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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Testing for neglected monetary effects on inflation

The ICM equation for aggregate consumer price inflation in Table 8.12 contains three key sources of inflation impulses to a small open economy: imported inflation including currency depreciation (a pass-through effect), domestic cost pressure (unit labour costs), and excess demand in the product market. Monetary shocks or financial market shocks may of course generate inflation
impulses in situations where they affect one or more of the variables associ­ated with these inflation channels. In this section, we will investigate another possibility, namely that shocks in monetary or financial variables have direct effects on inflation which have been neglected in the ICM...

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Summary and conclusions

The dominance of EqCMs over systems consisting of relationships between differenced variables (dVARs) relies on the assumption that the EqCM model coincides with the underlying data generating mechanism. However, that assumption is too strong to form the basis of practical forecasting. First, para­meter non-constancies, somewhere in the system, are almost certain to arise in the forecast period. The example in Section 11.2.1 demonstrated how allowance for non-constancies in the intercept of the cointegrating relations, or in the adjustment coefficients, make it impossible to assert the dominance of the EqCM over a dVAR...

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