A comparative assessment of alternative ex ante measures of inflation uncertainty

Hartmann, Matthias; Herwartz, Helmut; Ulm, Maren

doi:10.1016/j.ijforecast.2016.08.005

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A comparative assessment of alternative ex ante measures of inflation uncertainty

Hartmann, Matthias; Herwartz, Helmut; Ulm, Maren

2017 • In International Journal of Forecasting, 33 (1), p. 76-89

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https://hdl.handle.net/2268/267798

DOI
10.1016/j.ijforecast.2016.08.005

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Keywords :

Uncertainty measurement; Ex ante and ex post inflation uncertainty; Disagreement

Abstract :

[en] The existence of unconventional monetary and fiscal policy arrangements in industrialized economies has been raising concerns about the future evolution of inflation rates ever since the onset of the financial and sovereign debt crisis in 2008. However, the question of how inflation uncertainty should be quantified is an open issue. We assess the informative content of alternative ex ante quantifications of inflation uncertainty by predicting ex post squared inflation forecast errors in an out-of-sample forecasting contest. We find that the average across distinct models’ levels of ex ante uncertainty offers a greater predictive content than other uncertainty measures based on the cross-sectional variance of point forecasts, GARCH or stochastic volatility models.

Disciplines :

Macroeconomics & monetary economics

Author, co-author :

Hartmann, Matthias

Herwartz, Helmut

Ulm, Maren ; Université de Liège - ULiège > HEC Recherche

Language :

English

Title :

A comparative assessment of alternative ex ante measures of inflation uncertainty

Publication date :

2017

Journal title :

International Journal of Forecasting

ISSN :

0169-2070

eISSN :

1872-8200

Publisher :

Elsevier, Amsterdam, Netherlands

Volume :

Issue :

Pages :

76-89

Peer reviewed :

Peer Reviewed verified by ORBi

Available on ORBi :

