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Topics

TopicDiebold (2024)Huang & Petukhina (2022)
What is Time Series?11.1
Basic Time Series Properties
  Stationarity6.11.3.2
  White Noise (and Random Walks)6.21.3.4, 2.2
  Autocorrelation6.31.3.3, 2.1
  Trend and Seasonality52.4, 9.1
Time Series Tools
  Differencing and Backshift Operator6.5.13, 3.1.2
  Smoothing Techniques2.4
  Unit Root Tests9.3
  Information Criteria15.14.3
Univariate Models
  Autoregressive (AR) Models6.5, 6.73.3
  Moving Average (MA) Models7.1, 7.23.2
  ARMA/ARIMA Models7.2.43.4, 4
  SARIMA Models5
  ARCH Models86
Multivariate Models
  Vector Autoregression (VAR) Model167
  Vector Error Correction Model (VECM)9.4.2, 9.4.4
  State Space Models8.1
  Kalman Filter8.2
References
  1. Diebold, F. X. (2024). Forecasting in Economics, Business, Finance and Beyond. Department of Economics, University of Pennsylvania. https://www.sas.upenn.edu/~fdiebold/Textbooks.html
  2. Huang, C., & Petukhina, A. (2022). Applied time series analysis and forecasting with Python. Springer. 10.1007/978-3-031-13584-2