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Estimation of spatial autoregressions with stochastic weight matrices
Gupta, Abhimanyu - 2015
Persistent link: https://www.econbiz.de/10011393188
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Estimating stochastic volatility models : a new approach based on ARMA representations
Francq, Christian; Zakoïan, Jean-Michel - 2000
Persistent link: https://www.econbiz.de/10001549029
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Regression trendbehafteter Zeitreihen in der Ökonometrie
Hassler, Uwe - 2000 - 1. Aufl.
Persistent link: https://www.econbiz.de/10001516312
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Spurious regression in non-stationary panel time series with cross-unit cointegration
Urbain, Jean-Pierre; Westerlund, Joakim - 2008
Persistent link: https://www.econbiz.de/10003921291
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Asymptotic efficiency of the OLS estimator with singular limiting sample moment matrices
Uematsu, Yoshimasa - 2011
Persistent link: https://www.econbiz.de/10009423493
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Estimation in threshold autoregressive models with a stationary and a unit root regime
Gao, Jiti; Tjostheim, Dag; Yin, Jiying - In: Journal of econometrics 172 (2013) 1, pp. 1-13
Persistent link: https://www.econbiz.de/10009702338
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Optimal IV estimation of systems with stochastic regressors and VAR distribances with applications to dynamic systems
Mandy, David M.; Martins-Filho, Carlos - In: Econometric reviews 20 (2001) 4, pp. 485-505
Persistent link: https://www.econbiz.de/10001620910
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Least squares kernel smoothing of the implied volatility smile
Fengler, Matthias R.; Wang, Qihua - In: Applied quantitative finance, (pp. 193-207). 2009
Persistent link: https://www.econbiz.de/10003746018
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Spurious regression in nonstationary panels with cross-unit cointegration
Urbain, Jean-Pierre; Westerlund, Joakim - 2006
Persistent link: https://www.econbiz.de/10003483122
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MCMC vs OLS As a Tool for Interpolating Stochastic Processes
Brody, Caleb - 2018
The purpose of this notebook is to demonstrate a simple use case for MCMC (Markov Chain Monte Carlo) methods for solving for parameters of a known relationship in a system which is prone to black swan events and large jumps. This is only a test of principal, using simulated data, for the purpose...
Persistent link: https://www.econbiz.de/10012930569