The effects of institutional and technological change and business cycle fluctuations on seasonal patterns in quarterly industrial production series

Dijk, Dick van; Strikholm, Birgit; Teräsvirta, Timo

doi:10.1111/1368-423x.00103

Cited by 52 publications

(18 citation statements)

References 28 publications

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“…Although non-linear models can capture some characteristics of structural break models Potter, 2000, 2001;Carrasco, 2002), it may be the case that the break also implies changes in the parameters that determine the non-linearity. Univariate time-varying smooth transition models have been proposed by Lundbergh et al (2003) and have been applied to capture changes in seasonality of industrial production by Van Dijk et al (2003). In this section, a VAR with threshold non-linearity and a structural break is proposed.…”

Section: Structural Break Threshold Varsmentioning

confidence: 99%

Structural break threshold VARs for predicting US recessions using the spread

Galvão¹

2006

J of Applied Econometrics

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SUMMARYThis paper proposes a model to predict recessions that accounts for non-linearity and a structural break when the spread between long-and short-term interest rates is the leading indicator. Estimation and model selection procedures allow us to estimate and identify time-varying non-linearity in a VAR. The structural break threshold VAR (SBTVAR) predicts better the timing of recessions than models with constant threshold or with only a break. Using real-time data, the SBTVAR with spread as leading indicator is able to anticipate correctly the timing of the 2001 recession.

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Section: Structural Break Threshold Varsmentioning

confidence: 99%

Structural break threshold VARs for predicting US recessions using the spread

Galvão¹

2006

J of Applied Econometrics

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“…Changing seasonality of time series was first noted in the nineteenth century (Gilbart 1852, quoted in Bell andHillmer 1984), and is common in macroeconomic data (Canova and Ghysels 1994;Wells 1997;Franses and Koehler 1998;Van Dijk et al 2003). Such changes can be due to variations in seasonal amplitude from year to year or in the proportionality relationship between the seasonal at each month and the seasonal at each other month (i.e., the seasonal pattern) (Godfrey and Karreman 1964).…”

Section: Introductionmentioning

confidence: 97%

Design of Seasonal Adjustment Filter Robust to Variations in the Seasonal Behaviour of Time Series

Martelotte

Souza

Silva

2017

Journal of Official Statistics

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Considering that many macroeconomic time series present changing seasonal behaviour, there is a need for filters that are robust to such changes. This article proposes a method to design seasonal filters that address this problem. The design was made in the frequency domain to estimate seasonal fluctuations that are spread around specific bands of frequencies.We assessed the generated filters by applying them to artificial data with known seasonal behaviour based on the ones of the real macroeconomic series, and we compared their performance with the one of X-13A-S. The results have shown that the designed filters have superior performance for series with pronounced moving seasonality, being a good alternative in these cases.

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“…Although seasonal fluctuations and business cycles are traditionally assumed to be uncorrelated, for some macro-economic series there is increasing evidence that this assumption is not valid. For example Cecchetti et al (1997), Franses and de Bruin (1999), van Dijk et al (2003) and Osborn and Matas-Mir (2004) have found varying amounts of interactions between cycles and seasonal adjustment in unemployment and industrial production series using linear or non-linear smooth transition autoregression models. With a straightforward extension of our trend-season interaction model, we also examine interactions between the seasonal component and the business cycle.…”

Section: Introductionmentioning

confidence: 99%

Seasonality with Trend and Cycle Interactions in Unobserved Components Models

Koopman

Lee²

2008

SSRN Journal

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Summary. Unobserved components time series models decompose a time series into a trend, a season, a cycle, an irregular disturbance, and possibly other components. These models have been successfully applied to many economic time series. The standard assumption of a linear model, often appropriate after a logarithmic transformation of the data, facilitates estimation, testing, forecasting and interpretation. However, in some settings the linear-additive framework may be too restrictive. In this paper, we formulate a non-linear unobserved components time series model which allows interactions between the trend-cycle component and the seasonal component. The resulting model is cast into a non-linear state space form and estimated by the extended Kalman filter, adapted for models with diffuse initial conditions. We apply our model to UK travel data and US unemployment and production series, and show that it can capture increasing seasonal variation and cycle dependent seasonal fluctuations.

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The effects of institutional and technological change and business cycle fluctuations on seasonal patterns in quarterly industrial production series

Cited by 52 publications

References 28 publications

Structural break threshold VARs for predicting US recessions using the spread

Structural break threshold VARs for predicting US recessions using the spread

Design of Seasonal Adjustment Filter Robust to Variations in the Seasonal Behaviour of Time Series

Seasonality with Trend and Cycle Interactions in Unobserved Components Models

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