Top

Empirical Economics

Published in:

01-12-2014

Inflation uncertainty revisited: a proposal for robust measurement

Authors: Christian Grimme, Steffen R. Henzel, Elisabeth Wieland

Published in: Empirical Economics | Issue 4/2014

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Any measure of unobserved inflation uncertainty relies on specific assumptions which are most likely not fulfilled completely. This calls into question whether an individual measure delivers a reliable signal. To reduce idiosyncratic measurement error, we propose using common information contained in different measures derived from survey data, a variety of forecast models, and volatility models. We show that all measures are driven by a common component, which constitutes an indicator for inflation uncertainty. Moreover, our results suggest that using only one individual disagreement measure may be misleading, particularly during turbulent times. Finally, we study the Friedman–Ball hypothesis. Using the indicator, we show that higher inflation is followed by higher uncertainty. By contrast, we obtain contradictory results for the individual measures. We also document that, after an inflationary shock, uncertainty decreases in the first two months, which is traceable to the energy component in CPI inflation.

previous article Export and import market-specific characteristics

next article The long-run relationship of gold and silver and the influence of bubbles and financial crises

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Available only for authorised users

Mixed results with respect to the direction of causality are obtained inter alia by Grier and Perry (1998, 2000), Grier et al. (2004), and Berument and Dincer (2005). See also Davis and Kanago (2000) and Fountas and Karanasos (2007) and the papers cited therein.

The relation between disagreement and uncertainty is the subject of an ongoing debate. Bomberger and Frazer (1981), Bomberger (1996, 1999), and Giordani and Söderlind (2003) find supportive results, yet other studies report only a weak relationship or reject disagreement as a proxy (Zarnowitz and Lambros 1987; Lahiri et al. 1988; Rich and Butler 1998; Döpke and Fritsche 2006; Rich and Tracy 2010). Lahiri and Sheng (2010b) argue that disagreement is a reliable proxy for overall uncertainty if the forecast environment is stable.

See, for instance, the IMF Staff Position Note by Olivier Blanchard et al. (SPN/10/03), the comment by Ken Rogoff in the Financial Times on Aug 8, 2011, the address by Charles L. Evans at the Outlook Luncheon on Dec 5, 2011, and the comment by Paul Krugman in the NY Times on April 6, 2012.

We also computed the standard deviation and the quasi-standard deviation of forecasts (Giordani and Söderlind 2003). The quasi-standard deviation is defined as half the difference between the 84th and 16th percentiles. With normally distributed data, this measure equals the standard deviation. The correlation coefficient of these alternative dispersion measures and \(\mathrm{iqr}^s\) amounts to \(0.86\) and \(0.90\), respectively.

Giordani and Söderlind (2003) advocate the use of a “windowed” VAR—in opposition to a recursive VAR—where changes in the inflation process are quickly reflected in the parameter estimates.

See, for instance, Engle (1983), Cosimano and Jansen (1988), Brunner and Hess (1993), Grier and Perry (1996, 2000), Elder (2004), Grier et al. (2004) and Chang and He (2010).

See, for instance, Evans (1991), Evans and Wachtel (1993), Bhar and Hamori (2004), Berument et al. (2005), Caporale and Kontonikas (2009), and Caporale et al. (2012).

According to Lahiri and Sheng (2010b), overall forecast uncertainty is the sum of the variance of future aggregate shocks and the variance of idiosyncratic shocks. Model-based measures capture only the uncertainty common to all forecasters and neglect forecaster-specific shocks which are responsible for the disagreement among different forecasters.

Note that given the very similar factor loadings, PC1 remains virtually unaffected when we exclude one measure from the analysis. Our results thus do not hinge on one individual measure.

The results in this article remain unchanged when we replace PC1 with the average of the standardized measures. The results are available upon request.

Due to the CE survey, the main analysis is limited to a sample beginning in 1990. Hence, our sample covers a rather tranquil period as far as inflation is concerned. To see whether the results also hold for periods of high and volatile inflation, we conduct the analysis for the years 1970–1995 considering only the forecast-based and model-based approaches. Our results also hold for the earlier time-span. First, there appears to be a common component which explains the majority of the variation in the data (\(58~\%\)). Second, all individual measures contribute with a non-negligible weight. Third, the loadings are similar. Detailed results are available upon request.

The survey-based measures \(\mathrm{iqr}^s\) and \(\mathrm{ent}^s\) are also highly correlated with SPF uncertainty, which may be explained by the fact that these measures are also derived from a professional forecasters’ survey. For detailed results and a graphical representation, see Fig. 9 in the Appendix.

The result is robust to the choice of the lag length of the VAR according to BIC, which suggests two lags. Furthermore, it is robust if we exclude the recent crisis and end the sample in \(2007\):\(\text {M8}\), which is roughly when the US sub-prime crisis started to spill over into other sectors of the economy.

We also checked the reverse ordering of variables, which does not affect the results in a significant way.

