X11-like Seasonal Adjustment of Daily Data

Ladiray, Dominique; Mazzi, Gian Luigi

doi:10.4995/carma2018.2018.8574

Cited by 20 publications

(23 citation statements)

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“…In this section, we present the econometric methodology used in the paper. Modeling economic daily time series poses several and difficult challenges due to the coexistence of multiple seasonal components linked to various frequencies, the complex structure of the calendar, 2 the strength of its irregular component and its sensitivity to exogenous factors (e.g., outliers) that distort its usual behavior (Ladiray et al 2018 ).…”

Section: Econometric Methodsmentioning

confidence: 99%

“…Seasonal adjustment (SA) of daily data is necessary, in the same way as happens to monthly and quarterly data, in order to provide a meaningful signal of its underlying evolution. However, this task is notably more difficult than in the case of monthly or quarterly data due to the complexity of its seasonal component, formed by several subcomponents, some of them linked to fractional periodicities and its noisy nature (Ladiray et al 2018 ).…”

Section: Introductionmentioning

confidence: 99%

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Seasonal adjustment of the Spanish sales daily data

Cuevas¹,

Ledo²,

Quilis³

2021

SERIEs

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We present a procedure to perform seasonal adjustment over daily sales data. The model adjusts daily information from the Immediate Supply of Information System for Value Added Tax declaration forms compiled by the Spanish Tax Agency. The procedure performs signal extraction and forecasting at the daily frequency, by means of an unobserved components model. The daily information allows a permanently updated monitoring of the short-term economic conditions of the Spanish economy.

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Section: Econometric Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Seasonal adjustment of the Spanish sales daily data

Cuevas¹,

Ledo²,

Quilis³

2021

SERIEs

View full text Add to dashboard Cite

show abstract

“…More broadly, Ladiray et al (2018) discuss the drawbacks of taking annual growth for weekly data and point out that it not only include a phase shift by design but also can introduce spurious cycles.…”

Section: Handling High-frequency Datamentioning

confidence: 99%

Nowcasting World GDP Growth with High-Frequency Data

Jardet

Meunier

2020

SSRN Journal

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The Covid-19 crisis has shown how high-frequency data can help tracking economic turning points in real-time. Our paper investigates whether high-frequency data can also improve the nowcasting performances for world GDP growth on quarterly or annual basis. To this end, we select a large dataset of 151 monthly and 39 weekly series for 17 advanced and emerging countries representing 68% of world GDP. Our approach builds on a Factor-Augmented MIxed DAta Sampling (FA-MIDAS) which allows us to take advantage of our large database and to combine different frequencies. Models that include weekly data significantly outperforms other models relying on monthly or quarterly indicators, both in-and out-ofsample. Breaking down our sample, we show that models with weekly data have similar nowcasting performances relative to other models during "normal" times but strongly outperform them during "crisis" episodes (2008-2009 and 2020). We finally construct a nowcasting model of annual world GDP growth incorporating weekly data which give timely (one every week) and accurate forecasts (close to IMF and OECD projections, but with a 1 to 3 months lead). Policy-wise, this model can provide an alternative "benchmark" projection for world GDP growth during crisis episodes when sudden swings in the economy make the usual "benchmark" projections (from the IMF or the OECD) rapidly outdated.

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“…It introduces also a variable selection method for the identification of the relevant harmonic cycles. Obviously, other approaches are available in the literature; for the nonparametric approach see Ladiray et al (2018), and the references therein.…”

Section: Introductionmentioning

confidence: 99%

Seasonality in High Frequency Time Series

Proietti

Pedregal

2021

SSRN Journal

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Time series observed at higher frequencies than monthly frequency display complex seasonal patterns that result from the combination of multiple seasonal patterns (with annual, monthly, weekly and daily periodicities) and varying periods, due to the irregularity of the calendar. The paper deals with modelling seasonality in high frequency data from two main perspectives: the stochastic harmonic approach, based on the Fourier representation of a periodic function, and the time-domain random effects approach. An encompassing representation illustrates the conditions under which they are equivalent. Three major challenges are considered: the first deals with modelling the effect of moving festivals, holidays and other breaks due to the calendar. Secondly, robust estimation and filtering methods are needed to tackle the level of outlier contamination, which is typically high, due to the lower level of temporal aggregation and the raw nature of the data. Finally, we focus on model selection strategies, which are important, as the number of harmonic or random components that are needed to account for the complexity of seasonality can be very large.

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X11-like Seasonal Adjustment of Daily Data

Cited by 20 publications

References 0 publications

Seasonal adjustment of the Spanish sales daily data

Seasonal adjustment of the Spanish sales daily data

Nowcasting World GDP Growth with High-Frequency Data

Seasonality in High Frequency Time Series

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