textabstractIn this paper we propose a model selection strategy for a univariate periodic autoregressive time series which involves tests for one or more unit roots and for parameter restrictions corresponding to seasonal unit roots and multiple unit roots at the zero frequency. Examples of models that are considered are variants of the seasonal unit roots model and the periodic integration model. We show that the asymptotic distributions of various test statistics are the same as well-known distributions which are already tabulated. We apply our strategy to three empirical series to illustrate its ease of use. We find that evidence for seasonal unit roots based on nonperiodic models disappears when periodic representations are considered
textabstractThis paper considers model selection and forecasting issues in two closely related model...
This paper proposes an extension of Periodic AutoRegressive (PAR) modelling for time series with evo...
Rsriodic autoregressive time series models [PAR] are models which allow the AR parameters to vary wi...
This paper analyses the presence and consequences of a unit root in periodic autoregressive models f...
textabstractA periodic autoregressive time-series model assumes that the autoregressive parameters v...
A comprehensive seasonally integrated periodic autoregressive model is suggested which is shown to b...
Les procédures standards pour tester la présence de racines unitaires aux fréquences saisonnières so...
textabstractThis book considers periodic time series models for seasonal data, characterized by para...
textabstractThis paper focuses on the issue of period autoagressive time series models (PAR) selecti...
Part of the increasing interest in the treatment of seasonality in economic time series has focused ...
textabstractThis paper is concerned with forecasting univariate seasonal time series data using peri...
This paper proposes bootstrap tests for the presence of unit roots in a seasonal autoregressive mode...
Book cover Mathematical and Statistical Methods for Actuarial Sciences and Finance pp 79–85Cite as ...
This paper addresses the problem of testing for the presence of unit autoregressive roots in seasona...
This chapter is concerned with forecasting univariate seasonal time series data using periodic autor...
textabstractThis paper considers model selection and forecasting issues in two closely related model...
This paper proposes an extension of Periodic AutoRegressive (PAR) modelling for time series with evo...
Rsriodic autoregressive time series models [PAR] are models which allow the AR parameters to vary wi...
This paper analyses the presence and consequences of a unit root in periodic autoregressive models f...
textabstractA periodic autoregressive time-series model assumes that the autoregressive parameters v...
A comprehensive seasonally integrated periodic autoregressive model is suggested which is shown to b...
Les procédures standards pour tester la présence de racines unitaires aux fréquences saisonnières so...
textabstractThis book considers periodic time series models for seasonal data, characterized by para...
textabstractThis paper focuses on the issue of period autoagressive time series models (PAR) selecti...
Part of the increasing interest in the treatment of seasonality in economic time series has focused ...
textabstractThis paper is concerned with forecasting univariate seasonal time series data using peri...
This paper proposes bootstrap tests for the presence of unit roots in a seasonal autoregressive mode...
Book cover Mathematical and Statistical Methods for Actuarial Sciences and Finance pp 79–85Cite as ...
This paper addresses the problem of testing for the presence of unit autoregressive roots in seasona...
This chapter is concerned with forecasting univariate seasonal time series data using periodic autor...
textabstractThis paper considers model selection and forecasting issues in two closely related model...
This paper proposes an extension of Periodic AutoRegressive (PAR) modelling for time series with evo...
Rsriodic autoregressive time series models [PAR] are models which allow the AR parameters to vary wi...