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econometron.utils ​

The utils module provides essential tools for data preparation, estimation, optimization, and state-space modeling. These utilities are designed to streamline econometric workflows, enabling researchers and developers to build robust pipelines for data analysis and model estimation.

Key Features ​

Data Preparation ​

  • Cleaning and Transformation: Functions to preprocess time series data, including handling missing values, scaling, and normalization.
  • Visualization: Tools for plotting and analyzing time series trends.

Estimation ​

  • Maximum Likelihood Estimation (MLE): Efficient parameter estimation for econometric models.
  • Bayesian Estimation: Incorporates prior distributions for robust inference.
  • Ordinary Least Squares (OLS): A foundational method for linear regression analysis.

Optimization ​

  • Simulated Annealing: Global optimization technique for complex econometric functions ([Goffe et al., 1994]).
  • Genetic Algorithm: Evolutionary optimization for parameter tuning.

Bayesian Sampling ​

  • Random Walk Metropolis-Hastings (RWMH): Bayesian sampling method for posterior distributions.

Submodules ​

  • Data Preparation: Tools for cleaning, transforming, and visualizing time series data.
  • Estimation: Methods for MLE, Bayesian, and OLS estimation.
  • Optimizers: Advanced optimization techniques for econometric models.
  • Sampler: Random Walk Metropolis-Hastings - Bayesian sampling method for posterior distributions.

Integration Notes ​

  • All utilities are modular and can be integrated into custom econometric pipelines.
  • Designed for compatibility with Econometron's models and filters.

References ​

  • Hamilton, J.D. (1994). Time Series Analysis. Princeton University Press.
  • Box, G.E.P., & Jenkins, G.M. (1976). Time Series Analysis: Forecasting and Control. Holden-Day.
  • Dickey, D.A., & Fuller, W.A. (1979). "Distribution of the Estimators for Autoregressive Time Series with a Unit Root." Journal of the American Statistical Association, 74(366), 427-431.
  • Fernández-Villaverde, J., & Rubio-Ramírez, J. F. (2007). Estimating Macroeconomic Models: A Likelihood Approach. Review of Economic Studies, 74(4), 1059-1087.
  • Goffe, W. L., Ferrier, G. D., & Rogers, J. (1994). Global Optimization of Statistical Functions with Simulated Annealing. Journal of Econometrics, 60(1-2), 65-99.
  • Dorsey, R. E., & Mayer, W. J. (1995). Genetic Algorithms for Estimation Problems with Multiple Optima, Nondifferentiability, and Other Irregular Features. Journal of Business & Economic Statistics, 13(1), 53-66.
  • Durbin, J., & Koopman, S. J. (2012). Time Series Analysis by State Space Methods. Oxford University Press.
  • Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680.
  • Corana, A., Marchesi, M., Martini, C., & Ridella, S. (1987). Minimizing Multimodal Functions of Continuous Variables with the "Simulated Annealing" Algorithm. ACM Trans. Math. Softw., 13(3), 262-280.