Seasonality in macroeconomic time series

Hylleberg, Svend; Jørgensen, Clara; Sørensen, Nils Karl

doi:10.1007/bf01205406

Cited by 89 publications

(50 citation statements)

References 9 publications

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“…However, a body of research has recently shown that seasonal adjustment may alter such time series properties such as invertibility (Maravall, 1995), linearity (Ghysels et al, 1996), cointegration (Granger and Siklos, 1995), and short-run comovements (Cubadda, 1999). Since there is convincing evidence of seasonal unit roots in common macroeconomic time series (Hylleberg et al, 1993), it is important to model them properly. The common practise of adding seasonal dummies to the set of regressors leads to misspecified models when seasonal unit roots are present (Abeysinghe, 1994).…”

Section: Introductionmentioning

confidence: 99%

Small-sample improvements in the statistical analysis of seasonally cointegrated systems

Cubadda¹,

Omtzigt²

2005

Computational Statistics & Data Analysis

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New iterative reduced-rank regression procedures for seasonal cointegration analysis were proposed. The suggested methods are motivated by the idea that modelling the cointegration restrictions jointly at different frequencies may increase efficiency in finite samples. Monte Carlo simulations indicate that the new tests and estimators perform well with respect to already existing statistical procedures.

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Section: Introductionmentioning

confidence: 99%

Small-sample improvements in the statistical analysis of seasonally cointegrated systems

Cubadda¹,

Omtzigt²

2005

Computational Statistics & Data Analysis

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“…Hylleberg et al, 1993;Franses, 1996b). Am einfachsten Beispiel eines I(1)-Prozesses, dem Random Walk x t = x t−1 + ε t , kann leicht gezeigt werden, dass durch saisonales Differenzieren mit Hilfe von ∇ s ein nicht invertierbarer MA(s−1)-Prozess entsteht: ∇ s x t = x t − x t−s = ε t + ε t−1 + .…”

Section: Saisonale Unit-rootsunclassified

Prognose makroökonomischer Zeitreihen: Ein Vergleich linearer Modelle mit neuronalen Netzen

Köller¹

2014

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This thesis examines the potential of neural networks (NN) for forecasting of macroeconomic time series in comparison with linear models. The emphasis is on the autoregressive neural network (ARNN) model, which can be seen as a generalisation of the conventional autoregressive (AR) model where the non-linear part is implemented by a neural network of the feedforward type.Among the properties of macroeconomic time series that motivate various adaptations and enhancements both of linear and of neural network methodology are the following: high stochasticity, short length, non-stationary and seasonal behaviour and the fact that the existence of non-linearity is not clearly known a priori. The development and evaluation of the methods is based on simulated time series with known non-linear properties and on two macroeconomic time series: Austrian unemployment rate and Austrian industrial production index. Both series are in monthly observations and are seasonally unadjusted.After the introduction, the first main chapter of the thesis summarizes the basic concepts of linear univariate time series analysis and applies the linear methodology to the two selected macroeconomic time series. Particular attention is devoted to the problem of finding the appropriate differencing filter -conventional differences, seasonal differences or both -as a way to induce stationarity. Amongst other methods unit root tests are employed. The used linear models are the autoregressive (AR) and the autoregressive moving average (ARMA) model. These are augmented by a model part for deterministic seasonality. The models estimated on the two time series have rather high model order and, despite using a sparse modelling technique, comprise relatively many coefficients.The second main chapter treats various theoretic aspects and types of nonlinearity and carries out a sequence of hypothesis tests for non-linearity, in order to be able to rate the possible benefits of applying neural network methods to the two selected time series. The results of these tests indicate the presence of non-linear structure of the additive type, which can be exploited for better forecasting with ARNN models. However, these results have to be interpreted with care, as they might bei compromised by non-linearity of the multiplicative type, residual linear structure and possible structural breaks.The third main chapter introduces the ARNN model and develops methods for its estimation and specification. The specification contains a model part of determistic seasonality, includes a linear part and allows for sparse specification of coefficients. The generalisation capability of the ARNN model is to be ensured by four alternative model building approaches: the statistical-parametric approach with hypothesis testing and pruning, the classical approach with early stopping, the approach with regularisation and the Bayesian evidence framework. For each one of these approaches modifications and additional heuristics are contributed that seem appropriate in view of the a...

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“…Smilingly, Rudolph replies that Santa buys a lot of presents there. Hylleberg, Jorgensen, and Sorensen (1993) shows the corresponding trough in the …rst quarter for 12 out 15 countries, exceptions are the Netherlands, Sweden, and Taiwan. Similarly, Ghysels (1994) …nds a tendency for the trough e¤ect already starting in December.…”

Section: Model Of Cyclical Changes In Firm Conductmentioning

confidence: 99%

“…a boom in the fourth quarter and a following trough in the …rst quarter. Hylleberg, Jorgensen, and Sorensen (1993) …nd a fourth quarter boom for GDP for seven out of 15 countries (Australia, Finland, Japan, Norway, Sweden, Taiwan, and the US, but not for Argentina, Austria, Canada, Germany, Greece, Italy, the Netherlands, and the United Kingdom). "But Santa does not even deliver presents to Taiwan", Donner interrupts.…”

Section: Model Of Cyclical Changes In Firm Conductmentioning

confidence: 99%

Christmas Economics - A Sleigh Ride

Birg

Goeddeke

2014

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract Do you believe that at Christmas time the gas prices, the economy and the number of suicides peak? Do you think that the value of presents you are giving to your beloved is of importance? We show in this paper that conventional wisdom about Christmas is often doubtful. Furthermore, we give an idea of how Santa Claus -and maybe you -is able to …nance Christmas celebrations, why emergency departments are a place to especially avoid during this time of the year and why Christmas tree growers might care to explain the di¤erences across species to you this year. We cannot clearly establish whether Christmas entails a welfare loss or gain, however, we give you an idea as to which institutional settings might reduce a potential welfare loss. Also, we give advice about which behaviours might get you more Christmas presents from Santa this year. Finally, we …nd that more research is needed to give conclusive reasons why Santa Claus actually brings presents to (nearly) everyone. Terms of use: Documents in EconStor mayJEL classi…cation: A12, D1, D6, E3, H4, I1, Z1, XM45

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Seasonality in macroeconomic time series

Cited by 89 publications

References 9 publications

Small-sample improvements in the statistical analysis of seasonally cointegrated systems

Small-sample improvements in the statistical analysis of seasonally cointegrated systems

Prognose makroökonomischer Zeitreihen: Ein Vergleich linearer Modelle mit neuronalen Netzen

Christmas Economics - A Sleigh Ride

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