Back Matter
  • 1 https://isni.org/isni/0000000404811396, International Monetary Fund
  • | 2 https://isni.org/isni/0000000404811396, International Monetary Fund
  • | 3 https://isni.org/isni/0000000404811396, International Monetary Fund

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1

We thank Aleš Bulíř for his suggestions on the topics considered in the paper as well as Jan Syrovátka, Luboš Růžička, and Rita Choi for data inputs and excellent research assistance.

2

For a discussion of the principles and applications of IFT, see Clinton and others (2015), Alichi and others (2015a, b), Arbatli and others (2016), and Obstfeld and others (2016).

3

Freedman and Laxton (2009) discuss this issue in more depth.

4

Thus, Woodford (2005) highlights management of expectations as a key task in the practice of central banking, and Svensson (2007) argues for publishing the central bank’s forecast interest rate path. See also Clinton and others (2015) for a discussion of the practical issues involved with developing analytical frameworks and monetary policy models to support IFT regimes.

6

See Alichi and others (2015b) for a comparison of monetary policy reaction functions and the direct minimization of a loss function to more efficiently manage the short-run output-inflation trade-off.

7

See Clinton and others (2015) and references therein for a discussion of inflation-forecast-based (IFB) reaction functions and their use in monetary policy models developed in IFT central banks.

8

The frequency of policy meetings varies across central banks, for example: 6 per year at the Norges Bank and Swedish Riksbank; 8 at the Bank of Canada, the Czech National Bank, the Reserve Bank of New Zealand, the Bank of England, the European Central Bank, and the U.S. Federal Reserve; 12 at the Central Bank of Chile.

9

The short-term rate in the model need not be the actual rate announced by the central bank. The former should apply to a wide range of borrowing costs in the financial market (e.g., a 3-month rate), whereas the latter is usually a very short-term rate (e.g., an overnight repo rate) on central bank facilities. In practice, the announced policy rate has a heavy influence on all short-term market rates.

10

The focus here is on the interest rate, but of course the model also produces a consistent forecast for the whole range of relevant endogenous macroeconomic variables.

11

See Clinton and others (2015), Argov and others (2007), Coats, Laxton and Rose (2003), and Hunt, Rose and Scott (2008) for a discussion of these issues.

12

See, for example, Poloz (2014). In the past the Bank of Canada did not publish the path of the policy rate path, but used words to describe the policy assumptions in their output and inflation forecasts. For example, in the MPR from April 2013 they described the policy assumptions as “… This projection includes a gradual reduction in monetary stimulus over the projection horizon, consistent with achieving the inflation target.”

13

Czech National Bank, Inflation Report I/2008.

14

For example, see the statement by the Governor of the Riksbank, Ingves (2007). The absence of any particular problem for these central banks suggests that they are very effective communicators, or that the fear of a perceived commitment to an interest rate forecast was overblown.

15

The decision to start inflation targeting was actually taken by the Board in December 1997, effective from January 1998.

16

See Isard and Laxton (2000) and Laxton and Scott (2000) for an initial discussion of the role of models to support IFT, as well as Laxton, Rose and Scott (2009) for an update.

17

The CNB was introduced to DSGE models by IMF technical assistance (TA) and, in fact, the model used on the TA mission was one of the first DSGE models ever developed in a policymaking institution (for a description of the calibration to the Czech Republic see Laxton and Pesenti, 2003). However, it is important to emphasize that the GPM-g3 model was developed from scratch by modelers at the CNB and was the first DSGE model to be used as a core quarterly projection model in an IFT central bank. The developers of the Czech models have provided extensive technical assistance to other central banks working on developing IFT frameworks.

19

The members of the modeling and projection team generate baseline scenarios using the QPM-g3 and are also responsible, on a rotating basis, for model development. The CNB’s forecasting team is a considerably larger group of staff—the modeling and projection team is a subset of it. The forecasting team includes management of the Monetary Department and all the technically-oriented staff responsible for the analysis and forecasts of particular sectors (external and domestic economy, financial sector issues, fiscal policy, etc.). This also includes staff responsible for the CNB’s communications of the forecast and monetary policy.

20

See CNB’s Inflation Report I/2010 Box 2 for a description of the various methods used by the bank and the uncertainties surrounding the calculation of potential output.

21

For example, very critical remarks can be found in the transcript for the 30 October 2003 Board meeting (available in Czech language only): http://www.cnb.cz/miranda2/export/sites/www.cnb.cz/cs/menova_politika/br_zapisy_z_jednani/2003/2003_10_30/pt_10_SZ_30_10_03.pdf.

