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Table 1: FIML estimate of the econometric models Panel A: The US Panel B: Germany Parameter Estimate Std dev. t-statistic Estimate Std dev. t-statistic Phillips curve α π1 0.597 0.093 6.450 0.287 0.090 3.188 α π 2 0.079 0.105 0.751 0.394 0.083 4.772 α π 3 0.208 0.105 1.983 0.319 α π 4 0.116 α y 0.175 0.046 3.843 0.106 0.053 2.009 I-S curve β 0 (x100) 3.395 0.701 4.844 3.366 0.659 5.109 β y1 1.150 0.085 13.483 0.666 0.092 7.224 β y2-0.200 0.086-2.333 0.314 0.088 3.571 β ρ -0.348 0.215-1.618-0.508 0.335-1.518 Reaction function (1968:1-79:2) δ 0 (x100) 3.645 4.484 0.813-1.034 2.265-0.456 δ π 1 0.001 0.446 0.397 1.123 δ y 3.545 5.349 0.663 1.210 0.646 1.873 δ i 0.932 0.118 7.927 0.856 0.067 12.826 (1979:3-98:4) δ 0 (x100) 1.925 0.784 2.454 2.504 0.993 2.523 δ π 1 0.435 0.171 2.536 0.436 0.315 1.384 δ y 0.000 0.272 0.206 1.322 δ i 0.725 0.057 12.621 0.827 0.064 12.837 Likelihood -495.439-562.386 σ y σ ε LBc(8) (p-value) LM(8) (p-value) σ y σ ε LBc(8) (p-value) LM(8) (p-value) Phillips curve 2.519 3.366 13.519 2.233 7.655 7.808 1.077 (0.909) (0.095) 1.516 (0.468) (0.452) I-S curve 2.288 5.404 14.446 2.617 15.601 13.313 0.818 (0.714) (0.071) 1.134 (0.049) (0.102) Reaction function 1.990 12.441 19.738 2.894 14.400 6.511 (1968:1-79:2) 0.911 (0.133) (0.011) 1.230 (0.072) (0.590) Reaction function 3.426 11.418 58.061 2.550 16.328 20.424 (1979:3-98:4) 1.076 (0.179) (0.000) 0.673 (0.038) (0.009) Real rate Inflation (79:3-98:4) Nominal rate Real rate Inflation (79:3-98:4) Nominal rate Steady-state values 3.382 3.335 6.717 3.361 1.965 5.326 Note: LBc(8) is the Ljung-Box statistic, corrected for heteroskedasticity, obtained by regressing residuals on 8 lags. LM(8) is the Engle statistic for heteroskedasticity, obtained by regressing squared residuals on 8 lags. These statistics are distributed as χ 2 (8). Steady-state values are defined in Section 2.4.

Table 2: Stability tests The US Germany Panel A: Test for global stability p Sup-LR Avg-LR Exp-LR p Sup-LR Avg-LR Exp-LR Unknown date 12 48.89 a 22.09 a 20.20 a 11 27.47 19.97 b 11.42 b Break in 1979:3 12 39.85 a 11 23.72 b Panel B: Test for stability of policy-rule parameters p Sup-LR Avg-LR Exp-LR p Sup-LR Avg-LR Exp-LR Unknown date 4 22.05 a 5.74 6.88 a 4 22.45 a 11.28 a 8.14 a Break in 1979:3 4 15.22 a 4 8.01 Panel C: Test for stability of non-policy parameters p Sup-LR Avg-LR Exp-LR p Sup-LR Avg-LR Exp-LR Unknown date 8 24.71 b 12.37 10.13 b 7 20.11 10.85 6.88 Break in 1979:3 8 15.40 7 10.81 Note: Asymptotic critical values for the Sup-LR statistic are from Andrews (1993) and asymptotic critical values for the Exp-LR and the Avg-LR statistics are from Andrews and Ploberger (1994). p denotes the number of parameters allowed to shift at the break point. We assume that the break may occur over the subsample [π 0 T,(1-π 0 )T], with π 0 = 0.20. a and b denote that the statistic is significant at the 1 percent and 5 percent levels, respectively.

