# Probabilistic Approach to Assessing Macroeconomic Uncertainties

PRAMU

## Inflation uncertainty for Japan

Posted on: June 3rd, 2015 by pramu No Comments

# Inflation uncertainty for Japan

1. Figure 1. Minimum distance characteristics of fitted TPN and WSN distributions to U-uncertainties

WSN fits better than TPN, especially for medium and long horizons.

2. Figure 2. Term structure of the compound strength of forecast-induced monetary policy

The compound strength is computed as: $Strength=|alpha|cdot D_m+|beta|cdot D_k$, where $D_a=int_{|a|}^infty t^2varphi(t)dt=1-Phi(|a|)+|a|varphi(a)$. In the policy-input symmetric case, where $m=-k$ (and this is the assumption applied for empirical estimation), the multipliers $D_a$ can be ignored.

3. Table 1. Forecast uncertainty measures

h th.uncert RMSE(u) std(v) UR NUR
1 0.0294 3.45 3.08 1.61 0.71
2 0.0658 3.57 3.13 1.55 0.685
3 0.11 5.5 5.51 2.03 0.895
4 0.161 6.09 6.15 2.07 0.912
5 0.218 6.48 6.3 1.91 0.843
6 0.281 6.88 6.76 1.95 0.862
7 0.348 7.79 7.58 1.91 0.844
8 0.42 8.74 8.45 1.89 0.835
9 0.497 9.7 9.85 2.09 0.922
10 0.577 9.98 9.72 1.92 0.847
11 0.662 9.88 9.47 1.86 0.821
12 0.75 12.6 12.6 2.02 0.893
13 0.817 11.5 11.7 2.1 0.927
14 0.868 1 1 2.02 0.893
15 0.907 1 1 2.02 0.893
16 0.937 1 1 2.02 0.893
17 0.96 1 1 2.02 0.893
18 0.976 1 1 2.02 0.893
19 0.987 1 1 2.02 0.893
20 0.994 1 1 2.02 0.893
21 0.998 1 1 2.02 0.893
22 1 1 1 2.02 0.893
23 1 1 1 2.02 0.893
24 1 1 1 2.02 0.893

4. Figure 3. Ex-post and ex-ante inflation fan charts

 Ex-post uncertainty Ex-ante uncertainty