22

See the transcript of the 29 April 2004 meeting, which is unfortunately also available in the Czech only: http://www.cnb.cz/miranda2/export/sites/www.cnb.cz/cs/menova_politika/br_zapisy_z_jednani/2004/2004_04_29/pt_04_SZ_29_04_04.pdf. Already its first sentence by one of the Vice-Governors is revealing in this regard: “Abrupt swings in the opinion on the economy are inappropriate.”

23

Antoničová and others (2008) claim that: “In terms of the size of the integrated forecast error, the April 2004 and October 2005 forecasts can be regarded as the least successful.”

24

Useful software today includes MATLAB, IRIS, DYNARE, SIRIUS, and PYTHON. Model-related technical knowledge covers univariate and multivariate filtering (HP, bandpass, and Kalman) as well as solution methods for solving linear and nonlinear dynamic stochastic general equilibrium models. For useful examples of programs using these software see www.douglaslaxton.org.

25

See Coats, Laxton and Rose (2003) for documentation of the initial FPAS developed at the CNB.

26

The time pressure on staff can be reduced by automating regular processes, including data management, production of charts, tables and presentations, etc. This requires a significant investment of time by technically-skilled staff initially, but pays off in the longer run in terms of avoiding laborious activities and reducing the risk of human error.

27

Each quarter CNB staff does a forecast evaluation, based on a detailed model-consistent analysis of the factors contributing to forecast errors. The results of these evaluations are presented to the policymakers. They help identify priority areas for model improvement.

28

The modeling and projections team have grown over time to six economists. The enlargement of the team made it possible to create a rotational system composed of two 3-member teams, which rotate on an annual basis between forecasting and model development. During each calendar year one of the 3-member teams is responsible for the forecast and the other team does model development and economic research to support the FPAS.

29

During the initial period of the Great Recession the policymakers were concerned with the role of financial frictions in the transmission mechanism. In order to test the forecasting model for the presence (and magnitude) of shocks originated from the financial sector, the model was extended to include a financial block. This work was carried out by those members of the FT who were not responsible for forecasting at the time.

30

The loss function assigns a weight of 1, 1, and 0.5 on the squared deviation of inflation from its target, output gap, and change in the policy rate, respectively.

31

Alichi and others (2009) present a model in which people are initially undecided as to whether monetary policy will adhere to a new announced low-inflation target, or revert to a previous policy of high inflation. In their expectations of future inflation, some weight attaches both to the new target and the old high rate. Over time, the central bank builds credibility by keeping inflation low: the weight on the announced target goes to one, while that on the old high rate goes to zero.

32

Filáček and Saxa (2010) found strong effects of CNB forecasts on forecasts for interest rates and inflation of financial market analysts. Interestingly, they found that the start of publication of the CNB’s interest rate path strengthened the central bank’s coordination role for the inflation and exchange rate forecasts, but not for the interest rate outlook itself.

33

Until the end of 2007, the CNB’s Bank Board met to discuss monetary issues once a month. Since the beginning of 2008 there are only eight such meetings a year.

34

We are grateful to Jan Syrovátka from the CNB’s Monetary Department for having shared with us his archive of these figures and the corresponding data.

35

The press conference release stated: “Consistent with the macroeconomic forecast and its assumptions is a gradual rise in nominal interest rates over the entire forecast horizon.” In the Inflation Report, published later, it was made public that “The interest rate path consistent with the aforementioned CNB forecast was slightly above the expectations of financial market analysts in the near future. At the longer horizon, it was higher.”

36

The voting ratios for the July 2007 meeting were presented on a press conference on 26th July 2007. The presentation for the press conference can be found here: https://www.cnb.cz/miranda2/export/sites/www.cnb.cz/en/monetary_policy/bank_board_minutes/2007/download/TK_07SZ2007_AJ.pdf

37

This decision was announced on 8 March 2007.

38

Ex post, this can be regarded as an illustration of how IFT can outperform a backward-looking approach to inflation targeting. Of course, the actual depth of the Global Financial Crisis was unforeseen at that time, and eventually the rate cuts were much deeper than the February forecast had suggested.

39

Some Board members later expressed reservations about this forecast.

40

There are numerous other positive examples, e.g., Inflation Reports I/2009 (February meeting), III/2009 (August meeting), II/2010 (May meeting), IV/2010 (November meeting), II/2011 (May meeting), and to a lesser extent also II/2012 (May meeting) and III/2012 (August meeting).

