Back Matter

References

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1

The framework outlined here is being used by several IMF desk economists who meet regularly to share experiences, solve problems, and present results—Ricardo Adrogue, Zsofia Arvai, Roberto Benelli, Natan Epstein, Thomas Harjes, Ben Hunt, Jorge Canales Kriljenko, Irineu Evangelista de Carvalho Filho, Roberto Garcia-Saltos, Eva Jenkner, Meral Karasulu, Daniel Leigh, Rodolfo Luzio, Vincent Moissinac, Susanna Mursula, Papa N’Diaye, Anton Nakov, Hang Thi Thu Nguyen, Luca Ricci, Pau Rabanal, and Ivan Tchakarov. We would like to thank Alin Mirestean and Kexue Liu for providing support to new members of the team. We thank Jamie Armour, André Binette, and Patrick Perrier (Bank of Canada), and David Reifschneider (Federal Reserve Board) who generously shared data and simulation results. We also thank Shekhar Aiyar, Andrew Feltenstein, Charles Freedman, Peter Isard, Gian Maria Milesi-Ferretti, G. Russell Kincaid, Tohkir Mirzoev, and Carlo Sdralevich for their helpful comments on an earlier draft, Pille Snydstrup for editorial assistance, and Asmahan Bedri and Lei Lei Myaing for their work on the graphs and tables.

2

George E.P. Box and Norman R. Draper, “Empirical Model-Building and Response Surfaces” (Wiley 1987) pp. 424.

3

In a popular graduate textbook by Blanchard and Fischer (1989), the presentation of these types of models was relegated to the last chapter entitled “Some Useful Models.”

4

We have obviously not done justice to this story here. Recent surveys include Clarida, Gertler and Gali (1999), Lane (2001), and Woodford (2003a).

5

For a description about the role of judgment in the macro forecasts that are used in central banks see Sims (2002) and Svennson and Tetlow (2005). Romer and Romer (2000) and Sims (2002) show that the judgemental forecasts made at the Fed are better than pure model-based forecasts.

6

The degree of complexity and sophistication of macroeconomic models varies considerably across central banks. Models broadly along the lines of that presented here have often formed the basis of successful modeling efforts. Several institutions are building a new generation of core workhorse models with stronger choice-theoretic foundations. However, it is wise to start with simpler models to begin with and then develop more sophisticated systems over time—see Laxton and Scott (2000).

7

Laxton and Scott (2000) and Canales-Kriljenko and others (2005) discuss the role of models as well as the full range of related issues in operationalizing an inflation targeting framework.

9

The two papers overlap substantially. This paper targets mainly newcomers to modern structural macroeconomic modeling and potential consumers. The “how-to-guide” contains more nuts-and bolts details for modelers. It thus abbreviates the material contained in sections II and IIIA of this paper and expands on the rest.

10

IMF (2004a) provides a recent official description and assessment of financial programming, emphasizing its usefulness as an organizing framework and the need to supplement it with other tools in particular circumstances.

12

Leeper and Roush (2003) also make this point and cite as examples Romer (2000), Stiglitz and Walsh (2002), and Woodford (2003a). Clarida, Gali and Gertler (1999) estimate monetary policy reaction functions for several countries and argue that even avowed money targeters such as the Bundesbank did not in fact practice targeting monetary aggregates. Stone and Bhundia (2004) survey large and more developed countries (75-85 countries) and point out that money targets were used by only five countries in 1990, and by 2000 this regime became extinct.

13

IMF (2004a) provides an in-depth discussion of analytic frameworks and program design in Fund-supported programs. It notes the usefulness of financial programming as an organizing framework as well as the need to supplement it with other tools in particular circumstances. IMF (2004b) analyzes the experience with monetary aggregates in Fund-supported programs. It concludes that the high correlation between monetary aggregates and inflation reaffirms the importance of nominal anchors for controlling inflation. Berg and others (2003) discuss monetary policy in post-crisis situations and argue that, even in those cases, there is little practical role for monetary aggregates in the assessment of monetary policy in practice.

14

The question of the role of financial programming in Fund conditionality is a related but distinct question that is outside the scope of this paper. In IMF programs, the value of traditional monetary aggregate conditionality resides partly in its “safety” features. In this case, the monetary aggregate targets are not meant to structure thinking or policy with respect to “everyday” monetary policy choices but rather to identify gross breaches of the policy. Blejer and others (2001) discuss the difficulties involved in applying standard Fund quantitative performance criteria for monetary policy to countries that follow an inflation-targeting regime.

15

Bofinger and Schmidt (2004) rely on the anchoring heuristic to explain the otherwise puzzling tendency of professional foreign exchange forecasters to perform substantially worse than a naïve random walk and, in particular, to pay too much attention to actual changes in the exchange rate.

