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Do professional forecasters trust in Taylor-type rules? – Evidence from the Wall Street Journal poll

2011; Taylor & Francis; Volume: 45; Issue: 7 Linguagem: Inglês

10.1080/00036846.2011.613770

1466-4283

Ralf Fendel, Michael Frenkel, Jan‐Christoph Rülke,

Economic Theory and Institutions

AbstractThis article uses the monthly Wall Street Journal poll between 2002 and 2010 to analyse whether professional economic forecasters believe in and, thus, apply Taylor-type rules for their own forecasts. Using their forecasts for the Federal Funds rate, the inflation rate and capacity utilization, we estimate whether these are internally consistent with the message of Taylor(-type) rules. We find that the expectation formation can indeed be described by Taylor-type rules.Keywords: Taylor ruleexpectation formationmonetary policyFederal ReserveJEL Classification:: E52D84C33 AcknowledgementsWe are grateful to Helge Berger, Jörg Breitung, Jordi Galí, Stuart Glosser, Thomas Laubach, Eric M. Leeper, Marcelo Sanchez, Jan-Egbert Sturm, participants of an internal seminar at the European Central Bank (ECB), of the 12th ZEI Summer School and of the Euro Area Macroeconomic (EAM) seminar at the ECB as well as an anonymous referee for helpful comments and suggestions. Ralf Fendel thanks the Deutsche Bundesbank Research Department for generous support.Notes1 See Hamalainen (Citation2004) for a survey of empirical studies related to the US.2 In particular, for the output gap the literature demonstrated that it is relevant to discriminate between ex-post and real-time data (Orphanides, Citation2001). We take this issue into account and construct the output gap on the basis of both ex-post and real-time data.3 Since this issue is also not of a strong concern in the present article, we refer to the recent literature (Rudebusch, Citation2006).4 Thus, instead of directly asking financial market participants whether they believe in the Taylor rules, we search for their ‘revealed preferences’ concerning the usefulness of Taylor rules.5 Mitchell and Pearce (Citation2007), for instance, analyse the accuracy of the WSJ forecasts. They find that a majority of the professional forecasters produce unbiased interest rate forecasts, but the forecasts are indistinguishable from a random walk model and the economists are systematically heterogeneously distributed. Using a multivariate approach, Eisenbeis et al. (Citation2002) evaluate the performance of professional forecasters in the WSJ poll relative to the other participants. Their results suggest that the dispersion in the forecasts may serve as an indicator of how much uncertainty there may be about where the economy is going. Greer (Citation2003) focuses on the 1-year forecast of the 30-year US Treasury bond. He examines whether economists are able to predict the direction of change correctly and finds that this is indeed the case.6 In contrast to the view of Keane and Runkle (Citation1990), Laster et al. (Citation1999) develop a model in which forecasters are rewarded for forecast accuracy and publicity in case of giving the best forecast at a single point in time. As a consequence, forecasters whose wages depend most on publicity will differ most from the consensus forecast.7 Since the forecast horizon of the growth forecasts is on a quarterly basis, we calculate the projected growth rate by weighing the growth rate with the remaining months to the end of the forecast horizon. The Appendix provides a detailed description of the calculation.8 The same argument applies to the median of the forecast is also shown in Table 1.9 The fact that the private sector forecasters are known for their relatively good out-of-sample forecasting power (Ang et al., Citation2007) might be a reason why the central bank's behaviour can well be described by Taylor rules based on survey data. Gorter et al. (Citation2008) provide evidence that the European Central Bank's (ECB's) monetary policies decisions can be explained by survey data.10 More precisely, we use a recursive Hodrick–Prescott filter with the smoothing parameter set at λ = 14 400. Compared to the standard filter, the recursive calculation ensures that only information that was available at the time of forecast is taken into account.11 We started with this series in 1995 since the Organization for Economic Co-operation and Development (OECD) calculated the output gap to be zero in 1995.12 Croushore and Stark (Citation2001) provide an overview of the real-time database.13 The choice of using this measure of capacity utilization is superior to the output gap measure discussed above as it circumvents problems associated with the index number theory, the use of industrial production data levels, and exogenous detraining over the whole sample period. Note that by this definition an expected increase (decrease) in the unemployment rate results in a negative (positive) value of Et(ũt+k) in order to produce the same expected sign of the capacity utilization coefficient across all specifications.14 However, relaxing the assumption of a time-invariant long-term inflation target π* requires an appropriate time-variant measure for . We leave this to further research.15 This finding matches the well-demonstrated phenomenon that expectations in financial markets are rather static than dynamic (Mitchell and Pearce, Citation2007). Furthermore, Krueger and Kuttner (Citation1996) found that the Federal Funds future market provide efficient predictions on the future path of the Federal Funds rate. As the future and actual path of the Federal Funds rate are close to each other, static expectations seem reasonable as a means to forecast interest rates.