Deriving conditional distributions of AR(1) process with drift












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I have an AR(1) process with drift:



$y_t=μ+ρ_{t-1}+ε_t$



with the errors following AR(1) process: $ε_t=φε_{t-1}+u_t$



for $t=1, ..., T; ε_0=0$; and $u_t$ are iid $N(0, σ^2)$.



We have these independent priors: $φ$ ~ $N(0, 1)$; $ρ$ ~ $N(0, 1)$; $μ$ ~ $N(0, 100)$ and $σ^2$ ~ $IG(5, 10)$.



The task is to derive the following conditional distributions:



$f(σ^2|y,μ,ρ,φ)$, $f(φ|y,μ,ρ,σ^2)$, and $f(μ,ρ|y,φ,σ^2)$.



What I know is that the posterior is proportional to likelihood*prior. I assume that for the last conditional distribution, $f(μ,ρ|y,φ,σ^2)$ I am supposed to use joint $p(μ,ρ)$ as a prior.



But with this many parameters, I am very confused what likelihoods should be employed for the calculation of each conditional distribution. Could somebody please clarify this to me? How do I know what likelihood to use? Also, does AR(1) process play any role in this calculation?










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    $begingroup$


    I have an AR(1) process with drift:



    $y_t=μ+ρ_{t-1}+ε_t$



    with the errors following AR(1) process: $ε_t=φε_{t-1}+u_t$



    for $t=1, ..., T; ε_0=0$; and $u_t$ are iid $N(0, σ^2)$.



    We have these independent priors: $φ$ ~ $N(0, 1)$; $ρ$ ~ $N(0, 1)$; $μ$ ~ $N(0, 100)$ and $σ^2$ ~ $IG(5, 10)$.



    The task is to derive the following conditional distributions:



    $f(σ^2|y,μ,ρ,φ)$, $f(φ|y,μ,ρ,σ^2)$, and $f(μ,ρ|y,φ,σ^2)$.



    What I know is that the posterior is proportional to likelihood*prior. I assume that for the last conditional distribution, $f(μ,ρ|y,φ,σ^2)$ I am supposed to use joint $p(μ,ρ)$ as a prior.



    But with this many parameters, I am very confused what likelihoods should be employed for the calculation of each conditional distribution. Could somebody please clarify this to me? How do I know what likelihood to use? Also, does AR(1) process play any role in this calculation?










    share|cite|improve this question









    $endgroup$















      0












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      $begingroup$


      I have an AR(1) process with drift:



      $y_t=μ+ρ_{t-1}+ε_t$



      with the errors following AR(1) process: $ε_t=φε_{t-1}+u_t$



      for $t=1, ..., T; ε_0=0$; and $u_t$ are iid $N(0, σ^2)$.



      We have these independent priors: $φ$ ~ $N(0, 1)$; $ρ$ ~ $N(0, 1)$; $μ$ ~ $N(0, 100)$ and $σ^2$ ~ $IG(5, 10)$.



      The task is to derive the following conditional distributions:



      $f(σ^2|y,μ,ρ,φ)$, $f(φ|y,μ,ρ,σ^2)$, and $f(μ,ρ|y,φ,σ^2)$.



      What I know is that the posterior is proportional to likelihood*prior. I assume that for the last conditional distribution, $f(μ,ρ|y,φ,σ^2)$ I am supposed to use joint $p(μ,ρ)$ as a prior.



      But with this many parameters, I am very confused what likelihoods should be employed for the calculation of each conditional distribution. Could somebody please clarify this to me? How do I know what likelihood to use? Also, does AR(1) process play any role in this calculation?










      share|cite|improve this question









      $endgroup$




      I have an AR(1) process with drift:



      $y_t=μ+ρ_{t-1}+ε_t$



      with the errors following AR(1) process: $ε_t=φε_{t-1}+u_t$



      for $t=1, ..., T; ε_0=0$; and $u_t$ are iid $N(0, σ^2)$.



      We have these independent priors: $φ$ ~ $N(0, 1)$; $ρ$ ~ $N(0, 1)$; $μ$ ~ $N(0, 100)$ and $σ^2$ ~ $IG(5, 10)$.



      The task is to derive the following conditional distributions:



      $f(σ^2|y,μ,ρ,φ)$, $f(φ|y,μ,ρ,σ^2)$, and $f(μ,ρ|y,φ,σ^2)$.



      What I know is that the posterior is proportional to likelihood*prior. I assume that for the last conditional distribution, $f(μ,ρ|y,φ,σ^2)$ I am supposed to use joint $p(μ,ρ)$ as a prior.



      But with this many parameters, I am very confused what likelihoods should be employed for the calculation of each conditional distribution. Could somebody please clarify this to me? How do I know what likelihood to use? Also, does AR(1) process play any role in this calculation?







      probability-distributions bayesian






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      asked Dec 2 '18 at 0:59









      mineniviminenivi

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