Seasonality Tests

Busetti, Fabio; Harvey, Andrew

doi:10.1198/073500103288619061

Cited by 37 publications

(44 citation statements)

References 19 publications

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“…This descriptive evidence can be supported by formal statistical tests, such as the Canova -Hansen (1995) and Busetti and Harvey (2003) test, concerning the presence and the nature of the seasonal movements. A related issue is whether the responses are affected by the number of working days in the month and any other calender effect, such as the length of the month and Easter.…”

Section: Seasonalitymentioning

confidence: 83%

“…Busetti and Harvey (2003) derive the locally best invariant test of the null that there is no seasonality against a permanent seasonal component, that can be either deterministic or stochastic, or both. The seasonal component is decomposed into a deterministic term, a linear combination with fixed coefficients of sines and cosines defined at the seasonal frequencies λ j = 2πj/s, j = 1...[s/2], where s is the number of seasons in a year (e.g.…”

Section: Seasonalitymentioning

confidence: 99%

“…The test statistic proposed by Busetti and Harvey (2003) is consistent against both alternative hypotheses, and it is computed as follows:…”

Section: Seasonalitymentioning

confidence: 99%

See 2 more Smart Citations

New proposals for the quantification of qualitative survey data

Proietti

Frale

2011

Journal of Forecasting

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In this paper we deal with several issues related to the quantification of business surveys. In particular, we propose and compare new ways of scoring the ordinal responses concerning the qualitative assessment of the state of the economy, such as the spectral envelope and cumulative logit unobserved components models, and investigate the nature of seasonality in the series. We conclude with an evaluation of the type of business cycle fluctuations that is captured by the qualitative surveys.

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Section: Seasonalitymentioning

confidence: 83%

Section: Seasonalitymentioning

confidence: 99%

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New proposals for the quantification of qualitative survey data

Proietti

Frale

2011

Journal of Forecasting

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“…When seasonality is absent, unit root tests, see Dickey and Fuller (1979) and Phillips and Perron (1988), test the null of integration versus a stationary alternative see De Jong and Whiteman (1991), Koop (1992), Sims (1988), Sims and Uhlig (1991), Phillips (1991), Schotman and van Dijk (1991), Phillips and Perron (1994), among others, for the Bayesian approach to unit root testing; on the contrary, the tests proposed by Nyblom and Makelainen (1983) and Kwiatkowski et al (1992) test trend stationarity against the alternative of integration. Unit root tests were extended to the seasonal case by Hylleberg et al (1990), whereas the extension for stationarity tests was proposed by Canova and Hansen (1995), and Busetti and Harvey (2003). Other important references on whether seasonality is stochastically evolving over time include Hylleberg and Pagan (1997) and Koop and van Dijk (2000).…”

Section: Bayesian Stochastic Specification Searchmentioning

confidence: 99%

“…One approach is performing the class of seasonal unit root tests proposed by Hylleberg et al (1990), which is based on the finite autoregressive representation of the series and tests for the presence of roots with unit modulus and zero or seasonal phase in the autoregressive polynomial. An alternative approach is to carry out the stationarity tests proposed by Canova and Hansen (1995) and extended by Busetti and Harvey (2003).…”

Section: Introductionmentioning

confidence: 99%

Stochastic trends and seasonality in economic time series: new evidence from Bayesian stochastic model specification search

Proietti

Grassi

2014

Empir Econ

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An important issue in modelling economic time series is whether key unobserved components representing trends, seasonality and calendar components, are deterministic or evolutive. We address it by applying a recently proposed Bayesian variable selection methodology to an encompassing linear mixed model that features, along with deterministic effects, additional random explanatory variables that account for the evolution of the underlying level, slope, seasonality and trading days. Variable selection is performed by estimating the posterior model probabilities using a suitable Gibbs sampling scheme.The paper conducts an extensive empirical application on a large and representative set of monthly time series concerning industrial production and retail turnover. We find strong support for the presence of stochastic trends in the series, either in the form of a time-varying level, or, less frequently, of a stochastic slope, or both. Seasonality is a more stable component: only in 70% of the cases we were able to select at least one stochastic trigonometric cycle out of the six possible cycles. Most frequently the time variation is found in correspondence with the fundamental and the first harmonic cycles.An interesting and intuitively plausible finding is that the probability of estimating time-varying components increases with the sample size available. However, even for very large sample sizes we were unable to find stochastically varying calendar effects. AbstractAn important issue in modelling economic time series is whether key unobserved components representing trends, seasonality and calendar components, are deterministic or evolutive. We address it by applying a recently proposed Bayesian variable selection methodology to an encompassing linear mixed model that features, along with deterministic effects, additional random explanatory variables that account for the evolution of the underlying level, slope, seasonality and trading days. Variable selection is performed by estimating the posterior model probabilities using a suitable Gibbs sampling scheme.The paper conducts an extensive empirical application on a large and representative set of monthly time series concerning industrial production and retail turnover. We find strong support for the presence of stochastic trends in the series, either in the form of a time-varying level, or, less frequently, of a stochastic slope, or both. Seasonality is a more stable component: only in 70% of the cases we were able to select at least one stochastic trigonometric cycle out of the six possible cycles. Most frequently the time variation is found in correspondence with the fundamental and the first harmonic cycles.An interesting and intuitively plausible finding is that the probability of estimating time-varying components increases with the sample size available. However, even for very large sample sizes we were unable to find stochastically varying calendar effects.

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Risk premia and seasonality in commodity futures

Hevia

Petrella

Solà

2018

J of Applied Econometrics

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Summary We develop and estimate a multifactor affine model of commodity futures that allows for stochastic seasonality. We document the existence of stochastic seasonal fluctuations in commodity futures and that properly accounting for the cost‐of‐carry curve requires at least three factors. We estimate the model using data on heating oil futures and analyze the contribution of the factors to risk premia. Correctly specifying seasonality as stochastic is important to avoid erroneously assigning those fluctuations to other risk factors. We also estimate a nonlinear version of the model that imposes the zero lower bound on interest rates and find similar results.

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Seasonality Tests

Cited by 37 publications

References 19 publications

New proposals for the quantification of qualitative survey data

New proposals for the quantification of qualitative survey data

Stochastic trends and seasonality in economic time series: new evidence from Bayesian stochastic model specification search

Risk premia and seasonality in commodity futures

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