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Variational Bayes approximation of factor stochastic volatility models
Gunawan, David; Kohn, Robert; Nott, David - In: International journal of forecasting 37 (2021) 4, pp. 1355-1375
Persistent link: https://www.econbiz.de/10013274279
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Spatial dependence in regional business cycles : evidence from Mexican states
Kondo, Keisuke - 2015
Persistent link: https://www.econbiz.de/10011309912
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Latent multilateral trade resistance indices : theory and evidence
Koch, Wilfried; Lesage, James P. - In: Scottish journal of political economy : the journal of … 62 (2015) 3, pp. 264-290
Persistent link: https://www.econbiz.de/10011382843
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Spatial dependence in regional business cycles : evidence from Mexican states
Kondo, Keisuke - 2022
Persistent link: https://www.econbiz.de/10012817266
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Spatial dependence in regional business cycles : evidence from Mexican states
Kondo, Keisuke - 2021 - Revised June 15, 2021
Persistent link: https://www.econbiz.de/10012628229
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Bayesian spatial bivariate panel probit estimation
Baltagi, Badi H.; Egger, Peter; Kesina, Michaela - In: Spatial econometrics: qualitative and limited dependent …, (pp. 119-144). 2017
Persistent link: https://www.econbiz.de/10011587603
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Local marginal analysis of spatial data : a Gaussian Process Regression approach with Bayesian model and kernel averaging
Dearmon, Jacob; Smith, Tony E. - In: Spatial econometrics: qualitative and limited dependent …, (pp. 297-342). 2017
Persistent link: https://www.econbiz.de/10011587661
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City and industry network impacts on innovation by Chinese manufacturing firms : a hierarchical spatial-interindustry model
Sheng, Yuxue; Lesage, James P. - In: Spatial econometrics: qualitative and limited dependent …, (pp. 343-386). 2017
Persistent link: https://www.econbiz.de/10011587671
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The interplay between the Bayesian and frequentist approaches : a general nesting spatial panel data model
Ramírez, Andrés - In: Spatial economic analysis : the journal of the Regional … 12 (2017) 1, pp. 92-112
Persistent link: https://www.econbiz.de/10011669075
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Non-stationary partition modeling of geostatistical data for malaria risk mapping
Gosoniu, Laura; Vounatsou, Penelope - In: Journal of Applied Statistics 38 (2011) 1, pp. 3-13
The most common assumption in geostatistical modeling of malaria is stationarity, that is spatial correlation is a function of the separation vector between locations. However, local factors (environmental or human-related activities) may influence geographical dependence in malaria transmission...
Persistent link: https://www.econbiz.de/10008773825