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This study is concerned with forecasting time series variables and the impact of the level of aggregation on the efficiency of the forecasts. The present study contains major extensions of that research and also summarizes the earlier results to the extent they are of interest in the context of this study.
This is the new and totally revised edition of Lutkepohl's classic 1991 work. It provides a detailed introduction to the main steps of analyzing multiple time series, model specification, estimation, model checking, and for using the models for economic analysis and forecasting.
This graduate level textbook deals with analyzing and forecasting multiple time series. It considers a wide range of multiple time series models and methods. The models include vector autoregressive, vector autoregressive moving average, cointegrated, and periodic processes as well as state space and dynamic simultaneous equations models.
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