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Forecasting Aggregated Vector ARMA Processes

This study is concerned with forecasting time series variables and the impact of the level of aggregation on the efficiency of the forecasts. Since temporally and contemporaneously disaggregated data at various levels have become available for many countries, regions, and variables during the last d...

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Bibliographic Details
Main Author: Lütkepohl, Helmut
Format: eBook
Language:English
Published: Berlin, Heidelberg Springer Berlin Heidelberg 1987, 1987
Edition:1st ed. 1987
Series:Lecture Notes in Economics and Mathematical Systems
Subjects:
Quantitative Economics
Econometrics
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
Table of Contents:
  • 7.7 Concluding Remarks
  • 7.A Appendix: Proof of Relation (7.2.18)
  • 8. Temporal Aggregation of Flow Variables
  • 8.1 Forecasting with Known Processes
  • 8.2 Forecasts Based on Processes with Estimated Coefficients
  • 8.3 Forecasting with Autoregressive Processes of Unknown Order
  • 8.4 Temporally Aggregated Nonstationary Processes
  • 8.5 Small Sample Comparison
  • 8.6 Examples
  • 8.7 Summary and Conclusions
  • 8.A Appendix: Proof of Relation (8.2.23)
  • 9. Joint tTemporal and Contemporaneous Aggregation
  • 9.1 Summary of Processes and Predictors
  • 9.2 Prediction Based on Processes with Estimated Coefficients
  • 9.3 Prediction Based on Estimated Processes with Unknown Orders
  • 9.4 Monte Carlo Comparison of Predictors
  • 9.5 Forecasts of U.S. Gross Private Domestic Investment
  • 9.6 Summary and Conclusions
  • 10. Epilogue
  • 10.1 Summary and Conclusions
  • 10.2 Some Remaining Problems
  • Appendix. Data Used for Examples
  • 5. Forecasting Contemporaneously Aggregated Estimated Processes
  • 5.1 Summary of Assumptions and Predictors
  • 5.2 Estimated Coefficients
  • 5.3 Unknown Orders and Estimated Coefficients
  • 5.4 Nonstationary Processes
  • 5.5 Small Sample Results
  • 5.6 An Empirical Example
  • 5.7 Conclusions
  • 6. Forecasting Temporally and Contemporaneously Aggregated Known Processes
  • 6.1 Macro Processes
  • 6.2 Six Predictors
  • 6.3 Comparison of Predictors
  • 6.4 Nonstationary Processes
  • 6.5 Temporally and Contemporaneously Aggregated Vector ARMA Processes
  • 6.6 Conclusions and Comments
  • 7. Temporal Aggregation of Stock Variables - Systematically Missing Observations
  • 7.1 Forecasting Known Processes with Systematically Missing Observations
  • 7.2 Processes With Estimated Coefficients
  • 7.3 Processes With Unknown Orders and Estimated Coefficients
  • 7.4 Nonstationary Time Series with Systematically Missing Observations
  • 7.5 Monte Carlo Results
  • 7.6 Empirical Examples
  • 1. Prologue
  • 1.1 Objective of the Study
  • 1.2 Survey of the Study
  • 2. Vector Stochastic Processes
  • 2.1 Discrete-Time, Stationary Vector Stochastic Processes
  • 2.2 Nonstationary Processes
  • 2.3 Vector Autoregressive Moving Average Processes
  • 2.4 Estimation
  • 2.5 Model Specification
  • 2.6 Summary
  • 3. Forecasting Vector Stochastic Processes
  • 3.1 Forecasting Known Processes
  • 3.2 Forecasting Vector ARMA Processes with Estimated Coefficients
  • 3.3 Forecasting Autoregressive Processes of Unknown Order
  • 3.4 Forecasting Nonstationary Processes
  • 3.5 Comparing Forecasts
  • 3.6 Summary
  • 4. Forecasting Contemporaneously Aggregated Known Processes
  • 4.1 Linear Transformations of Vector Stochastic Processes
  • 4.2 Forecasting Linearly Transformed Stationary Vector Stochastic Processes
  • 4.3 Forecasting Linearly Transformed Nonstationary Processes
  • 4.4 Linearly Transformed Vector ARMA Processes
  • 4.5 Summary and Comments

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