Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-12-01T00:42:57.008Z Has data issue: false hasContentIssue false
  • English
  • Français

MODEL-BASED VS. PROFESSIONAL FORECASTS: IMPLICATIONS FOR MODELS WITH NOMINAL RIGIDITIES

Published online by Cambridge University Press:  17 February 2016

João Valle e Azevedo  and
João Tovar Jalles
João Valle e Azevedo*
Affiliation:
Banco de Portugal and Nova School of Business and Economics
João Tovar Jalles
Affiliation:
OECD
*
Address correspondence to: João Valle e Azevedo, Banco de Portugal, Research Department, Av. Almirante Reis, 71-6th floor, 1150-012 Lisboa, Portugal; e-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

We compare model forecast error statistics with forecast error statistics of professional forecasts. We look at a standard sticky-prices–wages model, concluding that it delivers too strong a theoretical forecastability of the variables under scrutiny, at odds with the data (professional forecasts). We argue that the lack of compatibility between the model and professional forecasts results from trying to fit inflation (which is probably nonstationary) to a model that assumes inflation is stationary. A modified version of the model, one with a varying inflation target, delivers a better fit in terms of forecastability.

Keywords

Survey of Professional ForecastersDSGE ModelsNominal Rigidities
Type
Articles
Information
Macroeconomic Dynamics , Volume 21 , Issue 1 , January 2017 , pp. 130 - 159
Copyright
Copyright © Cambridge University Press 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Adolfson, M., Laséen, S., Lindé, J., and Villani, M. (2007a) Bayesian estimation of an open economy DSGE model with incomplete pass-through. Journal of International Economics 72, 481–511.Google Scholar
Adolfson, M., Lindé, J., and Villani, M. (2007b) Forecasting performance of an open economy DSGE model. Econometric Reviews 26, 289–328.Google Scholar
Ang, A., Bekaert, G., and Wei, M. (2007) Do macro variables, asset markets, or surveys forecast inflation better? Journal of Monetary Economics 54 (4), 1163–1212.CrossRefGoogle Scholar
Baghestani, H. (2009) Survey evidence on forecast accuracy of U.S. term spreads. Review of Financial Economics 18, 156–162.CrossRefGoogle Scholar
Baghestani, H. (2012) Are professional forecasts of growth in U.S. business investment rational? Economics Letters 114, 132–135.Google Scholar
Capistrán, C. and Timmermann, A. (2009) Forecast combination with entry and exit of experts. Journal of Business and Economic Statistics 27, 428–440.Google Scholar
Christiano, L., Eichenbaum, M., and Evans, C. (2005) Nominal rigidities and the dynamic effects of a shock to monetary policy. Journal of Political Economy 113 (1), 1–45.Google Scholar
Cogley, T., Primiceri, G.E., and Sargent, T.J. (2010) Inflation-gap persistence in the US. American Economic Journal: Macroeconomics 2 (1), 43–69.Google Scholar
Cogley, T. and Sbordone, A.M. (2008) Trend inflation, indexation, and inflation persistence in the New Keynesian Phillips curve. American Economic Review 98 (5), 2101–2126.Google Scholar
Croushore, D. (1993) The survey of professional forecasters. Business Review, Federal Reserve Bank of Philadelphia (November/December), 3–15.Google Scholar
Croushore, D. (2006) An Evaluation of Inflation Forecasts from Surveys Using Real-Time Data. Working paper 06-19, Federal Reserve Bank of Philadelphia.CrossRefGoogle Scholar
Croushore, D. and Stark, T. (2001) A real-time data set for macroeconomists. Journal of Econometrics 105, 111–130.Google Scholar
Croushore, D. and Stark, T. (2003) A real-time data set for macroeconomists: Does the data vintage matter? Review of Economics and Statistics 85, 605–617.CrossRefGoogle Scholar
D'Agostino, A. and Whelan, K. (2008) Federal reserve information during the Great Moderation. Journal of the European Economic Association 6 (2–3), 609–620.CrossRefGoogle Scholar
Diebold, F.X. and Kilian, L. (2001) Measuring predictability: Theory and macroeconomic applications. Journal of Applied Econometrics 16 (6), 657–666.CrossRefGoogle Scholar
Durbin, J. and Koopman, S.J. (2001) Time Series Analysis by State Space Methods. Oxford, UK: Oxford University Press.Google Scholar
