Practical Model Specification Issues

Описание: Learn how to bridge the gap between theoretical economic models and empirical data in this comprehensive guide to identification and specification. This video walks through the practical challenges of taking a structural model to real-world data using the National Longitudinal Survey of Youth 1997 cohort.

Slide used in the video available here: https://raw.githack.com/tyleransom/st...
Source code for slides is available here: https://github.com/tyleransom/structu...

Key Topics Covered:
Identification challenges when moving from theory to data
Data availability and measurement reliability questions
Specification decisions: functional forms and distributional assumptions
Working with discrete vs. continuous variables
Calibration strategies for unidentifiable parameters
Practical example using NLSY97 data in Julia

What You'll Learn:
How to assess whether your data contains the variables your model requires
When to modify your theoretical model based on data constraints
How to make defensible functional form assumptions
The iterative process between theory and empirical implementation
Setting up statistical objective functions for estimation

Example Application:
This video demonstrates the process using a model of schooling and labor supply decisions, showing how to handle real-world constraints like binary education measures, potential experience calculations, and distributional assumptions for error terms.

Perfect for graduate students in economics, researchers working on structural models, and anyone interested in applied econometrics and empirical modeling strategies.

Tyler Ransom is an Associate Professor of Economics at the University of Oklahoma. Subscribe for more videos on data science, econometrics, and research methods!

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