Hey @yobin-tim

Please, provide inputs to the Gamma scale mixture of normal section of the doc.

Please include the following parts:

1. State the normal-gamma prior as the hierarchical prior in which the scalar scale premultiplying the covariance matrix of the normal prior for vector $\boldsymbol\alpha$ follows a gamma distribution $$\kappa_{\boldsymbol\alpha}\sim G(s,a)$$
2. Describe briefly a Gibbs sampler in which one step is the normal-ig2 for $\boldsymbol\alpha$ and $\sigma^2$ and the other is the gamma for $\kappa_{\boldsymbol\alpha}$
3. State the GIG full conditional posterior and its parameters
4. Present R code to sample from the GIG distribution using package GIGrvn

Introduce the material using the notation in line with that established in section Autoregressions.

Please, create a Pull Request and include there all your commits containing contributions to this section. In your commits, please, include changes only to the index.qmd file. Could you make your submission clear, making instructive comments on the individual commits? If you're planning to introduce changes to other parts of the website, you're welcome to do that in a separate pull request. This would require you either to play with the branches to which you commit changes in GitHub Desktop, or to wait with introducing changes to other parts of the page later on, when you submit the Pull Request about your section.

Hey @mandyxmg and @vsonnemans

Please, provide inputs to the Forecasting section of the doc.

Please include the following parts:

1. Conditional predictive density one period ahead - this part is assigned to @mandyxmg
2. Algorithm to sample from the predictive density with the description of how the latest draws of parameters and forecasted values are used in the iterative forecast - this part is assigned to @vsonnemans
3. Sampler implementation in R for a single draw s but at all horizons h=1,...,H

Introduce the material using the notation in line with that established in section Autoregressions.

Please, create a Pull Request and include there all your commits containing contributions to this section. In your commits, please, include changes only to the index.qmd file. Could you make your submission clear, making instructive comments on the individual commits? If you're planning to introduce changes to other parts of the website, you can do that in a separate pull request. This would require you either to play with the branches to which you commit changes in GitHub Desktop or to wait to introduce changes to other parts of the page later on when you submit the Pull Request about your section.

Hey @DjangoTG

Please, provide inputs to the Dummy observation prior section of the doc.

Please include the following parts:

1. State the idea behind the dummy observation prior as one that for the sake of the estimation of the parameters, requires only the extension of y and X matrices. The only challenge here is to adjust the framework you used in the paper to the univariate case of N=1. If I'm not mistaken, when N=1 both dummy initial observations and the sum-of-coefficient priors result in the same rows for y and X. In such a case, present the prior as extending y and X by one row only :)
2. Present R code to generate the additional row of y and X

Introduce the material using the notation in line with that established in section Autoregressions.

Please, create a Pull Request and include there all your commits containing contributions to this section. In your commits, please, include changes only to the index.qmd file. Could you make your submission clear, making instructive comments on the individual commits? If you're planning to introduce changes to other parts of the website, you can do that in a separate pull request. This would require you either to play with the branches to which you commit changes in GitHub Desktop or to wait to introduce changes to other parts of the page later on when you submit the Pull Request about your section.

Hey @jonasld23 and @ThomasKronhol

Please, provide inputs to the Natural-conjugate analysis section of the doc.

It includes four subsections:

1. Likelihood as normal-inverted gamma 2
2. Normal-inverted gamma 2 prior
3. Normal-inverted gamma 2 posterior
4. Sampling draws from the posterior

Introduce the material using the notation in line with that established in section Autoregressions.

The last subsection should include R function for sampling random draws from the posterior.

Please, create a Pull Request and include there all your commits containing contributions to this section. In your commits, please, include changes only to the index.qmd file. Could you make your submission clear, making instructive comments on the individual commits? If you're planning to introduce changes to other parts of the website, you're welcome to do that in a separate pull request. This would require you either to play with the branches to which you commit changes in GitHub Desktop, or to wait with introducing changes to other parts of the page later on, when you submit the Pull Request about your section.

Hey @nathanh93

Please, provide inputs to the Student-t error term section of the doc.

Please include the following parts (make each of them as short as possible, please):

1. Motivate why one would use Student-t error terms in two sentences.
2. Present a univariate Student-t distribution as IG2 scale mixture of a normal distribution
3. Present the necessary extension to the Gibbs sampler briefly using normal-ig2 for $\boldsymbol\alpha$ and $\sigma^2$ and the other is the ig2 for $\lambda$
4. State the IG2 full conditional posterior and its parameters
5. Present R code to sample from the ig2 distribution

Introduce the material using the notation in line with that established in section Autoregressions.

