Testing for Unit Roots in Seasonal Time Series

Dickey, David A.; Hasza, David P.; Fuller, Wayne A.

doi:10.1080/01621459.1984.10478057

Cited by 379 publications

(172 citation statements)

References 11 publications

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“…Table 2 (Column 1), shows that seasonality takes the form of stochastic seasonality -and, more specifically, the form of seasonal unit root-in the cases of exchange rates LIT/USD, LIT/DEM (over the whole period, 1974-2001) and JPY/USD (over the sub-period 1974-90): in all these cases, the presence of a seasonal unit-root can not be rejected, according to the standard procedure à la Dickey, Hasza and Fuller [8]. On the opposite, the presence of a seasonal unit-root has to be rejected in all the cases pertaining Euro, that is for the exchange rates of Euro vs all considered moneys; a similar result of absence of seasonal unit root applies to the rates GBP/USD (1974-2010) and JPY/USD (for the recent subperiod 1991-2010).…”

Section: Data and Test For Seasonalitymentioning

confidence: 99%

Are Exchange Rates Really Free from Seasonality ? An Exploratory Analysis on Monthly Time Series

Cellini

Cuccia²

2011

TOECONSJ

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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: This study deals with the seasonality of monthly time series of nominal exchange rates. Available literature overlooks seasonality in nominal bilateral exchange rates, and generally assumes that such rates are non-seasonal. We show that seasonality is absent in recent data, while it was present in selected cases from the Seventies and Eighties of the past century. We suggest that these pieces of evidence are consistent with the general equilibrium models of exchange rate determination. Terms of use: Documents in EconStor mayJEL Classification: C22, F31.

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Section: Data and Test For Seasonalitymentioning

confidence: 99%

Are Exchange Rates Really Free from Seasonality ? An Exploratory Analysis on Monthly Time Series

Cellini

Cuccia²

2011

TOECONSJ

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“…Der DHF-Test (vgl. Dickey et al, 1984, wo auch die kritischen Werte der Verteilung der Teststatistik tabelliert sind) testet die Hypothese einer saisonalen Unit-Root gegen die Alternativhypothese, dass keine Unit-Root vorhanden ist. In einer derüblichen Versionen des Tests lautet die Schätzgleichung: 30) wobei die saisonalen Dummyvariablen s jt den Wert Eins annehmen, wenn die Beobachtung t der Saison j zu zuordnen ist, ansonsten den Wert Null.…”

Section: Saisonale Unit-rootsunclassified

“…Im Rahmen dieser Methode wird die Varianz der Ausgangszeitreihe Banerjee et al (1993), Kwiatkowski et al (1992), Dickey et al (1984) und Beaulieu und Miron (1993) …”

Section: Ergebnisse Für Die Beiden Beispielzeitreihenunclassified

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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“…To increase the unit root test robustness, we also conducted both the augmented Dickey-Fuller (ADF) test [27][28][29] and the Phillips-Perron (PP) test [30][31][32]. It has been suggested that most macroeconomic series are integrated of order one (I(1)) [33].…”

Section: Methodsmentioning

confidence: 99%

Price Discovery from the Chinese A-Share Market: Trend Break Tests Using the Perron Mixed Model C

Zou¹,

Yan²,

Chau³

2016

Proceedings of the 2016 International Conference on Economy, Management and Education Technology

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Testing for Unit Roots in Seasonal Time Series

Cited by 379 publications

References 11 publications

Are Exchange Rates Really Free from Seasonality ? An Exploratory Analysis on Monthly Time Series

Are Exchange Rates Really Free from Seasonality ? An Exploratory Analysis on Monthly Time Series

Prognose makroökonomischer Zeitreihen: Ein Vergleich linearer Modelle mit neuronalen Netzen

Price Discovery from the Chinese A-Share Market: Trend Break Tests Using the Perron Mixed Model C

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