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Directional Accuracy of MMS Survey of Inflation-Output Forecasts of G7 Countries: A ROC Analysis

Date: January 10,2022 |Hits: 968 Download PDF How to cite this paper

Yasemin Ulu

Department of Economics, Saginaw Valley State University, University Center, Michigan, USA.

*Corresponding author: Yasemin Ulu


We study the directional forecast accuracy of inflation and output forecasts from Money Market Services Survey (MMS) for G7 countries using a Receiver Operating Characteristic (ROC) curve analysis. Applying ROC curve technique is interesting since ROC analysis can capture the accuracy of directional forecasts for different criterion while the conventional market timing tests can be used only for one. Our results indicate that forecasts of inflation and output from MMS survey contain valuable information for the target variables considered uniformly for all countries. Our findings reinforce the results found in the literature that MMS survey of inflation output forecasts for G7 countries have directional forecast accuracy considered separately, although they seem to fail rationality tests under symmetric loss for some G7 countries. We conclude in favor of directional accuracy of inflation and output forecasts of MMS survey for G7 countries for the period considered.


[1] Merton, R. C. (1981). On market timing and investment performance. I. An equilibrium theory of value for market forecasts. The Journal of Business, 54, 363-406.

[2] Schnader, M. H. and Stekler, H. O. (1990). Evaluating predictions of change. The Journal of Business, 63, 99-107.

[3] Stekler, H. O. (1994). Are economic forecasts valuable? Journal of Forecasting, 13, 495-505.

[4] Sinclair, T., Stekler, H. O., and Kitzinger, L. (2008). Directional forecasts of GDP and inflation: a joint evaluation with an application to Federal Reserve predictions. Applied Economics, 42, 2289-97.

[5] Pesaran, M. H. and Timmerman, A. (1992). A simple nonparametric test of predictive performance. Journal of Business and Economic Statistics, 10, 461-5.

[6] Stein, R. M. (2005). ‘The Relationship between Default Prediction and Lending Profits: Integrating ROC Analysis and Loan Pricing’. Journal of Banking & Finance, 29, 1213-1236.

[7] Bl¨ochlinger, A. and Leippold, M. (2006). ‘Economic Benefit of Powerful Credit Scoring’. Journal of Banking & Finance, 30, 851-873.

[8] Ravi, V. and Pramodh, C. (2008). ‘Threshold Accepting Trained Principal Component Neural Network and Feature Subset Selection: Application to Bankruptcy Prediction in Banks’. Applied Soft Computing, 8, 1539-1548.

[9] Berge, T. J. and Jordà, Ò. (2011). ‘Evaluating the Classification of Economic Activity into Recessions and Expansions’. American Economic Journal: Macroeconomics, 3, 246-277.

[10] Jordà, Ò. and A. M. Taylor. (2012). “The Carry Trade and Fundamentals: Nothing to Fear but FEER Itself”. Journal of International Economics, 88, 74-90.

[11] Drehmann, M. and Juselius, M. (2014). ‘Evaluating Early Warning Indicators of Banking Crises: Satisfying Policy Requirements’. International Journal of Forecasting, 30, 759-780.

[12] Lahiri, K. and Yang, L. (2013). Forecasting Binary Outcomes, in A. Timmermann and G. Elliott (eds.), ‘Handbook of Economic Forecasting Volume 2B’, North-Holland Amsterdam, pp. 1025-1106.

[13] Cohen, J., Garman, S., and Gorr, W. (2009). ‘Empirical Calibration of Time Series Monitoring Methods using Receiver Operating Characteristic Curves’. International Journal of Forecasting, 25, 484-497.

[14] Gorr, W. and Schneider, M. J. (2011). ‘Large-Change Forecast Accuracy: Reanalysis of M3-Competition Data using Receiver Operating Characteristic Analysis’. International Journal of Forecasting, 29, 274-281.

[15] Jordà, Ò, Schularick, M., and Taylor, A. M. (2011). ‘Financial Crises, Credit Booms, and External Imbalances: 140 Years of Lessons’. IMF Economic Review, 59, 340-378.