See Fig. 11 in the Appendix for results obtained from monetary VARs containing output, inflation, a short-term interest rate, and uncertainty. Our results remain unaffected when a larger VAR is employed. Furthermore, the impulse response is qualitatively the same when we estimate the bivariate VAR on a sample ending in \(2007\):\(\text {M8}\); this result is available upon request.

Only two of the responses (\(\mathrm{iqr}^s\) and \(\mathrm{rmse}^f\)) are similar to the response of PC1. The individual impulse responses are presented in Fig. 10 in the Appendix.

Bachmann R, Elstner S, Sims ER (2013) Uncertainty and economic activity: evidence from business survey data. Am Econ J 5(2):217–249

Ball L (1992) Why does high inflation raise inflation uncertainty? J Monet Econ 29(3):371–388CrossRef

Ball L, Cecchetti SG (1990) Inflation and uncertainty at short and long horizons. Brook Pap Econ Act 21(1):215–254CrossRef

Batchelor R, Dua P (1993) Survey vs ARCH measures of inflation uncertainty. Oxf Bull Econ Stat 55(3):341–353CrossRef

Batchelor R, Dua P (1996) Empirical measures of inflation uncertainty: a cautionary note. Appl Econ 28(3):333–341CrossRef

Berg TO, Henzel SR (2013) Point and denstiy forecasts for the Euro Area using many predictors: are large BVARs really superior? ifo Working Paper 155

Bernanke B, Mishkin FS (1997) Inflation targeting: a new framework for monetary policy? J Econ Perspect 11(2):97–116

Berument H, Dincer NN (2005) Inflation and inflation uncertainty in the G7 countries. Phys A 348(C):371–379CrossRef

Berument H, Kilinc Z, Ozlale U (2005) The missing link between inflation uncertainty and interest rates. Scott J Polit Econ 52(2):222–241CrossRef

Bhar R, Hamori S (2004) The link between inflation and inflation uncertainty: evidence from G7 countries. Empir Econ 29(4):825–853CrossRef

Bloom N (2009) The impact of uncertainty shocks. Econometrica 77(3):623–686CrossRef

Bomberger WA (1996) Disagreement as a measure of uncertainty. J Money Credit Bank 28(3):381–392CrossRef

Bomberger WA (1999) Disagreement and uncertainty: a reply to Rich and Butler (1998). J Money Credit Bank 31(2):273–276CrossRef

Bomberger WA, Frazer WJ (1981) Interest rates, uncertainty and the Livingston data. J Finance 36(3):661–675CrossRef

Brunner AD, Hess GD (1993) Are higher levels of inflation less predictable? A state-dependent conditional heteroskedasticity approach. J Am Stat Assoc 11(2):187–197

Caporale GM, Kontonikas A (2009) The Euro and inflation uncertainty in the European Monetary Union. J Int Money Finance 28(6):954–971CrossRef

Caporale GM, Onorante L, Paesani P (2012) Inflation and inflation uncertainty in the Euro Area. Empir Econ 43(2):597–615

Chang K-L, He C-W (2010) Does the magnitude of the effect of inflation uncertainty on output growth depend on the level of inflation? Manch Sch 78(2):126–148CrossRef

Chua CL, Kim D, Suardi S (2011) Are empirical measures of macroeconomic uncertainty alike? J Econ Surv 25(4):801–827CrossRef

Clark TE (2009) Is the Great Moderation over? An empirical analysis. Fed Reserve Bank Kans City Econ Rev Q IV:5–42

Cosimano TF, Jansen DW (1988) Estimates of the variance of US inflation based upon the ARCH model: comment. J Money Credit Bank 20(3):409–421CrossRef

Cukierman A, Meltzer AH (1986) A theory of ambiguity, credibility, and inflation under discretion and asymmetric information. Econometrica 54(5):1099–1128CrossRef

Cukierman A, Wachtel P (1982) Relative price variability and nonuniform inflationary expectations. J Polit Econ 90(1):146–157CrossRef

D’Amico S, Orphanides A (2008) Uncertainty and disagreement in economic forecasting. Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System No. 2008-56

Davis GK, Kanago BE (2000) The level and uncertainty of inflation: results from OECD forecasts. Econ Inq 38(1):58–72CrossRef

Döpke J, Fritsche U (2006) When do forecasters disagree? An assessment of German growth and inflation forecast dispersion. Int J Forecast 22(1):125–135CrossRef

Dovern J, Fritsche U, Slacalek J (2012) Disagreement among forecasters in G7 countries. Rev Econ Stat 94(4):1081–1096CrossRef

Dovern J, Weisser J (2011) Accuracy, unbiasedness and efficiency of professional macroeconomic forecasts: an empirical comparison for the G7. Int J Forecast 27(2):452–465CrossRef

Elder J (2004) Another perspective on the effects of inflation uncertainty. J Money Credit Bank 36(5):911–928CrossRef

Engle R (1983) Estimates of the variance of US inflation based on the ARCH model. J Money Credit Bank 15(3):286–301CrossRef

