Revisiting the Great Moderation using the Method of Indirect Inference

Table 13: `Taylor Rules' in the Data (with OLS): consistency with the estimated Timeless
Rule Model
Panel A: `Taylor Rules' in the Great Acceleration
`Taylor Rule' versions x Adj.R 2 Wald percentiles
~{ t = t + x x t +~{ t 1 + t 0.09 0.06 0.90 0.84 31.9
~{ t = t + x x t + t
t = t 1 +" t
0.30 0.07 0.92 0.85 69.1
~{ t = t 1 + x x t 1 + t 0.60 -0.01 n/a 0.24 36.9
~{ t = t 1 + x x t 1 +~{ t 1 + t -0.11 0.06 0.82 0.83 68.5
Panel B: `Taylor Rules' in the Great Moderation
`Taylor Rule' versions x Adj.R 2 Wald percentiles
~{ t = t + x x t +~{ t 1 + t 0.08 0.05 0.89 0.92 11.5
~{ t = t + x x t + t
t = t 1 +" t
0.07 0.06 0.93 0.90 95.6
~{ t = t 1 + x x t 1 + t 0.26 0.13 n/a 0.24 17.1
~{ t = t 1 + x x t 1 +~{ t 1 + t 0.03 0.04 0.89 0.91 89.7
biased by the non-stationarity of the interest rate. There is little statistical dierence in
the estimates across the two periods. The reported Wald percentiles indicate that these
empirical 'Taylor Rules' are indeed consistent with what the Timeless Rule implies: in
both panels the Taylor Rule regressions estimated are all within or on the 95% condence
bounds implied by the estimated Timeless Model.
This illustrates the identication problem with which we began this paper: a Taylor
Rule regression having a good t to the data may well be generated by a model where
there is no structural Taylor Rule at all. Here we suggest that the Timeless Rule model
we have found gets closest to tting US data in each episode is also generating these
Taylor Rule single-equation relationships.
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