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Models Module ​

Overview ​

This module contains all the core econometric and macroeconomic models implemented in the library, covering classical, Bayesian, and modern deep learning approaches.

List of Models ​

  • DSGE: Flexible, symbolic, and numerical solution methods for rational expectations models.
  • VAR: Multivariate time series modeling with automatic lag selection and diagnostic tools.link
  • SVAR: Structural VAR models for identifying economic shocks.link
  • VARIMA: Robust VARIMA implementation in Python, addressing a gap in the time series ecosystem.link
  • VARMA / SVARMA: Multivariate ARMA and structural extensions for time series analysis.link
  • NBEATS: Deep learning-based model for modern time series forecasting.
  • Custom State Space Models: User-defined state space modeling framework for specialized applications.link

References ​

  • Nimark, K. (2016). Solving Rational Expectations Models. Link
  • Sims, C. A. (1980). Macroeconomics and Reality.
  • Hamilton, J. D. (1994). Time Series Analysis.
  • Muth, J. F. (1961). Rational Expectations and the Theory of Price Movements.
  • Tsay, R. S. (Time Series Analysis and Applications).
  • Lucas, R. E. (Rational Expectations and Macroeconomic Policy).
  • Oreshkin, B. et al. (N-BEATS: Neural Basis Expansion Analysis for Time Series Forecasting).