Index: Questions and Answers

Questions and Answers

Routing Summary

Answered questions from the vault knowledge base. Each answer is cross-linked to its source notes and related concepts. Browse by topic below or search for keywords.

By Topic

Econometrics / Simulation-Based Estimation

• Q - Using SMM to Calibrate Agent Based Models — How to apply SMM to ABM calibration: moment selection, common random numbers, two-step W, standard errors, and comparison to genetic algorithm approaches

Causal Inference / Identification

• Q - Uncovering Causal Estimates from Non-Experimental Data — Nine strategies (CIA, DAGs, IV, DiD, RD, Synthetic Control, metalearners, BSTS, sensitivity analysis) with assumptions and estimands

Research Methodology / Multiple Comparisons

• Q - Handling Multiple Comparisons When Selecting From Hundreds of Models — Classical corrections vs. Bayesian alternatives (partial pooling, regularizing priors, projection predictive selection) for model search

Statistical Modeling / General

• Q - Common Pitfalls in Statistical Modeling — Eight major pitfall categories: confounding, forking paths, overfitting, missing data, golem misuse, neglecting model checks, computational issues, Type S/M errors

Bayesian vs. Frequentist Statistics

• Q - Differences Between Frequentist and Bayesian Statistics — Core philosophical divide (probability as frequency vs. belief), confidence vs. credible intervals, priors, hierarchical models, model comparison

Recent Questions

Question	Date	Key Sources
Q - Using SMM to Calibrate Agent Based Models	2026-04-11	Method of Simulated Moments, ABM Calibration Overview, Genetic Algorithm Calibration for ABM, SMM Weighting Matrix and Inference
Q - Uncovering Causal Estimates from Non-Experimental Data	2026-04-10	The Selection Problem, Instrumental Variables, Differences-in-Differences, Synthetic Control
Q - Differences Between Frequentist and Bayesian Statistics	2026-04-09	Probability and Bayesian Inference, Asymptotics and Frequentist Connections, Hierarchical Models
Q - Common Pitfalls in Statistical Modeling	2026-04-09	Spurious Association and Confounds, Garden of Forking Paths, Overfitting and Information Criteria, Model Checking
Q - Handling Multiple Comparisons When Selecting From Hundreds of Models	2026-04-09	Multiple Comparisons - Bayesian Perspective, Garden of Forking Paths, Partial Pooling as Multiple Comparisons Correction

All Questions

• Q - Using SMM to Calibrate Agent Based Models — Choose ABM parameters to minimize weighted distance between observed and simulated macro moments; enables formal standard errors and specification testing via J-test
• Q - Uncovering Causal Estimates from Non-Experimental Data — Nine identification strategies: CIA/matching, DAGs, IV, DiD, RD, synthetic control, metalearners, BSTS, sensitivity analysis
• Q - Differences Between Frequentist and Bayesian Statistics — Probability as frequency vs. belief; confidence vs. credible intervals; priors; partial pooling; WAIC vs. AIC; when each framework excels
• Q - Common Pitfalls in Statistical Modeling — Eight pitfall categories with remedies: confounding, forking paths, overfitting, missing data, golem misuse, model checking, computational issues, Type S/M errors
• Q - Handling Multiple Comparisons When Selecting From Hundreds of Models — Stop selecting by significance; use regularizing priors, projection predictive selection, or multilevel models with partial pooling