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System Estimation of Panel Data Models under Long-Range Dependence

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A general dynamic panel data model is considered that incorporates individual and interactive fixed effects allowing for contemporaneous correlation in model innovations. The model accommodates general stationary or nonstationary long-range dependence through interactive fixed effects and innovations, removing the necessity to perform a priori unit-root or stationarity testing. Moreover, persistence in innovations and interactive fixed effects allows for cointegration; innovations can also have vector-autoregressive dynamics; deterministic trends can be featured. Estimations are performed using conditional-sum-of-squares criteria based on projected series by which latent characteristics are proxied. Resulting estimates are consistent and asymptotically normal at standard parametric rates. A simulation study provides reliability on the estimation method. The method is then applied to the long-run relationship between debt and GDP. Supplementary materials for this article are available online.

Original languageEnglish
JournalJournal of Business and Economic Statistics
Pages (from-to)13-26
Number of pages14
Publication statusPublished - 2 Jan 2019

    Research areas

  • Debt and GDP, Endogeneity, Factor models, Fixed effects, Long memory, Panel data, ROOT, INFERENCE, WEAK, FRACTIONAL SYSTEMS, COINTEGRATION, MEMORY, AGGREGATION

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