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Bbsvars.bsky.social

๐š’๐š—๐šœ๐š๐šŠ๐š•๐š•.๐š™๐šŠ๐šŒ๐š”๐šŠ๐š๐šŽ๐šœ("๐š‹๐šœ๐šŸ๐šŠ๐š›๐šœ") ๐Ÿ’๐Ÿ’– ๐š’๐š—๐šœ๐š๐šŠ๐š•๐š•.๐š™๐šŠ๐šŒ๐š”๐šŠ๐š๐šŽ๐šœ("๐š‹๐šœ๐šŸ๐šŠ๐š›๐š‚๐™ธ๐™ถ๐™ฝ๐šœ") ๐Ÿ–ค๐Ÿ’œ ...and enjoy! #bsvars#bsvarSIGNs#rstats#foss#skyecon

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Bbsvars.bsky.social

The pipes work well! ๐Ÿšฟ๐Ÿšฐ The ๐—ฅ packages ๐—ฏ๐˜€๐˜ƒ๐—ฎ๐—ฟ๐˜€ and ๐—ฏ๐˜€๐˜ƒ๐—ฎ๐—ฟ๐—ฆ๐—œ๐—š๐—ก๐˜€ facilitate workflows with pipes! So choose your fav |> or %>% and go for it! #bsvars#bsvarSIGNs#rstats#foss#econsky

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Bbsvars.bsky.social

Performing structural and predictive analyses is just the same in both ๐—ฅ packages ๐—ฏ๐˜€๐˜ƒ๐—ฎ๐—ฟ๐˜€ and ๐—ฏ๐˜€๐˜ƒ๐—ฎ๐—ฟ๐—ฆ๐—œ๐—š๐—ก๐˜€! They're so so easy! ๐Ÿฐ๐Ÿช And they're complemented by superb ๐šœ๐šž๐š–๐š–๐šŠ๐š›๐šข() and ๐š™๐š•๐š˜๐š() functions! Nice! โ˜• ๐Ÿต #bsvars#bsvarSIGNs#rstats#foss

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Bbsvars.bsky.social

The workflows in the ๐—ฅ packages ๐—ฏ๐˜€๐˜ƒ๐—ฎ๐—ฟ๐˜€ and ๐—ฏ๐˜€๐˜ƒ๐—ฎ๐—ฟ๐—ฆ๐—œ๐—š๐—ก๐˜€ are v similar! ๐Ÿง๐Ÿจ Upload the package and data, ๐šœ๐š™๐šŽ๐šŒ๐š’๐š๐šข_* a model, and then just apply function ๐šŽ๐šœ๐š๐š’๐š–๐šŠ๐š๐šŽ() that will automatically choose the right estimation algorithm for the model. ๐Ÿฉ๐Ÿช Juicy! ๐Ÿฌ๐Ÿญ #bsvars#bsvarSIGNs#rstats#foss#econsky

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Bbsvars.bsky.social

In ๐—ฏ๐˜€๐˜ƒ๐—ฎ๐—ฟ๐—ฆ๐—œ๐—š๐—ก๐˜€, after estimating hyper-parameters, you sample estimate the rest of the parameters sampling independent draws from the posterior! So, no convergence and burn-in is needed!

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Bbsvars.bsky.social

The two ๐—ฅ packages ๐—ฏ๐˜€๐˜ƒ๐—ฎ๐—ฟ๐˜€ and ๐—ฏ๐˜€๐˜ƒ๐—ฎ๐—ฟ๐—ฆ๐—œ๐—š๐—ก๐˜€ are compatible wrt the functions, objects, and workflows! ๐Ÿ‘ฉโ€๐Ÿ’ผ๐Ÿ—๏ธ If you know ๐—ฏ๐˜€๐˜ƒ๐—ฎ๐—ฟ๐˜€, learning ๐—ฏ๐˜€๐˜ƒ๐—ฎ๐—ฟ๐—ฆ๐—œ๐—š๐—ก๐˜€ is super easy ๐Ÿ–ค๐Ÿ’œ #bsvars#bsvarSIGNs#rstats#foss#econsky

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Bbsvars.bsky.social

The two ๐—ฅ packages ๐—ฏ๐˜€๐˜ƒ๐—ฎ๐—ฟ๐˜€ and ๐—ฏ๐˜€๐˜ƒ๐—ฎ๐—ฟ๐—ฆ๐—œ๐—š๐—ก๐˜€ are compatible wrt the functions, objects, and workflows! ๐Ÿ‘ฉโ€๐Ÿ’ผ๐Ÿ—๏ธ So, once you learn how to use one package working with the other will be super easy! ๐Ÿ˜Žโ›ต #bsvars#bsvarSIGNs#rstats#econsky

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Bbsvars.bsky.social

Blazingly fast code with ๐—ฅ packages ๐—ฏ๐˜€๐˜ƒ๐—ฎ๐—ฟ๐˜€ and ๐—ฏ๐˜€๐˜ƒ๐—ฎ๐—ฟ๐—ฆ๐—œ๐—š๐—ก๐˜€! ๐Ÿ’ซ We implemented frontier econometric and numerical methods coded in ๐—–++ to make the estimation and analysis as fast as possible. ๐Ÿคฉ #bsvars#bsvarSIGNs#rstats#econsky

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Bbsvars.bsky.social

Team spirit! ๐Ÿ‘ญ Together, the ๐—ฅ packages ๐—ฏ๐˜€๐˜ƒ๐—ฎ๐—ฟ๐˜€ and ๐—ฏ๐˜€๐˜ƒ๐—ฎ๐—ฟ๐—ฆ๐—œ๐—š๐—ก๐˜€ offer a wide range of modelling approaches and identification for Bayesian Structural Vector Autorgressions! ๐Ÿ‘ซ Just to make certain that you will find there a model just right for your application! ๐Ÿ‘จโ€๐Ÿ’ป๐Ÿ‘ฉโ€๐Ÿ’ป #bsvars#bsvarSIGNs#rstats#skyecon

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Bbsvars.bsky.social

Hey! ๐Ÿจ I have just given a talk about ๐˜š๐˜ต๐˜ณ๐˜ถ๐˜ค๐˜ต๐˜ถ๐˜ณ๐˜ข๐˜ญ ๐˜”๐˜ข๐˜ค๐˜ณ๐˜ฐ ๐˜ˆ๐˜ฏ๐˜ข๐˜ญ๐˜บ๐˜ด๐˜ฆ๐˜ด ๐˜œ๐˜ด๐˜ช๐˜ฏ๐˜จ ๐˜™ ๐˜ฑ๐˜ข๐˜ค๐˜ฌ๐˜ข๐˜จ๐˜ฆ๐˜ด ๐˜ฃ๐˜ด๐˜ท๐˜ข๐˜ณ๐˜ด ๐˜ข๐˜ฏ๐˜ฅ ๐˜ฃ๐˜ด๐˜ท๐˜ข๐˜ณ๐˜š๐˜๐˜Ž๐˜•๐˜ด for the QuantEcon Lab at the ANU College of Business and Economics ๐Ÿฆ‡ quantecon.orgbsvars.github.io/2024-08-bsva...#bsvars#bsvarSIGNs#rstats#econsky

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