[16] Pierdzioch, C. and Rulke, J. C. (2015). “On the Directional Accuracy of Forecasts of Emerging Market Exchange Rates.” International Review of Economics and Finance, 38, 369-376.

[17] Pierdzioch, C. (2015). “A note on the Directional Accuracy of interest-rate Forecasts”. Applied Economics Letters, 22: 13, 1073-1077. DOI: 10.1080/13504851.2014.1000516.

[18] Pierdzioch, C. (2016). “Using ROC techniques to Measure the Effectiveness of Foreign Exchange Market Interventions”. Applied Economics, Vol. 23, No. 6, 389-393.

[19] Pierdzioch, C. and Schmidth, H. (2017). “On the Directional Accuracy of Inflation Forecasts: Evidence from South African Survey Data”. Journal of Applied Statistics.

[20] Lahiri, K. and Wang, J. G. (2013). ‘Evaluating Probability Forecasts for GDP Declines using Alternative Methodologies’. International Journal of Forecasting, 29, 175-190.

[21] Ulu, Y. (2015). “Rationality of inflation–output forecasts of MMS survey: international evidence”. Applied Economics, 47: 12, 1187-1198.

[22] Peterson, Wesley W. and Birdsall, Theodore G. (1953). “The theory of signal detectability: part I. the general theory. Electronic Defense Group: Technical Report, 13. University of Michigan, Ann Arbor.

[23] Lusted, Lee B. (1960). “Logical analysis in roentgen diagnosis”. Radiology, 74(2), 178-193.

[24] Swets, John A. (1973). “The relative operating characteristic in psychology”. Science, 182(4116), 990-1000.

[25] Mason, Ian B. (1982). “A model for the assessment of weather forecasts”. Australian Meterological Magazine, 30 (4), 291-303.

[26] Spackman, Kent A. (1989). “Signal detection theory: valuable tools for evaluating inductive Learning”. Proceedings of the Sixth International Workshop on Machine Learning. Morgan Kaufman, San Mateo, Calif, pp. 160-163.

[27] Pepe, Margaret S. (2003). “The Statistical Evaluation of Medical Tests for Classification and Prediction”. Oxford University Press, Oxford.

[28] Baker, S. G., B. S. Kramer. (2007). “Peirce, Youden, and Receiver Operation Characteristic Curves”. The American Statistician, 61: 343-346.

[29] Metz, C. E. (1986). ‘ROC Methodology in Radiologic Imaging’. Investigative Radiology, 21, 720-733.

[30] Lasko, T. A., Bhagwat, J. G., Zou, K. H., and Ohno-Machado, L. (2005). ‘The Use of Receiver Operating Characteristic Curves in Biomedical Informatics’. Journal of Biomedical Informatics, 38, 404-415.

[31] Bamber, J. D. (1975). “The Area Above the Ordinal Dominance Graph and the Area Below the Receiver Operating Characteristic Graph”. Journal of Mathematical Psychology, 12: 387-415.

[32] Hanley, J. A. and B. J. McNeil. (1982). “The Meaning and Use of the Area Under a Receiver Operating Characteristic Curve”. Radiology, 143: 29-36.

[33] Greiner, M., D. Pfeiffer, R. D. Smith. (2000). “Principles and Practical Application of the Receiver-Operating Characteristic Analysis for Diagnostic Tests.” Preventive Veterinary Medicine, 45: 23-41.

[34] Ulu, Y. (2013). “Multivariate test for forecast rationality under asymmetric loss functions: Recent evidence from MMS survey of inflation-output forecasts.” Economics Letters, 119: 2, 168-171.

How to cite this paper

Directional Accuracy of MMS Survey of Inflation-Output Forecasts of G7 Countries: A ROC Analysis

How to cite this paper: Yasemin Ulu. (2022) Directional Accuracy of MMS Survey of Inflation-Output Forecasts of G7 Countries: A ROC Analysis. Journal of Applied Mathematics and Computation6(1), 13-18.

DOI: http://dx.doi.org/10.26855/jamc.2022.03.003

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