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Table 5 The results of TVP-PVAR model

From: Is gold a safe haven for the dynamic risk of foreign exchange?

Parameter Mean SD 95% Interval CD Inefficiency
\({(}\sum \beta {)}_{{1}}\) 0.0023 0.0003 [0.0018, 0.0029] 0.366 5.20
\({(}\sum \beta {)}_{{2}}\) 0.0023 0.0003 [0.0018, 0.0029] 0.113 3.70
\({(}\sum a {)}_{{1}}\) 0.0056 0.0017 [0.0034, 0.0099] 0.796 15.53
\({(}\sum a {)}_{{2}}\) 0.0048 0.0024 [0.0031, 0.0079] 0.787 22.10
\({(}\sum h {)}_{{1}}\) 0.0054 0.0015 [0.0034, 0.0091] 0.516 13.45
\({(}\sum h {)}_{{2}}\) 0.0056 0.0016 [0.0034, 0.0097] 0.846 10.42
  1. Done on EViews 12
  2. In this study, we assume as a diagonal matrix for simplicity. Compared to the non-diagonal assumption, previous experiences of Primiceri [38] and Nakajima [34] indicate that this assumption is not sensitive to the results. The following priors are assumed for the i -th diagonals of the covariance matrices: \({(}\sum \beta {)}_{i}^{{ - 2}} \sim Gamma \, (40,0.02)\) \({(}\sum a {)}_{i}^{{ - 2}} \sim Gamma \, (40,0.02)\) \({(}\sum h {)}_{i}^{{ - 2}} \sim Gamma \, (40,0.02)\)
  3. For the initial state of the time-varying parameter, rather flat priors are set as \(\mu_{{\beta_{0} }} = \mu_{{a_{0} }} = \mu_{{h_{0} }} = 0\), and \(\sum {\beta_{0} } = \sum {a_{0} } = \sum {h_{0} } = 10 \times I\). To compute the posterior estimates, we draw M = 10,000 observations after the initial 1000 observations are discarded. The estimates of \(\sum \beta\) and \(\sum \alpha\) are multiplied by 100. SD = standard deviation; CD = convergence diagnostics statistics