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A PANIC Attack on Unit Roots and Cointegration Author info | Abstract | Publisher info | Download info | Related research | Statistics Jushan Bai () (Boston College)
Serena Ng () (Johns Hopkins University)
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This paper develops a new methodology that makes use of the factor structure of large dimensional panels to understand the nature of non-stationarity in the data. We refer to it as PANICÜ a 'Panel Analysis of Non-stationarity in Idiosyncratic and Common components'. PANIC consists of univariate and panel tests with a number of novel features. It can detect whether the nonstationarity is pervasive, or variable-specific, or both. It tests the components of the data instead of the observed series. Inference is therefore more accurate when the components have different orders of integration. PANIC also permits the construction of valid panel tests even when cross-section correlation invalidates pooling of statistics constructed using the observed data. The key to PANIC is consistent estimation of the components even when the regressions are individually spurious. We provide a rigorous theory for estimation and inference. In Monte Carlo simulations, the tests have very good size and power. PANIC is applied to a panel of inflation series.
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Paper provided by Boston College Department of Economics in its series Boston College Working Papers in Economics with number
519.
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Length: 40 pages
Date of creation: 01 Dec 2001Date of revision:
Handle: RePEc:boc:bocoec:519Contact details of provider: Postal: Boston College, 140 Commonwealth Avenue, Chestnut Hill MA 02467 USA Phone: 617-552-3670 Fax: +1-617-552-2308 Email: Web page: http://fmwww.bc.edu/EC/ More information through EDIRC
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Keywords: Panel data common factors common trends principal components Other versions of this item:
Find related papers by JEL classification: C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
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