since 03 February 2022

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Bibliography

Abel, J., Rich, R., Song, J., Tracy, J., The measurement and behavior of uncertainty: Evidence from the ECB survey of professional forecasters. Journal of Applied Econometrics 31 (2016), 533–550.
Akgiray, V., Conditional heteroscedasticity in time series of stock returns: Evidence and forecasts. Journal of Business, 1989, 55–80.
Ang, A., Bekaert, G., Wei, M., Do macro variables, asset markets or survey forecast inflation better ?. Journal of Monetary Economics 54 (2007), 1163–1212.
Batchelor, R., Dua, P., Empirical measures of inflation uncertainty: A cautionary note. Applied Economics 28 (1996), 333–341.
Becker, R., Clements, A.E., Are combination forecasts of S&P 500 volatility statistically superior?. International Journal of Forecasting 24 (2008), 122–133.
Benati, L., The “Great Moderation” in the UK. Journal of Money, Credit and Banking 40 (2008), 121–147.
Blaskowitz, O., Herwartz, H., Testing the value of directional forecasts in the presence of serial correlation. International Journal of Forecasting 30 (2014), 30–42.
Bloom, N., The impact of uncertainty shocks. Econometrica 77 (2009), 623–685.
Boero, G., Smith, J., Wallis, K.F., Uncertainty and disagreement in economic prediction: The bank of England survey of external forecasters. The Economic Journal 118 (2008), 1107–1127.
Bollerslev, T., Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31 (1986), 307–327.
Bomberger, W.A., Disagreement as a measure of uncertainty. Journal of Money, Credit and Banking 28 (1996), 381–392.
Branch, W.A., The theory of rationally heterogeneous expectations: Evidence from survey data on inflation expectations. The Economic Journal 114 (2004), 592–621.
Brock, W.A., Hommes, C., A rational route to randomness. Econometrica 65 (1997), 1059–1095.
Canova, F., G-7 inflation forecasts: Random walk, Phillips curve or what else?. Macroeconomic Dynamics 11 (2007), 1–30.
Capistrán, C., Ramos-Francia, M., Does inflation targeting affect the dispersion of inflation expectations?. Journal of Money, Credit and Banking 42 (2010), 113–134.
Chevillon, G., Direct multistep estimation and forecasting. Journal of Economic Surveys 21 (2005), 746–785.
Clements, M.P., Forecast uncertainty ex-ante and ex-post: US inflation and output growth. Journal of Business and Economic Statistics 32 (2014), 206–216.
Clements, M. P., & Galvao, A. B. (2014). Measuring macroeconomic uncertainty: US inflation and output growth. Available at SSRN 2436810.
Cogley, T., A simple adaptive measure of core inflation. Journal of Money, Credit and Banking 34 (2002), 94–113.
D'Amico, S., & Orphanides, A. (2008). Uncertainty and disagreement in economic forecasting. Federal Reserve Board Finance and Economic Discussion Series Working Paper, 56.
Diebold, F.X., Mariano, R.S., Comparing predictive accuracy. Journal of Business and Economic Statistics 13 (1995), 253–263.
Diether, K.B., Malloy, C., Scherbina, A., Differences of opinion and the cross section of stock returns. Journal of Finance 57 (2002), 2113–2141.
Dovern, J., Fritsche, U., Slacalek, J., Disagreement among forecasters in G7 countries. Review of Economics and Statistics 94 (2012), 1081–1096.
Engle, R.F., Autoregressive conditional heteroskedasticity with estimates of the variance of UK inflation. Econometrica 50 (1982), 987–1008.
Engle, R.F., Estimates of the variance of US inflation based upon the ARCH model. Journal of Money, Credit and Banking 15 (1983), 286–301.
Friedman, M., Inflation and unemployment. Journal of Political Economy 85 (1977), 451–472.
Giordani, P., Söderlind, P., Inflation forecast uncertainty. European Economic Review 47 (2003), 1037–1059.
Hamilton, J., Uncovering financial market expectations of inflation. Journal of Political Economy 93 (1985), 1224–1241.
Harvey, D., Leybourne, S., Newbold, P., Testing the equality of prediction mean squared errors. International Journal of Forecasting 13 (1997), 281–291.
Herrera, A.M., Pesavento, E., Oil price shocks, systematic monetary policy, and the ”Great Moderation”. Macroeconomic Dynamics 13 (2009), 107–137.
Jurado, K., Ludvigson, S., Ng, S., Measuring uncertainty. American Economic Review 105 (2015), 1177–1216.
Knight, J., Satchell, S., Forecasting volatility in the financial markets. 2007, Butterworth-Heinemann.
Lahiri, K., Liu, F., ARCH models for multi-period forecast uncertainty: A reality check using a panel of density forecasts. Terrell, D., Fomby, T., (eds.) Econometric analysis of financial and economic time series part A, 2005, Elsevier, North Holland, 321–363.
Lahiri, K., Peng, H., & Sheng, X. (2014). Measuring uncertainty of a combined forecast. Working Paper SUNY Albany.
Lahiri, K., Sheng, X., Measuring forecast uncertainty by disagreement: The missing link. Journal of Applied Econometrics 25 (2010), 514–538.
Lahiri, K., Teigland, C., Zaporowski, M., Interest rates and the subjective probability distribution of inflation forecasts. Journal of Money, Credit and Banking 20 (1988), 233–248.
Leitch, G., Tanner, J.E., Economic forecast evaluation: Profits versus the conventional error measures. American Economic Review 81 (1991), 580–590.
Mankiw, N.G., Reis, R., Disagreement about inflation expectations. NBER Macroeconomics Annual 18 (2004), 147–209.
McConnell, M.M., Perez-Quiros, G., Output fluctuations in the United States: What has changed since the early 1980s?. American Economic Review 90 (2000), 1464–1476.
Mincer, J.A., Zarnowitz, V., The evaluation of economic forecasts. Economic forecasts and expectations: analysis of forecasting behavior and performance, 1969, NBER, 3–46.
Mokinski, F., Sheng, X., Yang, J., Measuring disagreement in qualitative expectations. Journal of Forecasting 34 (2015), 405–426.
Okun, A.M., The mirage of steady inflation. Brookings Papers on Economic Activity 2 (1971), 485–498.
Patton, A.J., Sheppard, K., Original combinations of realised volatility estimators. International Journal of Forecasting 25 (2009), 218–238.
Rich, R., Song, J., Tracy, J., The measurement and behavior of uncertainty: Evidence from the ECB survey of professional forecasters., 2012 Federal Reserve Bank of New York Staff Reports, 588.
Rich, R., Tracy, J., Modeling uncertainty: Predictive accuracy as a proxy for predictive confidence., 2003 Federal Reserve Bank of New York Staff Reports, 253.
Stock, J.H., Watson, M.W., A comparison of linear and nonlinear univariate models for forecasting macroeconomic time series. Engle, R.F., White, H., (eds.) Cointegration, causality and forcasting: a festschrift in honor of Clive W. J. Granger, 1999, Oxford University Press, Oxford, 1–43.
Stock, J.H., Watson, M.W., Why has US inflation become harder to forecast?. Journal of Money, Credit and Banking 39 (2007), 3–33.
Stock, J. H., & Watson, M. W. (2008). Phillips curve inflation forecasts. NBER Working Paper, W14322.
Swanson, N.R., White, H., A model-selection approach to assessing the information in the term structure using linear models and artificial neural networks. Journal of Business and Economic Statistics 13 (1995), 265–275.
Wallis, K.F., Combining density and interval forecasts: A modest proposal. Oxford Bulletin of Economics and Statistics 67 (2005), 1389–1408.
Yu, J., Disagreement and return predictability of stock portfolios. Journal of Financial Economics 99 (2011), 162–183.
Zarnowitz, V., Lambros, L., Consensus and uncertainty in economic prediction. Journal of Political Economy 95 (1987), 591–621.