Engelberg, J., Manski, C.F., and Williams, J. (2009) Comparing the point predictions and subjective probability distributions of professional forecasters. Journal of Business and Economic Statistics 27, 30–41.Google Scholar
Fair, R.C. and Shiller, R.J. (1989) The informational context of ex ante forecasts. Review of Economics and Statistics 71 (2), 325–331.Google Scholar
Faust, J. and Wright, J. (2012) Forecasting inflation. June 27, 2012, draft for Handbook of Forecasting.Google Scholar
Gamber, E. and Smith, J. (2009) Are the Fed's inflation forecasts still superior to the private sector's? Journal of Macroeconomics 31 (2), 240–251.CrossRefGoogle Scholar
Gavin, W.T. and Mandal, R.J., (2003) Evaluating FOMC forecasts. International Journal of Forecasting 19, 655–667.Google Scholar
Giannone, D., Lenza, M., and Reichlin, L. (2008) Explaining the Great Moderation: It is not the shocks. Journal of the European Economic Association 6 (2–3), 621–633.Google Scholar
Granger, C.W.J. and Newbold, P. (1986). Forecasting Economic Time Series, 2nd ed. Orlando, FL: Academic Press.Google Scholar
Ireland, Peter N. (2007) Changes in the Federal Reserve's inflation target: Causes and consequences. Journal of Money, Credit and Banking 39 (8), 1851–2110.Google Scholar
Juillard, M., Kamenik, O., Kumhof, M., and Laxton, D. (2008) Optimal price setting and inflation inertia in a rational expectations model. Journal of Economic Dynamics and Control 32, 2584–2621.Google Scholar
Kolasa, M., Rubaszek, M., and Skrzypczynski, P. (2012) Putting the New Keynesian DSGE model to the real-time forecasting test. Journal of Money, Credit and Banking 44, 1301–1324.Google Scholar
McConnell, M. and Perez-Quiros, G. (2000) Output fluctuations in the United States: What has changed since the early 1980's? American Economic Review 90 (5), 1464–1476.Google Scholar
Romer, C. and Romer, D. (2000) Federal Reserve information and the behaviour of interest rates. American Economic Review 90, 429–457.CrossRefGoogle Scholar
Rotemberg, J.J. and Woodford, M. (1996) Real business-cycle models and the forecastable movements in output, hours, and consumption. American Economic Review 86, 71–89.Google Scholar
Rubaszek, M. and Skrzypczynski, P. (2008) On the forecasting performance of a small-scale DSGE model. International Journal of Forecasting 24, 498–512.Google Scholar
Schorfheide, F. (2005) Learning and monetary policy shifts. Review of Economic Dynamics 8 (2), 392–419.Google Scholar
Smets, F. and Wouters, R. (2003) An estimated dynamic stochastic general equilibrium model of the euro area. Journal of the European Economic Association 1 (5), 1123–1175.CrossRefGoogle Scholar
Smets, F. and Wouters, R. (2007) Shocks and frictions in US business cycles: A Bayesian DSGE approach. American Economic Review 97 (3), 586–606.CrossRefGoogle Scholar
Stark, T. (2010) Realistic Evaluation of Real-Time Forecasts in the Survey of Professional Forecasters. Federal Reserve Bank of Philadelphia, Research Rap Special Report.Google Scholar
Stock, J.H. and Watson, M.W. (2003) Has the Business Cycle Changed and Why? In NBER Macroeconomics Annual 2002, Vol. 17, pp. 159–230. National Bureau of Economic Research.Google Scholar
Stock, J.H. and Watson, M.W. (2007) Why has U.S. inflation become harder to forecast? Journal of Money, Credit and Banking 39, 3–34.CrossRefGoogle Scholar
Swanson, E., Anderson, G., and Levin, A. (2005) Higher-Order Perturbation Solutions to Dynamic, Discrete-Time Rational Expectations Models. Mimeo, Federal Reserve Bank of San Francisco.CrossRefGoogle Scholar
Valle e Azevedo, J. and Pereira, A. (2013) Macroeconomic Forecasting Using Low-Frequency Filters. Banco de Portugal WP 1-2013.Google Scholar
Zarnowitz, V. (1969) The new ASA-NBER survey of forecasts by economic statisticians. American Statistician 23, 12–16.Google Scholar
Zarnowtiz, V. and Braun, P. (1993) Twenty-two years of the NBER-ASA Quarterly Economic Outlook Surveys: Aspects and comparisons of forecasting performance. In Stock, J.H. and Watson, M.W. (eds.), Business Cycles, Indicators and Forecasting (NBER Studies in Business Cycles, Vol. 28), pp. 11–84. Chicago: University of Chicago Press.Google Scholar