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The posterior mean of [[latex($\mathbf A$)]] is a convex combination of [[latex($A^*(\theta)$)]], the prior mean, and of the OLS estimate of [[latex($\mathbf A$)]]. When [[latex($\lambda$)]] goes to infinity the posterior mean shrinks towards the prior mean, ''ie'' the projection of the DSGE model onto the VAR([[latex($p$)]]). We do not have a closed form expression for the joint posterior density of [[latex($\theta$)]] and [[latex($\lambda$)]] (the second term on the right hand side of \equaref{posterior1}). So the posterior distribution of [[latex($(\theta,\lambda)$)]] is recovered from an MCMC algorithm, as described in Del Negro and Schorfheide, except that we do estimate [[latex($\lambda$)]] as the deep parameters [[latex($\theta$)]]. The posterior mean of [[latex($\mathbf A$)]] is a convex combination of [[latex($A^*(\theta)$)]], the prior mean, and of the OLS estimate of [[latex($\mathbf A$)]]. When [[latex($\lambda$)]] goes to infinity the posterior mean shrinks towards the prior mean, ''ie'' the projection of the DSGE model onto the VAR([[latex($p$)]]). We do not have a closed form expression for the joint posterior density of [[latex($\theta$)]] and [[latex($\lambda$)]] (the second term on the right hand side of [Q3]). So the posterior distribution of [[latex($(\theta,\lambda)$)]] is recovered from an MCMC algorithm, as described in Del Negro and Schorfheide, except that we do estimate [[latex($\lambda$)]] as the deep parameters [[latex($\theta$)]].

Consider the order latex($p$) VAR representation for the latex($1\times m$) vector of observed variables latex($y_t$):

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where latex($u_t\sim \mathcal N\left( 0,\Sigma_u\right)$). Let latex($z_t$) be the latex($mp\times 1$) vector latex($\left[y_{t-1}',...,y_{t-p}'\right]'$) and define latex($\mathbf{A}=\left[\mathbf A_1',...,\mathbf A_p'\right]'$), the VAR representation can then be written in matrix form as:

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where latex($Y = (y_1',\dots,y_T')'$), latex($Z = (z_1',\dots,z_T')'$) and latex($\mathcal U = (u_1',\dots,u_T')'$).

Dummy observations prior for the VAR can be constructed using the VAR likelihood function for latex($\mathcal T = [\lambda T]$) artificial data simulated with the DSGE latex($\left( Y^{\ast },Z^{\ast}\right)$), combined with diffuse priors. The prior is then given by:

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implying that latex($\Sigma$) follows an inverted Wishart distribution and latex($\mathbf A$) conditional on latex($\Sigma$) is gaussian. Assuming that observables are covariance stationary, Del Negro and Schorfheide use the DSGE theoretical autocovariance matrices for a given latex($n\times 1$) vector of model parameters latex($\theta$), denoted latex($\Gamma_{YY}\left( \theta \right)$), latex($\Gamma_{ZY}\left( \theta \right)$), latex($\Gamma_{YZ}\left( \theta\right)$), latex($\Gamma_{ZZ}\left( \theta \right)$) instead of the (artificial) sample moments latex(${Y^*}'Y^*$), latex(${Z^*}'Y^*$), latex(${Y^*}'Z^*$), latex(${Z^*}'Z^*$). In addition, the latex($p$)-th order VAR approximation of the DSGE provides the first moment of the prior distributions through the population least-square regression:

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Conditional on the deep parameters of the DSGE latex($\theta $) and latex($\lambda$), the priors for the VAR parameters are given by:

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where latex($\Gamma_{ZZ}(\theta)$) is assumed to be non singular and latex($\lambda \geq \frac{mp+m}{T}$) for the priors to be proper (Note that it would not be possible to estimate the VAR model by OLS (or maximum likelihood) if we had latex($\mathcal T < m(p+1)$). In this case we would not have more observations than parameters to estimate). The a priori density of latex($\mathbf A$) is defined by latex($n+1$) parameters (latex($\theta$) and latex($\lambda$)), which is likely to be less than latex($mp$) (the VAR number of parameters). If we have a one-to-one relationship (no identification issues) between latex($(\theta,\lambda)$) and latex($\mathbf A$) it will be a good idea to estimate latex($(\theta,\lambda)$) instead of latex($\mathbf A$), ie to estimate fewer free parameters. To do so, Del Negro and Schorfheide complete the prior by specifying a prior distribution over the structural model's deep parameters: latex($p_0(\theta)$). We still have to set the weight of the structural prior, latex($\lambda$). Del Negro and Schorfheide choose the value of latex($\lambda$) that maximizes the marginal density. They estimate a limited number of DSGE-VAR models with different values of latex($\lambda$). For each model they also estimate the marginal density and select the model (ie the value of latex($\lambda$)) with highest marginal density. Dynare can also estimate directly latex($\lambda$) as another parameter, instead of doing a loop over the values of this parameter. So we define a prior on latex($\lambda$), which is assumed to be independent from latex($\theta$). Finally, the DSGE-VAR model has the following prior structure:

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where latex($p_0\left(\mathbf A, \Sigma \mid \theta ,\lambda \right)$) is defined by [P1a,P1b] and [P2].

The posterior distribution, may be factorized in the following way:

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where latex($\mathcal Y_T$) stands for the sample. A closed form expression for the first density function on the right hand side of [Q3] is available. Conditional on latex($\theta$) and latex($\lambda$), [P1a,P1b] and [P2] define a conjugate prior for the VAR model, so its posterior density belongs to the same family: the distribution of latex($\mathbf A$) conditional on latex($\Sigma$), latex($\theta$), latex($\lambda$) and the sample is matric-variate normal, and the distribution of latex($\Sigma$) conditional on latex($\theta$), latex($\lambda$) and the sample is inverted Wishart. More formally, we have:

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The posterior mean of latex($\mathbf A$) is a convex combination of latex($A^*(\theta)$), the prior mean, and of the OLS estimate of latex($\mathbf A$). When latex($\lambda$) goes to infinity the posterior mean shrinks towards the prior mean, ie the projection of the DSGE model onto the VAR(latex($p$)). We do not have a closed form expression for the joint posterior density of latex($\theta$) and latex($\lambda$) (the second term on the right hand side of [Q3]). So the posterior distribution of latex($(\theta,\lambda)$) is recovered from an MCMC algorithm, as described in Del Negro and Schorfheide, except that we do estimate latex($\lambda$) as the deep parameters latex($\theta$).

Example 1

References

DynareWiki: DsgeVar (last edited 2016-03-23 14:35:36 by StéphaneAdjemian)