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<p>The “retrieval from mixed frequency sampling” approach based on blocking—described e.g., in Anderson et al. (Econom Theory 32:793–826, 2016a)—is concerned with retrieving an underlying high frequency model from mixed frequency observations. In this paper, we investigate parameter-identifiability in the Johansen (Likelihood-based inference in cointegrated vector autoregressive models. Oxford University Press, Oxford, 1995) vector error correction model for mixed frequency data. We prove that from the second moments of the blocked process after taking differences at lag <i>N</i> (<i>N</i> is the slow sampling rate), the parameters of the high frequency system are generically identified. We treat the stock and the flow case.</p>

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Retrieval from mixed sampling frequency: generic identifiability in the unit root VAR

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  • 发表日期：

    2025年3月8日

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  • Philipp Gersing,
  • Leopold Sögner,
  • Manfred Deistler

摘要

The “retrieval from mixed frequency sampling” approach based on blocking—described e.g., in Anderson et al. (Econom Theory 32:793–826, 2016a)—is concerned with retrieving an underlying high frequency model from mixed frequency observations. In this paper, we investigate parameter-identifiability in the Johansen (Likelihood-based inference in cointegrated vector autoregressive models. Oxford University Press, Oxford, 1995) vector error correction model for mixed frequency data. We prove that from the second moments of the blocked process after taking differences at lag N (N is the slow sampling rate), the parameters of the high frequency system are generically identified. We treat the stock and the flow case.