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exuber: Recursive Right-Tailed Unit Root Testing with R

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Abstract

This paper introduces the R package exuber for testing and date-stamping periods of mildly explosive dynamics (exuberance) in time series. The package computes test statistics for the supremum ADF test (SADF) of Phillips, Wu and Yu (2011), the generalized SADF (GSADF) of Phillips, Shi and Yu (2015a,b), and the panel GSADF proposed by Pavlidis, Yusupova, Paya, Peel, Martínez-García, Mack and Grossman (2016); generates finite-sample critical values based on Monte Carlo and bootstrap methods; and implements the corresponding date-stamping procedures. The recursive least-squares algorithm that we introduce in our implementation of these techniques utilizes the matrix inversion lemma and in that way achieves significant speed improvements. We illustrate the speed gains in a simulation experiment, and provide illustrations of the package using artificial series and a panel on international house prices.

Suggested Citation

  • Enrique Martínez García & Efthymios Pavlidis & Kostas Vasilopoulos, 2020. "exuber: Recursive Right-Tailed Unit Root Testing with R," Globalization Institute Working Papers 383, Federal Reserve Bank of Dallas, revised 19 Oct 2021.
    • Handle: RePEc:fip:feddgw:87964
    DOI: 10.24149/gwp383r1
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    References listed on IDEAS

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    1. Pavlidis, Efthymios & Martínez-García, Enrique & Grossman, Valerie, 2019. "Detecting periods of exuberance: A look at the role of aggregation with an application to house prices," Economic Modelling, Elsevier, vol. 80(C), pages 87-102.
    2. Eddelbuettel, Dirk & Sanderson, Conrad, 2014. "RcppArmadillo: Accelerating R with high-performance C++ linear algebra," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1054-1063.
    3. Peter C. B. Phillips & Yangru Wu & Jun Yu, 2011. "EXPLOSIVE BEHAVIOR IN THE 1990s NASDAQ: WHEN DID EXUBERANCE ESCALATE ASSET VALUES?," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 52(1), pages 201-226, February.
    4. Ryan Greenaway‐McGrevy & Arthur Grimes & Mark Holmes, 2019. "Two countries, sixteen cities, five thousand kilometres: How many housing markets?," Papers in Regional Science, Wiley Blackwell, vol. 98(1), pages 353-370, February.
    5. Chang, Yoosoon, 2004. "Bootstrap unit root tests in panels with cross-sectional dependency," Journal of Econometrics, Elsevier, vol. 120(2), pages 263-293, June.
    6. repec:bla:obuest:v:61:y:1999:i:0:p:631-52 is not listed on IDEAS
    7. Alok Bhargava, 1986. "On the Theory of Testing for Unit Roots in Observed Time Series," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 53(3), pages 369-384.
    8. G. S. Maddala & Shaowen Wu, 1999. "A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(S1), pages 631-652, November.
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    Cited by:

    1. Janusz Sobieraj & Dominik Metelski, 2021. "Testing Housing Markets for Episodes of Exuberance: Evidence from Different Polish Cities," JRFM, MDPI, vol. 14(9), pages 1-29, September.
    2. Paul Mortimer-Lee, 2025. "The British Fiscal Morass and why the October Budget Made it Worse," National Institute of Economic and Social Research (NIESR) Policy Papers 43, National Institute of Economic and Social Research.

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    More about this item

    Keywords

    Mildly explosive time series; Right-tailed unit root tests; R;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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