Some Consequences of Temporal Aggregation in Empirical Analysis

By Massimiliano Marcellino. 1999

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Citations in the corpus

Causal Discovery from Temporally Aggregated Time Series (2017, background): “Numerous contributions have been made on analyzing the effects of the above two schemes to generate low-resolution data on the properties of the time series such as estimated causal relations and exogeneity (Tiao, 1972; Weiss, 1984; Granger, 1987; Marcellino, 1999; Rajaguru & Abeysinghe, 2008).”
Testing for Granger Causality with Mixed Frequency Data (2016, unknown): “Here our analysis extends Marcellino (1999).”
Discovering Temporal Causal Relations from Subsampled Data (2015, background): “As (3)More precisely, it gives a comprehensive study on the effects of temporal aggregation on exogeneity, causality, cointegration, unit roots, seasonal unit roots, impulse response functions, and trend-cycles decompositions; it finds that cointegration and unit roots are invariant to temporal aggr”
Temporal aggregation of univariate and multivariate time series models: a survey (2008, unknown): “This example confirms that, as pointed out by Marcellino (1999), causality is not a time series property invariant to temporal aggregation.”
Testing for Granger Causality in Large Mixed-Frequency VARs (2016, unknown): “Additionally, aggregating a high-frequency variable may lead to ’spurious’ (non-)causality in the common low-frequency setup (Breitung and Swanson, 2002), since causality is a property which is not invariant to temporal aggregation (Marcellino, 1999 or Sims, 1971).”
Temporal aggregation and spurious instantaneous causality in multiple time series models (2002, background): “Other important contributions include Zellner and Montmarquette (1971), Stram and Wei (1986), Lütkepohl (1987), Weiss (1984), and Marcellino (1999), to name but a few.”
Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials (2015, methodology): “The method that we adopt to derive the generating mechanism for y at LF is similar to that introduced by Brewer (1973), refined by Wei (1981) and Weiss (1984), and further extended by Marcellino (1999) to deal with general aggregation schemes and multivariate processes.”
Granger causality and the sampling of economic processes (2006, methodology): “Tilburg: Vakgroep CentER.”
Exchange Rates and Commodity Prices : Measuring Causality at Multiple Horizons (2015, background): “…invariant to aggregation: highfrequency data may reveal patterns which are aggregated away in low-frequency data, and causality in low-frequency data can also be spurious; see Tiao and Wei (1976),Wei (1982, 1990), Marcellino (1999), Breitung and Swanson (2002), and Silvestrini and Veredas (2008).”
Cointegration Analysis with Mixed-Frequency Data (2007, methodology): “For example, see Marcellino (1999) and Pons and Sansó (2005) for the explicit formulas for the relationship in several cases.”
Forecasting Euro Area Inflation: Does Aggregating Forecasts by Hicp Component Improve Forecast Accuracy? (2003, unknown): “Recently, there has been renewed interest in the consequences of temporal aggregation for empirical analysis (see Marcellino (1999)).”
Temporal aggregation of multivariate GARCH processes (2004, background): “There is a substantial literature on the effects of temporal aggregation for causality between time series, see e.g. Marcellino (1999) for a recent overview and references.”
Quarterly Real GDP Estimates for China and ASEAN4 with a Forecast Evaluation (2004, methodology): “( Marcellino, 1999 ) the most recommendable approach to disaggregating a time series”