Error Correction Models for Fractionally Cointegrated Time Series

Error correction models for fractionally cointegrated time series

This note provides a proof of Granger's (1986) error correction model for fractionally cointegrated variables and points out a necessary assumption that has not been noted before. Moreover, a simpler, alternative error correction model is proposed which can be employed to estimate fractionally cointegrated systems in three steps. JEL Classification Code: C32

First-Order Bias Correction for Fractionally Integrated Time Series

Complex Reduced Rank Models for Seasonally Cointegrated Time Series

This paper introduces a new representation for seasonally cointegrated variables, namely the complex error correction model, which allows statistical inference to be performed by reduced rank regression. The suggested estimators and tests statistics are asymptotically equivalent to their maximum likelihood counterparts. Tables are provided for both asymptotic and finite sample critical values, ...

Frequency Domain Principal Components Estimation of Fractionally Cointegrated Processes

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Which Methodology is Better for Combining Linear and Nonlinear Models for Time Series Forecasting?

Both theoretical and empirical findings have suggested that combining different models can be an effective way to improve the predictive performance of each individual model. It is especially occurred when the models in the ensemble are quite different. Hybrid techniques that decompose a time series into its linear and nonlinear components are one of the most important kinds of the hybrid model...