The transmission mechanism (Chapters 9 and 10)

All macroeconometric models contain a quantitative picture of how changes in nominal variables bring about real effects, the so-called transmission mech­anism. Sometimes representations of the transmission mechanism are the main objective of the whole modelling exercise, as when central banks seek to under­stand (and to convey to the public) how changes in the nominal interest rate affect real variables like the GDP growth rate and the rate of unemployment, and through them, the rate of inflation. Clearly, the wage and price submodel is one key element in the model of the transmission mechanism.

In modern economies, the transmission mechanism can be seen as a complex system where different groups of agents interact through markets which are often strongly interlinked, and an attractive feature of a macroeconomic model is that it represents the different linkages in a consistent framework. As an example, we take a closer look at the transmission mechanism of the medium term macroeconomic model, RIMINI.

By Norwegian standards, RIMINI is an aggregated macroeconometric model.[4] The core consists of some 30 important stochastic equations, and there are about 100 exogenous variables which must be projected by the forecaster. Such projections involve judgements, and they are best made manually based on information from a wide set of sources. The model should be run repeatedly to check for consistency between the exogenous assumptions and the results before one arrives at a baseline forecast. In this way the model serves as a tool taking account of international business cycle development, government policy, and market information, for example, forward market interest rates.

The RIMINI is a fairly closed model in the sense that the most important variables for the Norwegian economy are determined by the model, while the model conditions upon ‘outside’ variables like foreign prices and output and domestic policy variables like interest rates and tax rates. The model distin­guishes between five production sectors. The oil and shipping sectors are not modelled econometrically, nor is the sector for agriculture, forestry, and fishing. The two main sectors for which there exist complete submodels are manufactur­ing and construction (traded goods) and services and retail trade (non-traded goods). There are reasons to expect important differences in, for instance, the responses to changes in interest rates and exchange rates between traded and non-traded goods.

In RIMINI there are two main channels through which monetary policy instruments affect employment, output, and prices—the interest rate channel and the exchange rate channel. For the first channel—the effect of the inter­est rate—Figure 1.1 shows the roles of households and enterprises in RIMINI and also the main interaction between the demand-side (upper shaded box) and the supply-side (lower shaded box). The main point here is to illustrate the complexity and interdependencies that are typical of macroeconometric systems.

Assuming fixed exchange rates, an increase in the central bank interest rate for loans to the banks (the signal rate) immediately affects the money market interest rate. The money market rate in turn feeds into the deposit and lending rates of commercial and savings banks with a lag. Aggregate demand is affected through several mechanisms: there is a negative effect on housing prices (for a given stock of housing capital), which causes real household wealth to decline, thus suppressing total consumer expenditure. Also, there are nega­tive direct and indirect effects on real investment in the traded and non-traded sectors and on housing investment.

CPI inflation is reduced after a lag, mainly through the effects from changes in aggregate demand on aggregate output and employment, but also from changes in unit labour costs. Notably, productivity first decreases and then increases—due to temporary labour hoarding—to create a cyclical pattern in the effects of the change in the interest rate.

An appreciation of the Krone has a more direct effect on CPI inflation compared to the interest rate. As illustrated by the upper left box in Figure 1.2, it mainly works through reduced import prices with a lagged response which entails a complete pass-through to import and export prices after about 2 years. The model specification is consistent with a constant markup on unit labour costs in the long run. A currency appreciation has a negative effect on the


Figure 1.1. Interest rate channels in RIMINI. Given constant exchange rates

demand for traded goods. The direct effects are not of a large magnitude, because there are small relative price elasticities in the export equations and secondly because export prices (in local currency) adjust with a lag and tend to restore the relative prices. However, there are also important feedback mech­anisms as the decrease in the price level caused by the appreciation feeds back into aggregate demand from domestic sectors.

If we abandon the assumption of a fixed exchange rate, an increase of interest rates affects the money market rate and this induces an appreciation of the Krone. Hence, we obtain the combined effect of an interest rate increase


Figure 1.2. Exchange rate channels in RIMINI. Given constant interest rates

through both channels and the exchange rate channel strengthens the effect of interest rate changes on the rate of inflation. This will be analysed further in Section 9.5 in the context of the small macroeconometric model for Norway, which, as we alluded to in Section 1.2, shares many properties of the full RIMINI model.

This brief presentation of the transmission mechanism of an operational model also serves to demonstrate the complexity and interdependencies of an operational macroeconometric model. Again, it is evident that such a model is too big and complex to be formulated in one step, or to be estimated simultan­eously. Thus, there is a need to deal with subsectors of the economy—that is, we try to make sense out of bits and pieces rather than handling a complete model. The modelling of subsystems implies making simplifications of the joint distri­bution of all observable variables in the model through sequential conditioning and marginalisations, as discussed in Section 2.3.

The estimated model in Chapter 9 is based on the assumption that the short-run interest rate is an exogenous policy variable, and the chapter high­lights estimation results and model properties along with a discussion about the model’s potential to address monetary policy issues which are at the forefront of inflation targeting central banks. Inflation targeting means that the policy instrument (the interest rate) is set with the aim of controlling the conditional forecast of inflation 2-3 years ahead. In practice, this means that central bank economists will need to form a clear opinion about how the inflation fore­casts are affected by different future interest rate paths, which in turn amounts to quantitative knowledge of the transmission mechanism in the new regime. The main monetary policy channels in the small macroeconometric model are discussed on the basis of an analysis of dynamic multipliers.

In Chapter 10, we relax the assumption that the short-run interest rate is exogenous. We evaluate the performance of different types of reaction func­tions or Taylor-type interest rate rules. We perform counterfactual simulations over the period from 1995q1 to 2000q4. In addition to analysing the outcome from employing standard Taylor-type rules, including rules with interest rate smoothing, we also employ inter alia interest rate rules dubbed ‘real time’ rules since they are based on variables less prone to measurement errors, and ‘open economy’ rules which allow for interest rate responses to exchange rate misalignments. The performance of the employed rules is evaluated by standard efficiency measures and by deriving the mean deviations from targets, which may be of interest for policy makers, especially over short time horizons. We also introduce the root mean squared target error (RMSTE), an analogue to the well-known root mean squared forecast error. Finally we conduct simulation experiments where we vary the weights in the interest rate rules as well as the weights of the variables in the policy maker’s loss function. The results are sum­marised by estimating response surfaces on the basis of the range of weights considered in the simulations. We will assume that monetary policy rules aim at stabilising inflation around the inflation target, and that the monetary author­ities potentially put some weight also on the stabilisation of unemployment, output, and interest rates. The performance of different monetary policy rules can then be evaluated on the basis of the monetary authorities’ loss function.

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