Spectral Analysis of Economic Time Series. (PSME-1)

Foreword
Preface
Contents
Chapter 1. Introduction to the Analysis of Time Series
	1.1 Introduction
	1.2 Concise History of Time Series Analysis
	1.3 Plan of the Book
	References
Chapter 2. Nature of Economic Time Series
	2.1 Classification of Economic Series from a Statistical Viewpoint
	2.2 Trends and Seasonal Variation
	2.3 Business Cycles and Economic Fluctuations
	2.4 SomeImportant Advantages of Using Spectral Methods of Analysis
	2.5 Beveridge's Annual Wheat Price Series
	References
PART A. STATIONARY TIME SERIES
	Chapter 3. Spectral Theory
		3.1 Definitions
		3.2 Power Spectra
		3.3 Black Boxes and Processes with Rational Spectral Functions
		3.4 Filters
		3.5 Estimation of the Power Spectrum
		3.6 Nyquist Frequency, Aliasing
		3.7 Transformations of Stationary Processes
		Appendix
		References
	Chapter 4. Spectral Analysis of Economic Data
		4.1 An Analogy
		4.2 Economic implications of Spectral Decomposition
		4.3 Spectral Estimation
		4.4 Examples of Estimated Spectra
		4.5 Normality of Economic Series
		4.6 Some Special Filters
		References
	Chapter 5. Cross-spectral Analysis
		5.1 Cross-spectral Theory
		5.2 Coherence, Phase, Gain, and Argand Diagrams
		5.3 Processes differing only in Phase
		5.4 Special Cases
		5.5 Estimationof(Xf)
		5.6 Case when Coherence is not a Constant
		5.7 Sums and Products of Related Series
		5.8 The Partial Cross-spectrum and other Generalizations
		References
	Chapter 6. Cross-spectral Analysis of Economic Data
		6.1 Introduction
		6.2 Relationships between Economic Series
		6.3 Estimating Cross-spectra
		6.4 An Example of Estimated Cross-spectra
		6.5 A Note on the Interpretation of Coherence
	Chapter 7. Processes Involving Feedback
		7.1 Feedback and Cross-spectral Analysis
		7.2 Some Preliminary Results
		7.3 Definitions of Causality and Feedback
		7.4 Time-lags Connected with Causality and Feedback
		7.5 Strength of Causality and Feedback
		7.6 Tests for Causality and Feedback
		7.7 Removing the Basic Assumption of Section 7.3
		7.8 Calculations Involved in Testing for Feedback
		7.9 Causality and Feedback Varying with Frequency
		7.10 Summary and Conclusions
		References
PART B. NON-STATIONARY TIME SERIES
	Chapter 8. Series With Trending Means
		8.1 Introduction
		8.2 Leakage Problems
		8.3 Regression Analysis
		8.4 Filters for Determining Trend
		8.5 Conclusion
		References
	Chapter 9. Series with Spectrum Changing with Time
		9.1 Definitions
		9.2 Particular Cases
		9.3 Effect of Non-stationarity on Estimated Spectrum and Crossspectrum
		9.4 Some Experiments Designed to Check the Results of the Previous Section
		9.5 The Use of Filters with Non-stationary Series
		References
	Chapter 10. Demodulation
		10.1 Introduction
		10.2 Demodulation
		10.3 Practical Aspects of Demodulation
		10.4 Uses and Examples of Demodulation
		References
	Chapter 11. Non-stationarity and Economic Series
		11.1 Visible trends in Time Series
		11.2 Trends in Mean
		11.3 Trends in Variance
		11.4 Spectrum Changing with Time
		11.5 Contamination: Gaps, Strikes, Crises, and Wars
		11.6 Quality of Data and Effects of Errors
		11.7 Effect of Varying Month-length
		References
	Chapter 12. Application of Cross-spectral Analysis and Complex Demodulation: Business Cycle Indicators (by M. Hatanaka)
		12.1 Business Cycle Indicators
		12.2 Lead-lag in terms of all Time Points
		12.3 Frequency Band for the Major Component of the Cyclical Component
		12.4 New Problems Brought Out by Cross-spectral Analysis
		12.5 Selection of Cycle Indicators and Reference Time Series
		12.6 Experiences with Filtering
		12.7 Strength of Cyclical Components: A Digression
		12.8 Examples of Cross-spectra and their Interpretations
		12.9 Conclusions from the Cross-spectra
		12.10 Complex Demodulation to Study the Changes in Lead-lag
		12.11 Examples of Complex Demodulation to Study the Changes in Lead-lag
		12.12 Suggestions for future study
	Chapter 13. Application of Partial Cross-spectral Analysis: Tests of Acceleration Principle for Inventory Cycle (by M. Hatanaka)
		13.1 Inventory Cycle
		13.2 A simple Version of the Acceleration Principle
		13.3 The Acceleration Principle compared with the Square Root Principle and the "Supply Conditions" Hypothesis
		13.4 Testing Procedures with the use of Cross-spectra and Partial Crossspectra
		13.5 Results of the Tests
		13.6 Different Assumptions as to Sales Expectations and their Effects upon the Results of the Tests
		13.7 Treatment of Outstanding Orders
		13.8 Conclusions
	Chapter 14. Problems Remaining
		14.1 Some Problems to be Solved
		14.2 Conclusions
Index