#bsvars β Public Fediverse posts
Live and recent posts from across the Fediverse tagged #bsvars, aggregated by home.social.
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π Ah, it's so good when co-authors go through the final code review! ποΈ Thanks Fei for correcting the forecasts! π
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π Ah, it's so good when co-authors go through the final code review! ποΈ Thanks Fei for correcting the forecasts! π
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π Ah, it's so good when co-authors go through the final code review! ποΈ Thanks Fei for correcting the forecasts! π
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π Ah, it's so good when co-authors go through the final code review! ποΈ Thanks Fei for correcting the forecasts! π
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β€οΈππ Already this Friday! Tomasz is presenting the newest version of our R package bpvars at the Ghana R Users Conference! β€οΈππ The package and the material is great! And the conference looks super interesting! Join us β€οΈππ
β€οΈππ https://bsvars.org/2026-07-Ghana-R
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β€οΈππ Already this Friday! Tomasz is presenting the newest version of our R package bpvars at the Ghana R Users Conference! β€οΈππ The package and the material is great! And the conference looks super interesting! Join us β€οΈππ
β€οΈππ https://bsvars.org/2026-07-Ghana-R
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β€οΈππ Already this Friday! Tomasz is presenting the newest version of our R package bpvars at the Ghana R Users Conference! β€οΈππ The package and the material is great! And the conference looks super interesting! Join us β€οΈππ
β€οΈππ https://bsvars.org/2026-07-Ghana-R
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β€οΈππ Already this Friday! Tomasz is presenting the newest version of our R package bpvars at the Ghana R Users Conference! β€οΈππ The package and the material is great! And the conference looks super interesting! Join us β€οΈππ
β€οΈππ https://bsvars.org/2026-07-Ghana-R
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β€οΈππ Already this Friday! Tomasz is presenting the newest version of our R package bpvars at the Ghana R Users Conference! β€οΈππ The package and the material is great! And the conference looks super interesting! Join us β€οΈππ
β€οΈππ https://bsvars.org/2026-07-Ghana-R
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That's a co-author grade π² Finds a mistake in preliminary code, corrects your C++ code, submits a Pull Request, becomes a contributor! I think we have a paper! Thanks, Fei Shang!
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That's a co-author grade π² Finds a mistake in preliminary code, corrects your C++ code, submits a Pull Request, becomes a contributor! I think we have a paper! Thanks, Fei Shang!
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That's a co-author grade π² Finds a mistake in preliminary code, corrects your C++ code, submits a Pull Request, becomes a contributor! I think we have a paper! Thanks, Fei Shang!
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That's a co-author grade π² Finds a mistake in preliminary code, corrects your C++ code, submits a Pull Request, becomes a contributor! I think we have a paper! Thanks, Fei Shang!
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That's a co-author grade π² Finds a mistake in preliminary code, corrects your C++ code, submits a Pull Request, becomes a contributor! I think we have a paper! Thanks, Fei Shang!
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π verbs are functions in our R packages π bsvars bsvarSIGNs bpvars bvars π
π specify a model
β¨ estimate it
β compute things to interpret
π« forecast future values
β¨ verify hypothesesπ https://bsvars.org
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π verbs are functions in our R packages π bsvars bsvarSIGNs bpvars bvars π
π specify a model
β¨ estimate it
β compute things to interpret
π« forecast future values
β¨ verify hypothesesπ https://bsvars.org
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π verbs are functions in our R packages π bsvars bsvarSIGNs bpvars bvars π
π specify a model
β¨ estimate it
β compute things to interpret
π« forecast future values
β¨ verify hypothesesπ https://bsvars.org
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π functions verify test model and data properties in our R packages π bsvars bsvarSIGNs bpvars bvars π
π verify homoskedasticity
β¨ verify normality
β verify restrictions on autoregressive parameters
π« using Bayes factorsπ https://bsvars.org
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π functions verify test model and data properties in our R packages π bsvars bsvarSIGNs bpvars bvars π
π verify homoskedasticity
β¨ verify normality
β verify restrictions on autoregressive parameters
π« using Bayes factorsπ https://bsvars.org
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π functions verify test model and data properties in our R packages π bsvars bsvarSIGNs bpvars bvars π
π verify homoskedasticity
β¨ verify normality
β verify restrictions on autoregressive parameters
π« using Bayes factorsπ https://bsvars.org
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π functions verify test model and data properties in our R packages π bsvars bsvarSIGNs bpvars bvars π
π verify homoskedasticity
β¨ verify normality
β verify restrictions on autoregressive parameters
π« using Bayes factorsπ https://bsvars.org
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π function forecast is used to obtain draws from the predictive density in our R packages π bsvars bsvarSIGNs bpvars bvars π
π Bayesian forecasting with state-of-the-art models
β¨ point/density forecasting
β great plots
π« blazingly fast computationsπ https://bsvars.org
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π function forecast is used to obtain draws from the predictive density in our R packages π bsvars bsvarSIGNs bpvars bvars π
π Bayesian forecasting with state-of-the-art models
β¨ point/density forecasting
β great plots
π« blazingly fast computationsπ https://bsvars.org
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π function forecast is used to obtain draws from the predictive density in our R packages π bsvars bsvarSIGNs bpvars bvars π
π Bayesian forecasting with state-of-the-art models
β¨ point/density forecasting
β great plots
π« blazingly fast computationsπ https://bsvars.org
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π function forecast is used to obtain draws from the predictive density in our R packages π bsvars bsvarSIGNs bpvars bvars π
π Bayesian forecasting with state-of-the-art models
β¨ point/density forecasting
β great plots
π« blazingly fast computationsπ https://bsvars.org
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π we use non-centred stochastic volatility for structural VARs
β¨ with a prior for variances centred at homoskedasticity with strong shrinkage
π« verify partial identification of a shock
β efficient estimation and normalisation
π great for fiscal policy
π the paper behind my R package bsvars -
π we use non-centred stochastic volatility for structural VARs
