Inflation equations derived from the P*-model

The P*-model is presented in Section 8.5.4. The basic variables of the model are calculated in much the same way for Norway as for the Euro area in the previous section. Figure 8.16 shows the price gap (p — p*)t and the real money gap (rm — rm*)t along with the corresponding level series using Norwegian data. The price gap is obtained from equation (8.16) after first applying the HP filter to calculate equilibria for output (y*) and velocity (v*), respectively. As for the Euro area we have used A = 1600 to smooth the output series y* and A = 400 to smooth velocity v*. Then p* can be calculated from (8.14), as well as the price – and real money gaps. It is easily seen from the figure that (p — p*)t = —(rm — rm*)t.


The reference path for money growth A4mt is calculated in a ...

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Forecast comparisons

Both models condition upon the rate of unemployment ut, average labour productivity at, import prices pit, and GDP mainland output yt. In order to investigate the dynamic forecasting properties we enlarge both models with relationships for these four variables, in the same manner as in Chapter 9.

Figure 11.5 illustrates how the ICM-based model forecast the growth rates of wages and prices, Awt and Apt. It is also instructive to consider the forecasts for the change in the real wage A(w—p)t and the annual rate of inflation, A4pt. The forecast period is from 1995(1) to 1996(4). The model parameters are estimated on a sample which ends in 1994(4). These dynamic forecasts are conditional on the actual values of the non-modelled variables (ex post forecasts)...

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The NPCM in Norway

Consider the NPCM (with forward term only) estimated on quarterly Norwegian data[65]:

Apt = 1.06 Apt+1 + 0.01 wst + 0.04 Apit + dummies (7.21)

(0.11) (0.02) (0.02)

x2(10) = 11.93[0.29].

The closed economy specification has been augmented heuristically with import price growth (Apit) and dummies for seasonal effects as well as special events in the economy described in Bardsen et al. (2002b). Estimation is by GMM for the period 1972(4)-2001(1). The instruments used (i. e. the variables in z1) are lagged wage growth (Awt-1, Awt-2), lagged inflation (Apt-1, Apt-2), lags of level and change in unemployment (ut-1, Aut-1, Aut-2), and changes in


Figure 7.2. Rolling coefficients ±2 standard errors of the NPCM, estimated on Norwegian data ending in 1993(4)-2000(4)...

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Credit expansion crt

The growth rate of real credit demand, Acrt, is sluggish, and it is also affected in the short run by income effects. In addition the equation contains a step dummy st for the abolition of currency controls (which again takes the value 1 after 1990(3) and (0) before) and a composite dummy variable

CRdumt = [0.5i85q3 + i85q4 + 0.5i86q1 + i87q1 + P dum]t

to account for the deregulation of financial markets.

Acrt = — 0.26 + 0.17Acrt_ i + 0.42Acrt_ 2 + 0.10Ayt (0.05) (0.06) (0.06) (0.02)

— 0.27ARLt_i — 0.026ecmcrt + 0.015CRdumt — 0.006st (9.11) (0.12) (0.005) (0.002) (0.002)

T = 1972(4)-2001(1) = 114

a = 0.61%

F ar(i-5)(5, 101) = 0.52[0.75] xLmality(2) = 0.06[0.97]

Fhetx2(13, 92) = 0.94[0.51].

(Reference: see Table 9.2. The numbers in [..] are p-values.)

The long-run properties are tho...

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The Incomplete Competition Model

The dynamic version of the ICM is presented in Chapters 5 and 6 and an example of empirical estimation is discussed in greater detail within the frame­work of a small econometric model for Norway in Chapter 9 (Section 9.2). We shall therefore be brief in the outline of the ICM for the Euro area; details are given in Jansen (2004).

The econometric approach follows a stepwise procedure, where the outcome can be seen as a product of interpretation and formal testing: we first consider an information set of wages, prices, and an appropriate selection of conditioning variables like the output gap, unemployment, productivity, import prices, etc. It turns out that the data rejects the long-run restrictions from theory in this case...

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RMSTEs and their decomposition

Table 10.2 shows the results from a series of counterfactual model simulations. For each interest rate rule we show the bias, standard deviation, and RMSTE measured relative to a baseline scenario. The baseline is the results we obtain for the variables from a model simulation where the interest rate is kept equal to actual sample values.[107]

Flexible and strict rules The least volatile development in interest rates is seen to follow from the strict targeting rule (ST). The sharp rise in output growth in 1997 is reflected in the volatility of the interest rates implied by the flexible rule (FLX) and the smoothing rule (SM). The FLX rule puts three times more weight on inflation than on output growth. Table 10...

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