16

Garratt and others (2003) estimate a small quarterly model for the United Kingdom.

18

The IMF’s Global Economy Model (GEM), described in Laxton and Pesenti (2003), for example, represents such a model. The set of tools that we rely upon for our simple model, described in an Appendix in the companion paper, are similar to those required to work with GEM.

19

Coats, Laxton and Rose (2003) describe how such an approach is practiced in the Czech Republic, based on a core model along the lines described in this paper. The IMF has developed a multi-country, new-open-economy macro model to study the medium-term and long-term implications of fiscal policies. See Botman and others (2006).

20

We say “consistently” because it may be appropriate to extend the model to incorporate an element of adaptive expectations.

21

See Gali and Monacelli (2002) and Monacelli (2004) for micro-founded models along the lines of that discussed here (pg. 112).

22

Leeper (2003), page 112, uses this terminology to describe the state of the art in model building in central banks.

23

For an accessible introduction to this exploding literature, see for example Clarida, Gali, and Gertler (1999). For a recent overview, see Woodford (2003a).

24

The companion paper describes the model and in particular the supply side in somewhat more detail.

25

The proposed parameter values in this section are based on experience with this type of model in a number of countries. Many have been implemented at central banks and are unpublished; see however Coats, Laxton and Rose (2003) for an application to the Czech Republic.

26

Inflation is measured as the annualized quarterly change, in percent, so πt = 400[log(cpit) − log(cpit–1)]. π4t is the Four-quarter change in the CPI, in other words π4t = 100[log(cpit) − log(cpit–4)].

27

Many factors impact on the exchange rate pass-through and its determinants, including central bank credibility, the composition of trade, the importance of distribution costs, the nature of shocks, and the degree of monopoly power.

28

Thus, any deviation of interest rates from equilibrium either at home or abroad should result in the exchange rate deviating from equilibrium, unless such rate deviations were identical. Any other movement in exchange rates is captured by the residual in the exchange rate equation, which can be thought of as a temporary shock to the risk premium.

29

If the model as outlined in this section is used to generate artificial data, and the IP equation is estimated with OLS on this artificial data, the estimated coefficient on the interest rate differential is likely to be zero or even negative. See Chin and Meredith (1998) for an example.

30

The value of δz matters for forecasting and policy analysis. When δz = 1, the real exchange rate will be a function of the future sum of real interest differentials (and risk premia) and will provide a direct and rapid channel through which monetary policy will operate. Some policymakers have argued that more robust policies should assume a much smaller value of δz because it may be imprudent to rely so heavily on these forward-looking linkages in the face of uncertainty. Isard and Laxton (2000) show that under uncertainty about the value of δz, it will be prudent to assume that δz is slightly below 0.5 because of larger and asymmetric costs that would result from assuming extreme values such as zero or one.

31

For a brief introduction to the vast literature evaluating alternative monetary policy rules see Hunt and Orr (1999) and Taylor (1999).

32

This restriction, which is necessary to provide an anchor for the system, has come to be known as the Taylor principle, after John Taylor who popularized the idea of using interest rate reaction functions as guidelines for evaluating the stance of monetary policy. The original 1993 Taylor rule imposed a zero weight on interest rate smoothing (γRSlag = 0) and implied a γπ (a backward-looking, year-on-year measure of inflation in that case) of 0.5 and a value for γygap of 0.5. After specifying a loss function it is straightforward technically either to optimize the parameters in a simple rule or to compute the path of interest rates with optimal control techniques—see Laxton and Pesenti (2003) and Svensson and Tetlow (2005) for examples of both approaches.

33

The analyst could create a loss function, for example one that depends on the variance of output and inflation and possibly interest rates and then simulate the model to determine how, in the face of a given pattern of shocks, a particular rule performs.

34

For a recent review of interest rate rules for developing economies, see IMF Research Bulletin, June 2005, Volume 6, Number 2.

36

Parrado (2004) estimates a reaction function for Singapore in which the instrument is the change in the exchange rate. It would in principle also be possible to extend the model to a situation in which a monetary aggregate serves as the instrument, though as discussed above substantial consideration should be given to the question of whether this is a sensible or realistic reaction function. Alternatively, if monetary aggregates carry information about future inflation not otherwise captured in the model, they could be included in the inflation equation; the authorities would then react to monetary aggregates through their effect on expected inflation.

37

Many models have interesting treatments of the supply side and address these issues. The IMF’s GEM represents one approach.

38

If the central bank smoothes interest rates, then there are some transitional dynamics until the nominal rate adjusts to the new equilibrium.

