Regarding own lags, the estimation results are largely consistent with the literature on
inflation persistence in the euro area (see e.g. Alvarez et al. (2006)) stemming from the
Eurosystems’ Inflation Persistence Network (IPN). Table 4 provides an overview over the
sum of the coefficients of the lagged dependent variable for each of the endogenous variable.
The sum of the coefficients is relatively small for PPI energy, suggesting that there is little
persistence in this component and prices change rather frequently. The persistence increases
at the later stages of the production, with services inflation having the highest persistence, i.e.
the lowest frequency of price changes. However, the estimation results suggest that PPI
intermediate goods inflation is of rather similar persistence as HICP non-energy industrial
goods inflation, somewhat in contrast to the IPN findings.
Table 4 Sum of lagged dependent variable from the panel estimation
|
Component |
Sum of coeff. |
Component |
Sum of coeff. |
|
PPI_ENE |
0.09 |
HICP_FDPR |
0.28 |
|
PPI_INT |
0.41 |
HICP_NEIG |
0.42 |
|
PPI_CONS |
0.15 |
HICP_SERV |
0.57 |
PPI_ENE: PPI energy; PPI_INT: PPI intermediate goods; PPI_CONS: PPI consumer goods; HICP_FDPR:
HICP processed food; HICP_NEIG: HICP non-energy industrial goods; HICP_SERV: HICP services.
We use the results to estimate the impact of shocks on the exogenous variables via the
individual price variables. To do so, we estimate the equations and forecast 16 quarters ahead
for all price variables, using the forecasted variables from earlier steps in the pricing chain to
forecast those later in the pricing chain and assuming no further changes in the exogenous
variables except the shocked variable over the forecast horizon. As a result, the effect of the
shocked variable is also indirectly transmitted via the pricing chain. As we are mainly
interested in the results for the euro area as a whole, we apply the coefficients estimated in the
panel of countries directly to euro area data.8 The resulting impact multipliers for an exchange
rate and commodity price change by 1% each are shown in Chart 5 to Chart 9.
Chart 5 shows the effect of a 1% appreciation of the nominal effective exchange rate on PPI
energy (PPENE), PPI intermediate goods (PPINT), PPI consumer goods (PPCONS) and, on
the right hand side, on processed food prices (CPFDPR), non-energy industrial goods prices
(CPNEIG), and services prices (CPSERV). Moreover, we show the weighted average of the
effect on processed food, non-energy industrial goods and services, i.e. the HICP excluding
unprocessed food and energy (CPEX). The result is strongest on PPI energy, with an impact
8 This approach is identical to using the weighted averages of country simulations, as we impose the
coefficients to be homogeneous across countries.
И ECB
Working Paper Series No 1104
November 2009