A new index for measuring seasonality: A transportation cost approach
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Seasonality
Seasonal adjustment
Seasonality
Seasonal adjustment
Industrial Production
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It is well known that mis-specification of a trend leads to spurious cycles in detrended data (see, e.g., Nelson and Kang (1981)). Seasonal-adjustment procedures make assumptions, either implicitly or explicitly, about roots on the unit circle both at the zero and seasonal frequencies. Consequently, seasonal-adjustment procedures may produce spurious seasonal variation and other statistically undesirable effects. In this paper we document for a large class of widely used US quarterly macroeconomic series the effects of competing seasonal-adjustment procedures on the univariate time-series properties of the adjusted series. We also investigate which procedures are most appropriate given the properties of the data. Overall, we find very significant differences and evidence that several U.S. macroeconomic time series contain a mixture of deterministic and stochastic seasonal components.
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Univariate
Seasonal adjustment
Seasonality
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The use of chain-linked methods reduces significantly the problem of price structure obsolescence present in fixed base environments. However, price updating introduces a new dimension that may produce confusion if not accounted for. Probably the most notorious difficulty generated by the introduction of chain-linked indices to the measurement of GDP has been that the aggregate is not the direct sum of its components, thus not only making it harder to explain its behaviour but also making it more cumbersome to work with the series in a consistent manner. Because of the non-additivity of the components, one of the processes that have been affected is that of the indirect seasonal adjustment. This document presents a consistent framework to identify and track down the sources of seasonal effects to its components in an aggregate measure chain-linked using the annual overlap method. This is done based on the decomposition of component’s contributions and the indirect seasonal adjustment. The framework allows separating the effects on growth rates into non-systematic seasonal effects, systematic seasonality and changes in systematic seasonality.
Seasonal adjustment
Obsolescence
Seasonality
Chain (unit)
Component (thermodynamics)
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The use of chain-linked methods reduces significantly the problem of price structure obsolescence present in fixed base environments. However, price updating introduces a new dimension that may produce confusion if not accounted for. Probably the most notorious difficulty generated by the introduction of chain-linked indices to the measurement of GDP has been that the aggregate is not the direct sum of its components, thus not only making it harder to explain its behaviour but also making it more cumbersome to work with the series in a consistent manner. Because of the non-additivity of the components, one of the processes that have been affected is that of the indirect seasonal adjustment. This document presents a consistent framework to identify and track down the sources of seasonal effects to its components in an aggregate measure chain-linked using the annual overlap method. This is done based on the decomposition of component’s contributions and the indirect seasonal adjustment. The framework allows separating the effects on growth rates into non-systematic seasonal effects, systematic seasonality and changes in systematic seasonality.
Seasonal adjustment
Seasonality
Obsolescence
Chain (unit)
Component (thermodynamics)
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Abstract. In this paper we review recent developments in econometric modelling of economic time series with seasonality. The prime focus is on econometric models which incorporate explicit descriptions of seasonal variation, instead of removing this variation using a seasonal adjustment method. This review centres around developments in seasonal unit root models and in periodic parameter models, both in the univariate and multivariate context. Several empirical examples are used for illustration. We also discuss several areas for further research.
Seasonality
Univariate
Seasonal adjustment
Variation (astronomy)
Econometric model
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Multivariate analysis can help to focus on economic phenomena, including trend and cyclical movements. To allow for potential correlation with seasonality, the present paper studies a three component multivariate unobserved component model, focusing on the case of quarterly data and showing that economic restrictions, including common trends and common cycles, can ensure identification. Applied to seasonal aggregate gender employment in Australia, a bivariate male/female model with a common cycle is preferred to both univariate correlated component and bivariate uncorrelated component specifications. This model evidences distinct gender-based seasonal patterns with seasonality declining over time for females and increasing for males.
Univariate
Seasonality
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Uncorrelated
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This paper examines the implications of treating seasonality as an unobserved component which changes slowly over time. This approach simplifies the specification of dynamic relationships by separating non-seasonal from seasonal factors. We illustrate this approch using the consumption model of Davidson et al (1978) and estimate a stable error correction model between consumption, income and prices over the period 1958-92. More generally, we argue that autoregressive models are unlikely to successfully model slowly changing seasonality, and may confound seasonal effects with the dynamic responses of prime interest. Our approach can be used in a wide range of cases and we show that there is little loss in efficiency even if seasonality is deterministic
Seasonality
Consumption
Seasonal adjustment
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Spurious relationship
Seasonal adjustment
Univariate
Seasonality
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This paper examines the implications of treating seasonality as an unobserved component which changes slowly over time. This approach simplifies the specification of dynamic relationships by separating non-seasonal from seasonal factors. We illustrate this approach using the consumption model of Davidson et al (1978) and estimate a stable error correction model between consumption, income and prices over the period 1958-92. More generally, we argue that autoregressive models are unlikely to successfully model slowly changing seasonality, and may confound seasonal effects with the dynamic responses of prime interest. Our approach can be used in a wide range of cases and we show that there is little loss in efficiency even if seasonality is deterministic.
Seasonality
Seasonal adjustment
Consumption
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Seasonal Effects in Qualitative Responses of Monthly Business Surveys, Modeled with Log-Linear Probability Methods, Can Be Extracted Like in Linear Regression Or Some Time Series Models by Using Dummy Variables. There Is Another Route to Extract the Seasonal Effect, Which Is to Unique to Surveys, by Incorporating Questions on the Questionnaires Which Force Respondents to Make a Distinction Between Seasonal and Cyclical Effects. the Belgian, French and German Surveys Allowed Us to Compare the Alternative Approaches to Seasonality. We Estimated a Model of Micro-Based Firm Behavior and Found the Approach with Survey-Based Seasonal Measures More Desirable. Dummy Variables Pick Up Aggregate Seasonal Effects Across Industries. the Advantage of Survey-Based Measures of Seasonal Effects Is That They Are Firm-Specific Observations Instead of Aggregate and Hence Tend to More Precise. Moreover, the Responses to Requests to Separate Seasonal From Cyclical Effects Seems to Be Fairly Consistent.
Seasonality
Seasonal adjustment
Survey data collection
Aggregate data
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