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Getting the ROC into Sync

Author

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  • Liu Yang
  • Kajal Lahiri
  • Adrian Pagans

Abstract

Judging the conformity of binary events in macroeconomics and finance has often been done with indices that measure synchronization. In recent years, the use of Receiver Operating Characteristic (ROC) curve has become popular for this task. This paper shows that the ROC and synchronization approaches are closely related, and each can be represented as a weighted average of correlation coefficients between a set of binary indicators and the target event. An advantage of such a representation is that inferences on the degree of conformity can be made robust to serial dependence in the underlying series in the standard framework of a linear regression model. Such serial correlation is common in macroeconomic and financial data.

Suggested Citation

  • Liu Yang & Kajal Lahiri & Adrian Pagans, 2022. "Getting the ROC into Sync," CAMA Working Papers 2022-01, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    • Handle: RePEc:een:camaaa:2022-01
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    File URL: https://cama.crawford.anu.edu.au/sites/default/files/publication/cama_crawford_anu_edu_au/2022-01/1_2022_yang_lahiri_pagan.pdf
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    Keywords

    Receiver operating characteristic curve; Synchronization; Correlation; Economic recession; Serial dependence;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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