratio test is shown to be as …
Persistent link: https://www.econbiz.de/10013243404
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Bayesian unit root test in double threshold heteroskedastic models
Chen, Cathy W. S.; Chen, Shu-yu; Lee, Sangyeol - In: Computational economics 42 (2013) 4, pp. 471-490
Persistent link: https://www.econbiz.de/10010249863
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Efficient Sequential Monte-Carlo Samplers for Bayesian Inference
Nguyen, Thi - 2017
In many problems, complex non-Gaussian and/or nonlinear models are required to accurately describe a physical system of interest. In such cases, Monte Carlo algorithms are remarkably flexible and extremely powerful approaches to solve such inference problems. However, in the presence of a...
Persistent link: https://www.econbiz.de/10012954910
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Approximate Bayesian Computations to fit and compare insurance loss models
Goffard, Pierre-Olivier; Laub, Patrick J. - In: Insurance / Mathematics & economics 100 (2021), pp. 350-371
Persistent link: https://www.econbiz.de/10012622398
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Bayesian Inference on GARCH Models Using the Gibbs Sampler
Bauwens, Luc; Lubrano, Michel - 1999
compared with importance sampling and the Metropolis-Hastings algorithm. It is applied to estimate an asymmetric Student …
Persistent link: https://www.econbiz.de/10014197191