Evans M (1991) Discovering the link between inflation rates and inflation uncertainty. J Money Credit Bank 23(2):169–184CrossRef

Evans M, Wachtel P (1993) Inflation regimes and the sources of inflation uncertainty. J Money Credit Bank 25(3):475–511CrossRef

Fountas S, Karanasos M (2007) Inflation, output growth, and nominal and real uncertainty: empirical evidence for the G7. J Int Money Finance 26(2):229–250CrossRef

Friedman M (1977) Nobel lecture: inflation and unemployment. J Polit Econ 85(3):451–472CrossRef

Giordani P, Söderlind P (2003) Inflation forecast uncertainty. Eur Econ Rev 47(6):1037–1059CrossRef

Grier KB, Henry OT, Olekalns N, Shields K (2004) The asymmetric effects of uncertainty on inflation and output growth. J Appl Econom 19(5):551–565CrossRef

Grier KB, Perry MJ (1996) Inflation, inflation uncertainty, and relative price dispersion: evidence from bivariate GARCH-M models. J Monet Econ 38(2):391–405CrossRef

Grier KB, Perry MJ (1998) On inflation and inflation uncertainty in the G7 countries. J Int Money Finance 17(4):671–689CrossRef

Grier KB, Perry MJ (2000) The effects of real and nominal uncertainty on inflation and output growth: some GARCH-M evidence. J Appl Econom 15(1):45–58CrossRef

Hartmann M, Herwartz H (2013) Did the introduction of the Euro have an impact on inflation uncertainty? An empirical assessment. Macroeconomic Dynamics, forthcoming

Harvey A, Ruiz E, Shephard N (1994) Multivariate stochastic variance models. Rev Econ Stud 61(2):247–264CrossRef

Henzel SR, Mayr J (2013) The mechanics of VAR forecast pooling: a DSGE model based Monte Carlo study. N Am J Econ Finance 24(1):1–24CrossRef

Kilian L (1998) Small-sample confidence intervals for impulse response functions. Rev Econ Stat 80(2):218–230CrossRef

Kim S, Shephard N, Chib S (1998) Stochastic volatility: likelihood inference and comparison with ARCH models. Rev Econ Stud 65(3):361–393CrossRef

Knotek ES, Khan S (2011) How do households respond to uncertainty shocks? Fed Reserve Bank Kans City Econ Rev Q II:63–92

Lahiri K, Sheng X (2010a) Learning and heterogeneity in GDP and inflation forecasts. Int J Forecast 26(2):265–292CrossRef

Lahiri K, Sheng X (2010b) Measuring forecast uncertainty by disagreement: the missing link. J Appl Econom 25(4):514–538CrossRef

Lahiri K, Teigland C, Zaporowski M (1988) Interest rates and the subjective probability distribution of inflation forecasts. J Money Credit Bank 20(2):233–248CrossRef

Mankiw NG, Reis R, Wolfers J (2003) Disagreement about inflation expectations. NBER Macroecon Annu 18:209–248

Peng A, Yang L (2008) Modelling uncertainty: a recursive VAR bootstrapping approach. Econ Lett 99(3):478–481CrossRef

Rich RW, Butler JS (1998) Disagreement as a measure of uncertainty: a comment on Bomberger (1996). J Money Credit Bank 30(3):411–419

Rich RW, Tracy J (2010) The relationship among expected inflation, disagreement, and uncertainty: evidence from matched point and density forecasts. Rev Econ Stat 92(1):200–207CrossRef

Stock JH, Watson MW (1999) Forecasting inflation. J Monet Econ 44(2):293–335CrossRef

Stock JH, Watson MW (2002) Macroeconomic forecasting using diffusion indices. J Am Stat Assoc 20(2):147–162

Stock JH, Watson MW (2007) Why has US inflation become harder to forecast? J Money Credit Bank 39(1):3–33CrossRef

Vasicek O (1976) A test for normality based on sample entropy. J R Stat Soc Ser B 38(1):54–59

Zarnowitz V, Lambros LA (1987) Consensus and uncertainty in economic predictions. J Polit Econ 95(3):591–621CrossRef

Title: Inflation uncertainty revisited: a proposal for robust measurement
Authors: Christian Grimme
Steffen R. Henzel
Elisabeth Wieland
Publication date: 01-12-2014
Publisher: Springer Berlin Heidelberg
Published in: Empirical Economics / Issue 4/2014
Print ISSN: 0377-7332
Electronic ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-013-0789-z

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 4/2014

Bank competition in transition countries: Are those markets really in equilibrium?

Succeeding in innovation: key insights on the role of R&D and technological acquisition drawn from company data

Robust tests for time-invariant individual heterogeneity versus dynamic state dependence

Forecasting spatially dependent origin and destination commodity flows

Estimating the Markov-switching almost ideal demand systems: a Bayesian approach

Trade facilitation and country size

Premium Partner