What are the different methods for calibrating economic models?

Economic models are calibrated via two methods: calibration assigns parameter values based on external sources, aligning outputs with data despite unrealistic assumptions. Estimation, however, derives parameters from data using statistical techniques, offering a more rigorous approach with formal validation. Each method has pros and cons, depending on data availability, model complexity, and researcher objectives. Comparing methods and employing validation techniques ensures model credibility.

Calibration by judgment involves relying on expert opinion and qualitative assessments to adjust parameters in economic models. This method incorporates the insights and expertise of economists or domain specialists to fine-tune model parameters, especially when data is scarce or uncertain. Experts may provide subjective estimates based on their understanding of economic relationships, historical trends, or qualitative observations. Calibration by judgment complements quantitative methods and can be particularly useful for complex or novel economic phenomena where empirical data is limited. However, it's important to acknowledge and mitigate potential biases in expert judgments to ensure robust model calibration.

Calibration by estimation involves using statistical techniques to estimate model parameters based on available data. This method typically employs econometric tools such as maximum likelihood estimation, ordinary least squares regression, or instrumental variables regression. Researchers use historical data to estimate parameters that best fit the model's equations, ensuring that the model reflects real-world economic relationships. Calibration by estimation is particularly useful when direct measurement of model parameters is challenging or when data is abundant, allowing for rigorous parameter estimation to achieve a close alignment between the model and empirical observations.

Calibration involves adjusting model parameters until the model's predictions align with historical data. It's a straightforward method that sets parameters based on observed averages or representative values from empirical studies. Estimation, on the other hand, uses statistical techniques to systematically determine the model parameters that best fit the data. Techniques such as Maximum Likelihood Estimation (MLE) or Generalized Method of Moments (GMM) are commonly employed, providing a more rigorous statistical framework for parameter determination.

Calibration by simulation involves adjusting model parameters iteratively until simulated outcomes closely match observed data. This method utilizes computational simulations of the model's behavior to explore various parameter combinations. Researchers compare simulated results with real-world data, adjusting parameters to minimize differences. Monte Carlo methods and agent-based modeling are common simulation techniques employed in economic calibration. Simulation-based calibration accommodates complex economic systems and allows for scenario analysis. It's particularly useful when direct parameter estimation is challenging due to model complexity or limited data availability.

Calibration by comparison involves comparing the output of an economic model to empirical data or the outputs of other models, aiming to identify and adjust parameters to minimize discrepancies. This method often employs statistical techniques like regression analysis or model selection criteria such as AIC or BIC to quantify the fit between model predictions and observed data. It can also involve qualitative assessments of how well the model captures key economic phenomena. Calibration by comparison provides a practical way to refine model parameters, ensuring the model's relevance and accuracy in representing real-world economic dynamics.