Linear time series model estimation

version 2.0.5 (8.33 KB) by Apostolos Panagiotopoulos
A quick and easy way to estimate and analyze linear time series models


Updated Wed, 16 Mar 2022 15:35:13 +0000

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The function estimates:
  • Models with exogenous variables only
  • Autoregression models (AR)
  • Vector autoregression models (VAR)
  • Error correction models (ECM)
  • Vector error correction models (VECM)
The avalable estimation methods are the Ordinary Least Squares and the Maximum Likelihood Estimation
The output of the function includes:
  • Coefficients: estimation, standard errors, significance, lower and upper levels
  • Coefficients variance-covariance matrix
  • Residuals
  • Residuals variance-covariance matrix
  • Information criteria (AIC, AICc, BIC)
  • log-likelihood
  • R squared
  • Residuals tests: 1) One-sample Kolmogorov-Smirnov test, 2) Augmented Dickey-Fuller test, 3) Kwiatkowski, Phillips, Schmidt, and Shin test, 4) Durbin-Watson test, 5) Ljung-Box Q-test, 6) Engle’s ARCH test, and 7) Cross-sectional correlation (VAR and VECM only).

Cite As

Apostolos Panagiotopoulos (2022). Linear time series model estimation (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2021a
Compatible with any release
Platform Compatibility
Windows macOS Linux
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