β¨ with a prior for variances centred at homoskedasticity with strong shrinkage
π« verify partial identification of a shock
β efficient estimation and normalisation
π great for fiscal policy
π the paper behind my R package bsvars -
π we use non-centred stochastic volatility for structural VARs
β¨ with a prior for variances centred at homoskedasticity with strong shrinkage
π« verify partial identification of a shock
β efficient estimation and normalisation
π great for fiscal policy
π the paper behind my R package bsvars -
π we use non-centred stochastic volatility for structural VARs
β¨ with a prior for variances centred at homoskedasticity with strong shrinkage
π« verify partial identification of a shock
β efficient estimation and normalisation
π great for fiscal policy
π the paper behind my R package bsvars -
β It's official now! β¨ Our latest paper is out in the Journal of Econometrics π« https://doi.org/10.1016/j.jeconom.2025.106107
β Partial identification of structural vector autoregressions with non-centred stochastic volatility π
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β It's official now! β¨ Our latest paper is out in the Journal of Econometrics π« https://doi.org/10.1016/j.jeconom.2025.106107
β Partial identification of structural vector autoregressions with non-centred stochastic volatility π
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β It's official now! β¨ Our latest paper is out in the Journal of Econometrics π« https://doi.org/10.1016/j.jeconom.2025.106107
β Partial identification of structural vector autoregressions with non-centred stochastic volatility π
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β It's official now! β¨ Our latest paper is out in the Journal of Econometrics π« https://doi.org/10.1016/j.jeconom.2025.106107
β Partial identification of structural vector autoregressions with non-centred stochastic volatility π
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π use our function compute to process estimation output and obtain posterior draws for quantities of interest π
π impulse responses
β¨ forecast variance decomposition
π« historical decomposition
β structural shocks
π fitted values
π conditional sd
π« regime probabilitiesπ https://bsvars.org/
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π use our function compute to process estimation output and obtain posterior draws for quantities of interest π
π impulse responses
β¨ forecast variance decomposition
π« historical decomposition
β structural shocks
π fitted values
π conditional sd
π« regime probabilitiesπ https://bsvars.org/
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π to estimate is to run Bayesian estimation and obtain draws from posterior distribution π
π https://bsvars.org/
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π to estimate is to run Bayesian estimation and obtain draws from posterior distribution π
π https://bsvars.org/
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π verify! π I forgot about verify! β how could I forget about verify?! π
π€£ππ #bsvars #rstats #neverForgetVerify
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π verify! π I forgot about verify! β how could I forget about verify?! π
π€£ππ #bsvars #rstats #neverForgetVerify
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π verify! π I forgot about verify! β how could I forget about verify?! π
π€£ππ #bsvars #rstats #neverForgetVerify
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π verify! π I forgot about verify! β how could I forget about verify?! π
π€£ππ #bsvars #rstats #neverForgetVerify
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π verify! π I forgot about verify! β how could I forget about verify?! π
π€£ππ #bsvars #rstats #neverForgetVerify
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π in our package use one of the specify functions to create model specification and customise it π
π specify functions do all that:
π specify model's priors
β¨ specify identification
π« create data matrices
β create starting valuesπ https://bsvars.org/
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π in our package use one of the specify functions to create model specification and customise it π
π specify functions do all that:
π specify model's priors
β¨ specify identification
π« create data matrices
β create starting valuesπ https://bsvars.org/
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π bsvars verbs are commands used for your data analysis using our packages π
π specify a model
β¨ estimate it
π« compute quantities of interest
β forecastπ https://bsvars.org/
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π bsvars verbs are commands used for your data analysis using our packages π
π specify a model
β¨ estimate it
π« compute quantities of interest
β forecastπ https://bsvars.org/
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π bsvars verbs are commands used for your data analysis using our packages π
π specify a model
β¨ estimate it
π« compute quantities of interest
β forecastπ https://bsvars.org/
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π bsvars verbs are commands used for your data analysis using our packages π
π specify a model
β¨ estimate it
π« compute quantities of interest
β forecastπ https://bsvars.org/
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π our packages come in all colours β¨ exhibit a high level of integration in terms of syntax, workflows, and design β so they are all easy for you to use π« you learn one - you get it all π and offer a great range of models and applications π€©
π https://bsvars.org/
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π our packages come in all colours β¨ exhibit a high level of integration in terms of syntax, workflows, and design β so they are all easy for you to use π« you learn one - you get it all π and offer a great range of models and applications π€©
π https://bsvars.org/
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π our packages come in all colours β¨ exhibit a high level of integration in terms of syntax, workflows, and design β so they are all easy for you to use π« you learn one - you get it all π and offer a great range of models and applications π€©
π https://bsvars.org/
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π our packages come in all colours β¨ exhibit a high level of integration in terms of syntax, workflows, and design β so they are all easy for you to use π« you learn one - you get it all π and offer a great range of models and applications π€©
π https://bsvars.org/
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π€π Our bpvars package includes a range of model specifications!
π€ they're fantastic for forecasting dynamic panel data
π they provide robust basis fitting various data well -
π€π Our bpvars package includes a range of model specifications!
π€ they're fantastic for forecasting dynamic panel data
π they provide robust basis fitting various data well -
π€π Our bpvars package includes a range of model specifications!
π€ they're fantastic for forecasting dynamic panel data
π they provide robust basis fitting various data well -
ππ€ version 2.0 of our R package bpvars for forecasting with Bayesian panel vector autoregressions is out on CRAN! And it's great!