Revisiting the Great Moderation using the Method of Indirect Inference

Table 3: Performance of the Optimal Timeless Rule Model in the Great Moderation
Panel A: Directed Wald- data dynamics
VAR(1) 95% 95% Values estimated In/Out
coecients lower bound upper bound with real data
11 0.7408 0.9689 0.8950 In
12 -0.0316 0.0329 0.0395 Out
13 -0.0709 0.0896 0.0315 In
21 -0.2618 0.8132 -4.28e-05 In
22 0.4102 0.7617 0.8243 Out
23 -0.3954 0.3056 -0.0657 In
31 -0.3197 0.2122 0.0105 In
32 0.0050 0.1735 0.0979 In
33 0.1090 0.5052 0.2353 In
Directed Wald percentile
for dynamics
86.4
Panel B: Directed Wald- data volatilities
Volatilities of 95% 95% Values calculated In/Out
endogenous variables lower bound upper bound with real data
V ar(~{) 0.0042 0.0264 0.0156 In
V ar(x) 0.0686 0.1627 0.1620 In
V ar() 0.0095 0.0204 0.0149 In
Directed Wald percentile
for volatilities
89.6
Note: Estimates reported in panel B are magnied by 1000 times as their original values.
Panel C: Full Wald statistic
Chosen data features
Full Wald percentile
Dynamics+Volatilities 77.1
95% upper bound, the test returns a directed Wald percentile of 86.4. This means at
95% (or even at 90%) condence level the real-data-based estimates are easily explained
by their joint distribution generated from model simulations, indicating that the model
has in general captured the dynamic features of the data pretty precisely.
Panel B then examines the model's capacity to explain the data's volatility. It shows
the observed data variances not only lie individually within the 95% bounds but are also
jointly explained by the model at the 95% level (indeed, also marginally at 90%), since
the directed Wald is 89.6. Thus compared to the data the Timeless Rule model is also
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