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1. econweb.ucsd.edu › ~jhamiltonJames D. Hamilton Home Page - University of California, San Diego

   econweb.ucsd.edu › ~jhamilton

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   We illustrate how our structural model can be used to analyze how individual producers and consumers would dynamically adapt to a geopolitical event such as a major disruption in the supply of oil from Russia. Links to replication data and code, presentation slides, and video.

2. econweb.ucsd.edu › ~jhamilto › palgrav1Regime-Switching Models - University of California, San Diego

   econweb.ucsd.edu › ~jhamilto › palgrav1

   Regime-Switching Models. May 18, 2005. James D. Hamilton. Department of Economics, 0508. University of California, San Diego. La Jolla, CA 92093-0508. jhamilton@ucsd.edu. Prepared for: Palgrave Dictionary of Economics Many economic time series occasionally exhibit dramatic breaks in their behavior, asso-

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4. econweb.ucsd.edu › ~jhamilto › softwareData and Software from James D. Hamilton

   econweb.ucsd.edu › ~jhamilto › software

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   Data and MATLAB code to implement the examples in Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information, Econometrica, Sept 2015, by Christiane Baumeister and James Hamilton.

5. ocw.mit.edu › courses › 14-384-time-series-analysisReadings | Time Series Analysis | Economics | MIT OpenCourseWare

   ocw.mit.edu › courses › 14-384-time-series-analysis

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   • Introduction: Stationary Time Series. 1–3. Introduction to stationary time series. [Hamilton] Chapters 1–5, 7, and 8. *[Hall and Heyde] Chapter 3. [Brockwell and Davis] Chapters 1, 3, and Section 5.7.
   • Mutivariate Stationary Analysis. 6–7. VAR. *[Hamilton] Chapters 10, and 11. *[Lütkepohl] Chapters 2, and 3 (2005). Watson, M. “Vector Autoregressions and Cointegration.”
   • Univariate Non-Stationary Processes 12 Asymptotic theory of empirical processes. *[Hamilton] Sections 17.1–17.3. [Hall and Heyde] Chapters 3, 4, and 5, and the Appendix.
   • Multivariate Non-Stationary. 16–17. Multivariate unit roots and co-integration. Stock, J. H. “Asymptotic Properties of Least Squares Estimators of Cointegrating Vectors.”

6. web.pdx.edu › ~crkl › readingsSTATE-SPACE MODELS* - Portland State University

   web.pdx.edu › ~crkl › readings

   JAMES D. HAMILTON. University of California, San Diego. Contents Abstract 1. The state-space representation of a linear dynamic system 2. The Kalman filter. 2.1. Overview of the Kalman filter 2.2. Derivation of the Kalman filter 2.3. Forecasting with the Kalman filter 2.4.

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7. www.sciencedirect.com › science › articleChapter 50 State-space models - ScienceDirect

   www.sciencedirect.com › science › article

   Jan 1, 1994 · Chapter 50 STATE-SPACE MODELS* JAMES D. HAMILTON University of California, San Diego Contents Abstract 3041 1. The state-space representation of a linear dynamic system 3041 2. The Kalman filter 3046 2.1.

8. www.semanticscholar.org › paper › State-space-modelsState-space models | Semantic Scholar

   www.semanticscholar.org › paper › State-space-models

   Hamilton developed a technique for estimating financial market expectations of inflation based on the observed time-series properties of interest rates and inflation. The technique is based on a … Expand