41

In addition, the ECB was moving toward an interest rate increase (premature, as it turned out).

43

This is a reassessment compared to Dincer and Eichengreen (2014), which gave the CNB a score of 12 for the 2008-2010 period, compared to 14.5 in the updated time series. In the earlier assessment, Dincer and Eichengreen did not award full transparency score to the CNB for data publication and forward-looking explanation of policy decisions, and regarded it as non-transparent in terms of publicly evaluating achievement of its operating targets, in contradiction to the actual practice.

44

Supporting evidence is that the CNB won the Central Banking Transparency Award 2015 from Central Banking Publications for openness and use of new communication tools. According to Central Banking Publications: “The CNB has long been considered among the top tier of central banks when it comes to openness… In the period under review, the CNB pushed the envelope even further—becoming the first central bank to publish blog posts from its senior officials.”

45

Between 2009 and mid-2013 the CNB published a fan-chart for endogenous CZK/EUR exchange rate at the press conference—a practice that has been suspended since November 2013 when the CNB announced an exchange rate floor as an unconventional monetary policy instrument.

46

The Board publishes a full transcript of policy meetings with a delay of six years. The six-year lag was chosen to match the term in office of individual Board members, to avoid inhibiting frank and open debate. This publication has yet to attract much attention from academics or journalists.

47

Tůma elaborated as follows: “I believe that the publishing of individual votes is increasingly pushing the Chairman to behave opportunistically (in the sense that he or she always joins the majority). And I can confirm that I felt the pressure intensely in the autumn of 2009 when I was outvoted a few times in key policy decisions. At that time, I seriously considered to resign. I also believe that the voting pattern of the Board members becomes less flexible—it is difficult to reverse your decision without appearing as admitting a mistake in the public’s eyes.”

48

Policymakers may also have forecasts of their own. For example, the Federal Open Market Committee of the Federal Reserve publishes summaries of the economic projections of members.

50

Governor Tůma (2010): “Developing such a (forecasting) system and making it an acceptable methodology for decision makers was one of the major transformations taking place in the Bank in the last decade. This is because the decision making under IT is torn between two fundamental opposites. At one hand, all forecasts and decisions are wrong ex post, no matter how good forecasting system and models you have. Essentially, some of your assumptions always turn out incorrect. Yet, on the other hand, you need to feel comfortable to make a good decision ex ante—i.e. today.”

51

See Hampl (2014), slide 23.

52

Tůma (2010): “The forecasting system must give the policymakers the comfort in making these ex ante decisions under uncertainty. This implies that forecasting accuracy is not as important as the ability to consistently differentiate between various alternative future developments. Our forecasting system has developed over the years to a truly disciplining tool for the policy debates and a platform for analyzing risks and their policy implications. These properties made it an acceptable tool for the policymakers.”

53

Other cases are in Brůha and others (2013).

54

For all the three scenarios, an alternative outlook for foreign variables was first simulated using the global NiGEM model. In the second step, it was used in the GPM-g3 model to produce alternative forecasts for the Czech economy.

55

For more on CNB’s experience of adding the exchange rate as a complementary monetary policy tool to stimulate the economy when the policy rate is at the effective lower bound, see Alichi and others (2015a), Clinton and others (2015) and Franta and others (2014).

56

For more arguments expaining why the exchange rate was judged to be the most efficient policy tool in the Czech circumstances see Franta and others (2014).

57

Governor Singer stressed on the press conference, taking place on November 1, that “Interest rates will remain at this level (i.e. technical zero) over a longer horizon until inflation pressures increase significantly”. The presentation from the November 1 press conference can be found here: http://www.cnb.cz/miranda2/export/sites/www.cnb.cz/en/monetary_policy/bank_board_minutes/2012/download/tk_07sz2012_aj.pdf

58

The version of the GPM-g3 model at that moment was linear and without the ELB constraint, and thus had no role for unconventional monetary policy.

59

The “real-time versions” of these simulations were produced in 2013 by František Brázdik, Tibor Hlédik, Zuzana Humplová and František Kopřiva. Figure 15 presents an improved version prepared by Jaromír Tonner.

60

This also shows the importance of the expectations channel, through which the central bank can influence the pass-through of changes in the exchange rate to prices using forward guidance (Franta and others, 2014).

Czech Magic: Implementing Inflation-Forecast Targeting at the CNB
Author: Kevin Clinton, Tibor Hlédik, Mr. Tomás Holub, Mr. Douglas Laxton, and Hou Wang