On seasonal error correction when the processes include different numbers of unit roots

Arbeitspapier

We propose a seasonal cointegration model [SECM] for quarterly data which includes variables with different numbers of unit roots and thus needs to be transformed in different ways in order to yield stationarity. A Monte Carlo simulation is carried out to investigate the consequences of specifying a SECM with all variables in annual diffrerences in this situation. The SECM in annual differences is compared to the correctly specified model. Pre-testing for unit roots using two different approaches, and where the models are specified according to the unit root test results, is also considered. The forecast mean squared error criterion and certain parameter estimation results indicate that, in practice, a cointegration model where all variables are transformed with the annual difference filter is more robust than one obtained by pre-testing for a smaller number of unit roots. The second best choice, when the true model is not known and when the aim is to forecast, is an ordinary VAR model, also in annual differences.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: SSE/EFI Working Paper Series in Economics and Finance ; No. 418

Classification and Topics

• Classification: Wirtschaft ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Forecasting Models; Simulation Methods
• Subject: Seasonal cointegration ; forecasting ; Kointegration ; Unit Root Test ; Theorie ; Fehlerkorrekturmodell

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Lyhagen, Johan ; Löf, Mårten
• Event: Veröffentlichung
• (who): Stockholm School of Economics, The Economic Research Institute (EFI)
• (where): Stockholm
• (when): 2001

Further information

• Handle: http://hdl.handle.net/10419/56135
• Last update: 10.03.2025, 11:42 AM CET