Table 3: Implied parameters for optimal monetary-policy rules using program (9) Weights in the Optimal parameter values Unconditional standard deviations loss function (λ;1 λ) δ π 1 δ y δ i σ π σ y σ i (k) Panel A: The US k=6 (0.00;1.00) 0.63 2.96 0.68 3.68 1.66 6.00 (0.50;0.50) 2.25 2.49 0.71 2.26 2.01 6.00 (1.00;0.00) 2.61 1.47 0.75 2.13 2.38 6.00 k=5 (0.00;1.00) 0.63 1.55 0.71 3.21 1.93 5.00 (0.50;0.50) 1.26 1.33 0.73 2.53 2.15 5.00 (1.00;0.00) 1.38 0.86 0.73 2.44 2.39 5.00 k=4.7 (0.00;1.00) 0.60 1.00 0.70 3.10 2.12 4.70 (0.50;0.50) 0.90 0.88 0.72 2.72 2.26 4.70 (1.00;0.00) 0.97 0.64 0.72 2.65 2.42 4.70 Model estimate 0.43 0.00 0.72 3.40 3.10 4.75 Panel B: Germany k=6 (0.00;1.00) 0.67 2.43 0.76 4.66 1.98 6.00 (0.50;0.50) 2.43 2.59 0.77 2.94 2.61 6.00 (1.00;0.00) 2.55 1.08 0.81 2.59 3.59 6.00 k=5 (0.00;1.00) 0.63 1.25 0.77 3.87 2.40 5.00 (0.50;0.50) 1.26 1.33 0.79 3.13 2.75 5.00 (1.00;0.00) 1.32 0.72 0.79 2.89 3.39 5.00 k=4.7 (0.00;1.00) 0.60 0.84 0.77 3.62 2.70 4.70 (0.50;0.50) 0.85 0.85 0.78 3.27 2.89 4.70 (1.00;0.00) 0.91 0.59 0.78 3.11 3.29 4.70 Model estimate 0.44 0.27 0.83 3.80 4.00 4.90

Table 4: Implied parameters for optimal monetary-policy rules using program (8) Weights in the Optimal parameter values Unconditional standard deviations loss function (µ π ; µ y ; 1 µ π µ y ) δ π 1 δ y δ i σ π σ y σ i Panel A: The US Inflation targeter (0.95;0.00;0.05) Output-gap targeter (0.00;0.95;0.05) Interest-rate targeter (0.00;0.00;1.00) Balanced preferences (0.35;0.35;0.30) 2.98 1.63 0.74 2.08 2.38 6.31 0.63 2.85 0.69 3.64 1.67 5.91 0.54 0.36 0.68 3.11 2.51 4.54 0.89 0.87 0.71 2.72 2.26 4.71 Model estimate 0.43 0.00 0.72 3.40 3.10 4.75 Panel B: Germany Inflation targeter (0.95;0.00;0.05) Output-gap targeter (0.00;0.95;0.05) Interest-rate targeter (0.00;0.00;1.00) Balanced preferences (0.35;0.35;0.30) 3.20 1.26 0.81 2.51 3.67 6.55 0.67 3.07 0.75 5.09 1.85 6.58 0.56 0.47 0.76 3.46 3.20 4.61 0.93 0.94 0.78 3.24 2.86 4.77 Model estimate 0.44 0.27 0.83 3.80 4.00 4.90

Table 5: Implied parameters for the German optimal monetary-policy rules for various values of α y, using program (9) Weights in the Optimal parameter values Unconditional standard deviations loss function (µ π ; µ y ; 1 µ π µ y ) δ π 1 δ y δ i σ π σ y σ i Model estimate 0.44 0.27 0.83 4.93 4.20 4.92 Optimal rule with the estimated nonpolicy parameters (α y =0.106) (0.00;0.70;0.30) 0.63 1.16 0.77 3.81 2.46 4.94 (0.35;0.35;0.30) 0.93 0.94 0.78 3.24 2.86 4.77 (0.70;0.00;0.30) 1.15 0.67 0.79 2.97 3.35 4.88 Optimal rule with α y =0.158 (0.00;0.70;0.30) 0.64 1.30 0.77 3.59 2.24 4.98 (0.35;0.35;0.30) 0.96 1.13 0.78 3.12 2.49 4.92 (0.70;0.00;0.30) 1.20 0.94 0.78 2.92 2.75 5.05 Optimal rule with α y =0.054 (0.00;0.70;0.30) 0.60 1.01 0.77 4.56 3.00 5.38 (0.35;0.35;0.30) 0.88 0.75 0.78 3.66 3.75 4.88 (0.70;0.00;0.30) 1.06 0.37 0.80 3.16 5.08 4.94

Table 6: Implied parameters for the German optimal monetary-policy rules for various values of β ρ, using program (9) Weights in the Optimal parameter values Unconditional standard deviations loss function (µ π ; µ y ; 1 µ π µ y ) δ π 1 δ y δ i σ π σ y σ i Model estimate 0.44 0.27 0.83 4.93 4.20 4.92 Optimal rule with the estimated nonpolicy parameters (β ρ =-0.508) (0.00;0.70;0.30) 0.63 1.16 0.77 3.81 2.46 4.94 (0.35;0.35;0.30) 0.93 0.94 0.78 3.24 2.86 4.77 (0.70;0.00;0.30) 1.15 0.67 0.79 2.97 3.35 4.88 Optimal rule with β ρ =-0.84 (0.00;0.70;0.30) 0.58 1.04 0.70 3.61 2.36 4.39 (0.35;0.35;0.30) 0.86 0.81 0.72 3.02 2.81 4.14 (0.70;0.00;0.30) 1.06 0.50 0.74 2.73 3.47 4.22 Optimal rule with β ρ =-0.17 (0.00;0.70;0.30) 0.73 1.49 0.86 4.53 2.75 6.80 (0.35;0.35;0.30) 1.08 1.36 0.87 3.94 3.00 6.79 (0.70;0.00;0.30) 1.35 1.20 0.87 3.67 3.24 6.97

