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Aggregate Output Measurements: a Common Trend Approach

Martin Almuzara, Gabriele Fiorentini () and Enrique Sentana ()

No 2021_03, Econometrics Working Papers Archive from Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti"

Abstract: We analyze a model for N different measurements of a persistent latent time series when measurement errors are mean-reverting, which implies a common trend among measure-ments. We study the consequences of overdifferencing, finding potentially large biases in maximum likelihood estimators of the dynamics parameters and reductions in the preci-sion of smoothed estimates of the latent variable, especially for multiperiod objects such as quinquennial growth rates. We also develop an R 2 measure of common trend observability that determines the severity of misspecification. Finally, we apply our framework to US quarterly data on GDP and GDI, obtaining an improved aggregate output measure.

Keywords: Cointegration; GDP; GDI; Overdifferencing; Signal Extraction (search for similar items in EconPapers)
JEL-codes: C32 E01 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2021-02
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-mac and nep-ore
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Working Paper: Aggregate Output Measurements: A Common Trend Approach (2021) Downloads
Working Paper: Aggregate Output Measurements: A Common Trend Approach (2021) Downloads
Working Paper: Aggregate output measurements: a common trend approach (2021) Downloads
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