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integrated models and deterministic seasonality models. As well as examining how forecasts are computed in each case, the …. Section 3 discusses less traditional models, specifically nonlinear seasonal models and models for seasonality in variance …. Such nonlinear models primarily concentrate on interactions between seasonality and the business cycle, either using a …
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Infra-monthly time series have increasingly appeared on the radar of official statistics in recent years, mostly as a consequence of a general digital transformation process and the outbreak of the COVID-19 pandemic in 2020. Many of those series are seasonal and thus in need for seasonal...
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We consider a nonparametric test for the null of seasonal unit roots in quarterly time series that builds on the RUR (records unit root) test by Aparicio, Escribano, and Sipols. We find that the test concept is more promising than a formalization of visual aids such as plots by quarter. In order...
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For many economic time-series variables that are observed regularly and frequently, for example weekly, the underlying activity is not distributed uniformly across the year. For the aim of predicting annual data, one may consider temporal aggregation into larger subannual units based on an...
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We take a simple time-series approach to modeling and forecasting daily average temperature in U.S. cities, and we inquire systematically as to whether it may prove useful from the vantage point of participants in the weather derivatives market. The answer is, perhaps surprisingly, yes....
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document seasonality in house prices for the US and the UK using formal statistical tests and illustrate its quantitative … shocks in non-durables in order to characterise seasonality in house prices as an equilibrium outcome. We provide empirical … evidence for seasonality in house prices with our small model using US and UK data. …
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