Table 7: Implied parameters for the German optimal monetary-policy rules for various values of β y1 +β y2, using program (9) Weights in the Optimal parameter values Unconditional standard deviations loss function (µ π ; µ y ; 1 µ π µ y ) δ π 1 δ y δ i σ π σ y σ i Model estimate 0.44 0.27 0.83 4.93 4.20 4.92 Optimal rule with the estimated nonpolicy parameters (β y1 +β y2 =0.98) (0.00;0.70;0.30) 0.63 1.16 0.77 3.81 2.46 4.94 (0.35;0.35;0.30) 0.93 0.94 0.78 3.24 2.86 4.77 (0.70;0.00;0.30) 1.15 0.67 0.79 2.97 3.35 4.88 Optimal rule with β y1 +β y2 =1.05 (0.00;0.70;0.30) 0.71 1.53 0.77 3.93 2.50 5.39 (0.35;0.35;0.30) 1.05 1.35 0.78 3.34 2.86 5.26 (0.70;0.00;0.30) 1.32 1.12 0.79 3.07 3.27 5.41 Optimal rule with β y1 +β y2 =0.91 (0.00;0.70;0.30) 0.51 0.71 0.75 3.72 2.40 4.60 (0.35;0.35;0.30) 0.72 0.46 0.76 3.15 2.83 4.38 (0.70;0.00;0.30) 0.84 0.14 0.76 2.89 3.37 4.47

Fig. 1: Inflation rate, output gap and short-term interest rate The US 20 15 10 5 0-5 -10 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 Short-term interest rate Inflation rate Output gap Germany 20 15 10 5 0-5 -10 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 Short-term interest rate Inflation rate Output gap

Fig. 2a: The US optimal policy frontier 3.5 3.0 Model estimate (1979-98) (σ π=3.4, σ y =3.1, σ i =4.8) 2.5 2.0 k=4.7 k =5.0 k=6.0 1.5 2.0 2.5 3.0 3.5 4.0 Standard deviation of inflation Fig. 2b: The German optimal policy frontier 4.5 4.0 Model estimate (1979-98) (σ π=3.8, σ y =4.0, σ i =4.9) 3.5 3.0 k=4.7 2.5 k =5.0 2.0 k =6.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 Standard deviation of inflation

Fig. 3: Simulation of a temporary I-S shock under estimated rule (ER) and optimal rule (OR) The US 1.2 0.8 0.8 0.6 0.4 0.4 0.2 0.0 0.0-0.4-0.2-0.8 00 02 04 06 08 10 12 14 16 18 20-0.4 00 02 04 06 08 10 12 14 16 18 20 Output gap (ER) Output gap (OR) Inflation (ER) Inflation (OR) 1.2 0.25 0.20 0.8 0.4 0.0 0.15 0.10 0.05 0.00-0.05-0.4 00 02 04 06 08 10 12 14 16 18 20-0.10 00 02 04 06 08 10 12 14 16 18 20 Short nominal rate (ER) Short nominal rate (OR) Long real rate (ER) Long real rate (OR) Germany 1.0 0.4 0.8 0.3 0.6 0.2 0.4 0.2 0.1 0.0 0.0-0.2-0.1-0.4 00 02 04 06 08 10 12 14 16 18 20-0.2 00 02 04 06 08 10 12 14 16 18 20 Output gap (ER) Output gap (OR) Inflation (ER) Inflation (OR) 0.6 0.20 0.4 0.15 0.10 0.2 0.05 0.0 0.00-0.2 00 02 04 06 08 10 12 14 16 18 20-0.05 00 02 04 06 08 10 12 14 16 18 20 Short nominal rate (ER) Short nominal rate (OR) Long real rate (ER) Long real rate (OR)

Fig. 4: Shifts in German optimal policy frontier (with µ i =0.3) - Change in the sensitivity of inflation to movements in the output gap 6 5 4 Model estimate (1979-98) (σ π=3.8, σ y =4.0, σ i =4.9) 3 α y =0.054 α y =0.158 Estimated α y 2 2.5 3.0 3.5 4.0 4.5 5.0 Standard deviation of inflation

Fig. 5: Shifts in German optimal policy frontier (with µ i =0.3) - Change in the interestsensitivity of the I-S curve 4.5 4.0 Model estimate (1979-98) (σ π=3.8, σ y =4.0, σ i =4.9) 3.5 Estimated β ρ 3.0 β ρ = 0.17 2.5 β ρ = 0.84 2.0 2.5 3.0 3.5 4.0 4.5 5.0 Standard deviation of inflation

Fig. 6: Shifts in German optimal policy frontier (with µ i =0.3) - Change in the persistence of the output-gap equation 4.5 4.0 Model estimate (1979-98) (σ π=3.8, σ y =4.0, σ i =4.9) 3.5 β y1 +β y2 =0.91 β y1 +β y2 =1.05 3.0 2.5 Estimated β y1 and β y2 2.0 2.5 3.0 3.5 4.0 4.5 5.0 Standard deviation of inflation