Please, create a Pull Request and include there all your commits containing contributions to this section. In your commits, please, include changes only to the index.qmd file. Could you make your submission clear, making instructive comments on the individual commits? If you're planning to introduce changes to other parts of the website, you can do that in a separate pull request. This would require you either to play with the branches to which you commit changes in GitHub Desktop or to wait to introduce changes to other parts of the page later on when you submit the Pull Request about your section.

Hey @rayccg and @Wintersend

Please, provide inputs to the Estimating autoregressions after 2020 section of the doc.

Please include the following two parts:

1. Concept
2. Algorithm with code

Introduce the material using the notation in line with that established in section Autoregressions.

Please, create a Pull Request and include there all your commits containing contributions to this section. In your commits, please, include changes only to the index.qmd file. Could you make your submission clear, making instructive comments on the individual commits? If you're planning to introduce changes to other parts of the website, you can do that in a separate pull request. This would require you either to play with the branches to which you commit changes in GitHub Desktop or to wait to introduce changes to other parts of the page later on when you submit the Pull Request about your section.

Hey @hanwenzhang0317

Please, provide inputs to the Inverted gamma 2 scale mixture of normal section of the doc.

Please include the following parts:

1. State the normal-inverted gamma 2 prior as the hierarchical prior in which the scalar scale premultiplying the covariance matrix of the normal prior for vector $\boldsymbol\alpha$ follows an inverted gamma 2 distribution $$\kappa_{\boldsymbol\alpha}\sim IG2(s,\nu)$$
2. Describe briefly a Gibbs sampler in which one step is the normal-ig2 for $\boldsymbol\alpha$ and $\sigma^2$ and the other is the ig2 for $\kappa_{\boldsymbol\alpha}$
3. State the IG2 full conditional posterior and its parameters
4. Present R code to sample from the ig2 distribution

Introduce the material using the notation in line with that established in section Autoregressions.

Please, create a Pull Request and include there all your commits containing contributions to this section. In your commits, please, include changes only to the index.qmd file. Could you make your submission clear, making instructive comments on the individual commits? If you're planning to introduce changes to other parts of the website, you're welcome to do that in a separate pull request. This would require you either to play with the branches to which you commit changes in GitHub Desktop, or to wait with introducing changes to other parts of the page later on, when you submit the Pull Request about your section.

Hey @IoTiFeelo

Please, provide inputs to the Estimating error term variance prior scale section of the doc.

Please include the following parts:

1. State the ig2-gamma prior as the hierarchical prior in which the scalar scale of the IG2 prior for vector $\sigma^2$ follows a gamma distribution $$\underline{s}_{\sigma^2}\sim G(s,a)$$
2. Describe briefly a Gibbs sampler in which one step is the normal-ig2 for $\boldsymbol\alpha$ and $\sigma^2$ and the other is the gamma for $\underline{s}_{\sigma^2}$
3. State the gamma full conditional posterior and its parameters
4. Present R code to sample from the gamma distribution

Introduce the material using the notation in line with that established in section Autoregressions.

Please, create a Pull Request and include there all your commits containing contributions to this section. In your commits, please, include changes only to the index.qmd file. Could you make your submission clear, making instructive comments on the individual commits? If you're planning to introduce changes to other parts of the website, you're welcome to do that in a separate pull request. This would require you either to play with the branches to which you commit changes in GitHub Desktop, or to wait with introducing changes to other parts of the page later on, when you submit the Pull Request about your section.

Hey @mantihuang and @Eung1

Please, provide inputs to the Stochastic volatility heteroskedasticity section of the doc.

Please include the following parts:

1. Write out the model equations
2. List essential techniques for Bayesian computations with citations
3. Scrutinise the Gibbs sampler providing only the most important points
4. Present provided R code to sample a draw from full conditional posterior distributions by linking its elements to the parts of algo described in point 3. above.

Introduce the material using the notation in line with that established in section Autoregressions.

Please, create a Pull Request and include there all your commits containing contributions to this section. In your commits, please, include changes only to the index.qmd file. Could you make your submission clear, making instructive comments on the individual commits? If you're planning to introduce changes to other parts of the website, you can do that in a separate pull request. This would require you either to play with the branches to which you commit changes in GitHub Desktop or to wait to introduce changes to other parts of the page later on when you submit the Pull Request about your section.