In this section, we present estimated reduced form versions of the AWM and ICM inflation equations in order to evaluate the models and to compare forecasts based on these equations with forecasts from the inflation models referred to in Section 8.5, that is, the P*-model and the NPCM. The models are estimated on a common sample covering 1972(4)-2000(3), and they are presented in turn below, whereas data sources and variable definitions are found in Jansen (2004).
8.6.1 The reduced form AWM inflation equation
We establish the reduced form inflation equation from the AWM by combining the wage and price equations of the AWM (see appendix B of Jansen 2004)... Read More
The non-stationary nature of many economic time series has a bearing on virtually all aspects of econometrics, including forecasting. Recent developments in forecasting theory have taken this into account, and provide a, framework for understanding typical findings in forecast evaluations: for example, why certain types of models are more prone to forecast failure than others. In this chapter we discuss the sources of forecast failure most likely to occur in practice, and we compare the forecasts of a large econometric forecasting model with the forecasts stemming from simpler forecasting systems, such as dVARs... Read More
In this chapter, we develop an econometric model for forecasting of inflation in Norway, an economy that recently opted for inflation targeting. We illustrate the estimation methodology advocated earlier, by estimating and evaluating a model of prices, wages, output, unemployment, the exchange rate, and interest rates on government bonds and bank loans. The model is built up sequentially. We partition the simultaneous distribution function into a small model of wages and prices, and several marginal models for the rest of the economy. The choice of model framework for the wage and price model follows from the analysis in earlier chapters. We use the model to analyse the transmission mechanism and to address monetary policy issues related to inflation targeting.
On 29 M... Read More
Finally, we shall take a look at this very elegant method introduced by Sargent. It consists of the following steps:
1. Write the model in terms of lead – and lag-polynomials in expectations.
2. Factor the polynomials, into one-order polynomials, deriving the roots.
3. Invert the factored one-order polynomials into the directions of converging forward polynomials of expectations.
Again, we use the simplifying definition
zt = bp2Xt + £pt:
so the model is again
Apt = bp1EtApt+i + bpiApt-i + zt.
Note that the forward, or lead, operator, F, and lag operator, L, only work on the variables and not expectations, so:
LEtzt = Etzt-i
FEtZt = Etzt+i L-i = F.
The model can then be written in terms of expectations as:
-bpiEtApt+i + EtApt – bpEApt-i = EtZt, and using the lead – and lag-operators:
(-bpiF... Read More
In this section, we discuss the dynamic properties of the full model. In the
simulations of the effects of an increase in the interest rate below we have not
Figure 9.9. Accumulated responses of some important variables to a 1 per
cent permanent increase in the interest rate RSt
incorporated the non-linear effect in the unemployment equation. Hence the results should be interpreted as showing the impact of monetary policy when the initial level of unemployment is so far away from the threshold value that the non-linear effect will not be triggered by the change in policy.
Figure 9.9 shows the simulated responses to a permanent rise in the interest rate RSt by 100 basis points, that is, by 0.01, as of 1994(1)... Read More