39

Experience with use within the IMF to date suggests that, where the authorities are willing and able to discuss their own views on the properties of the economy and of their own models, the process of using the model to solicit the judgment of policymakers has worked particularly well.

40

Of course, an enormous amount of research is now directed at improving the microeconomic foundations of these sorts of models to make them more consistent with the inertia in the data. It is likely that, over time, reference to larger micro-founded structural models will become a more important part of designing and calibrating smaller policy models.

41

The Orphanides (2003) monetary policy reaction function is implemented as an option in the example program discussed below.

42

Schmidt-Hebbel and Tapia (2002) have compiled views about the monetary policy transmission mechanism and other features of the economy from twenty central banks.

43

For a discussion of estimation issues of models designed for monetary policy analysis, see Coletti and others (1996), Hunt, Rose, and Scott (2000), Benes and others (2003), Faust and Whiteman (1997), and Kapetanios, Pagan and Scott (2005). For a critical assessment of approaches that are based excessively on letting the data speak for designing policy models see Coletti and others (1996), Faust and Whiteman (1997), Hunt, Rose, and Scott (2000) and Coats, Laxton and Rose (2003).

44

These Bayesian techniques can be thought of as a more formal version of the calibration/parameterization method described here. An Appendix in the companion paper refers to details of a particular software called DYNARE (Juillard, 2004) which presents tools these techniques.

45

We have developed examples of programs for these types of models and shared them with desk economists. For example, Hunt, Tchaidze, and Westin (2005) estimate the model discussed in this paper in the case of Iceland using Bayesian techniques. See Smets and Wouters (2004) and Juillard and others (2005) for other recent applications to DSGE macroeconomic models.

46

The companion paper (Berg, Karam, and Laxton 2006b) contains a similar but somewhat more detailed version of this section.

47

The companion paper goes into substantially more detail. A more extensive example of the implementation of a similar model can be found in Coats, Laxton and Rose (2003).

48

This very simple specification roughly mimics the properties of the GEM and other models like FRB-US that model oil as a factor of production. See Hunt (2005).

49

We had to add a measure of core inflation to the model because the Bank of Canada’s Quarterly Projection Model (QPM) uses this as its key inflation variable, and we wanted to do some comparisons across the two models.

50

Obviously, there is considerable uncertainty about these estimates. The implications of different estimates for the output gap can be easily and quickly analyzed, however.

51

For the model of the Canadian economy we mainly examined the simulation properties of the Bank of Canada’s QPM model, although we also looked at the properties of a model called NAOMI developed by Steve Murchison then at the Department of Finance, Canada. See Coletti and others (1996) and Murchison (2001). For the model of the U.S. economy, we looked at the properties of the Fed’s FRB-US model. Good overviews of the structure and properties of FRB/US can be found in Reifschneider, Tetlow and Williams (1999) and Brayton and others (1997), but for a more complete description of the FRB-US model see Brayton and Tinsley (1996).

52

The sacrifice ratio is defined as the cumulative output losses associated with a permanent one percentage point decline in inflation. In quarterly models, this is computed by doing an experiment where the inflation target is reduced by one percentage point forever and then cumulating the effects on the annual output gap.

53

There are two additional reasons why inflation may be more responsive now to current and future output gaps. One is that the level of competition has risen over time in both the labor market and product market. As shown in Bayoumi, Laxton and Pesenti (2004), in the Fund’s Global Economy Model this will work to reduce the sacrifice ratio and increase the sensitivity of inflation to current and future output gaps. Second, the weight on the forward-looking inflation terms in the inflation equation may have increased, which will have a similar type of effect. For some empirical evidence on the latter for the United States, see Bayoumi and Sgherri (2004).

54

We emphasize “implicit” in the sense that the desk has not used SMPMOD to arrive at her forecasts. But had SMPMOD been used, this is what the residuals would have implied.

55

This is consistent with results reported in Schmidt, Hebbel, and Tapia (2002), where inflation response is slower than the output response, as many channels of monetary policy transmission to inflation go through the expenditure and the output gap.

56

See Coats, Laxton and Rose (2003) for a similar model with term structure, administratively set prices, richer expectation dynamics and unemployment, for example.

57

Hunt, Tchaidze, and Westin (2005) analyze the optimal monetary policy reaction function in this framework for Iceland.

58

A number of country teams are now exploring the application of the framework to emerging market economies. For example, Epstein and others (2006) apply the framework to Israel. The model could be a useful addition to the toolkit in the IMF’s analysis of advanced economies, however, as even here there is no systematic use of modern modeling techniques.

A Practical Model-Based Approach to Monetary Policy Analysis—Overview
Author: Mr. Douglas Laxton, Mr. Andrew Berg, and Mr. Philippe D Karam