econometrics-phd-level

econometrics-phd-level is a skill for Claude Code, Codex from jayjeo/econometrics-phd-level-skill. It costs 201 tokens per session (8,498 once invoked), scanned A, original, MIT.

A guide to econometrics, the use of statistics to study relationships in data, based on a 12-part Korean lecture series. It routes questions to explanations of topics such as regression, panel data, instrumental variables, and causal comparisons.

In plain words
What is it for?
Understanding or explaining regression results, standard errors, confidence intervals, fixed effects, difference-in-differences, regression discontinuity, instrumental variables, and related Stata analyses.
Why use it?
Econometrics uses assumptions and technical terms that are easy to apply incorrectly. This guide helps explain the methods progressively and connects questions to the relevant lesson material.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Understanding or explaining regression results, standard errors, confidence intervals, fixed effects, difference-in-differences, regression discontinuity, instrumental variables, and related Stata analyses.

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Install with agentmods
npx agentmods add skills/jayjeo/econometrics-phd-level-skill/econometrics-phd-level
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add jayjeo/econometrics-phd-level-skill --skill econometrics-phd-level
Clone the repo
git clone --depth 1 https://github.com/jayjeo/econometrics-phd-level-skill

Made for: Claude Code, Codex.

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README.md
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Per session 201 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 8,498 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00201 $0.08498
Opus 5 $0.00101 $0.04249
Sonnet 5 $0.00040 $0.01700
Haiku 4.5 $0.00020 $0.00850

Measured 10d ago against content hash 7648addc4cc8, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

econometrics-phd-level scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/econometrics-phd-level/SKILL.md · 233 lines

How it starts

The opening of the file, as written. The whole thing — 233 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Easy Econometrics (Korean Lecture Series) — Router

이 skill은 한국어 12편 강의 시리즈 '수학없는 쉬운 계량경제학'(2026년 3월 11일 ~ 4월 29일)의 분석·요약·재설명을 돕는 Progressive Disclosure 라우터다. 본문은 의사결정 트리이며, 실제 정의·식·STATA 코드·강의자 비유의 본문은 모두 references/*.md 에 위임한다. Claude는 사용자의 질문 키워드에서 가장 가까운 references 파일 1~3개를 골라 그 파일만 읽고 답해야 한다.

When to Use

다음 중 하나라도 해당하면 이 skill 을 호출하라.

  1. 사용자가 이 시리즈의 특정 회차(260311, 260313, 260316, 260318, 260323, 260401, 260408, 260410, 260414, 260415, 260420, 260429) 또는 그 강의에서 다룬 주제를 언급할 때.
  2. 시리즈에 등장한 표준 데이터셋(auto.dta, card.dta, R_data9_1.dta, R2.dta, grunfeld.dta, mus207mepspresdrugs, RD.dta, panelf3.dta, nlswork.dta)을 언급할 때.
  3. 다음 토픽 키워드가 등장할 때: 회귀분석, 단순/다중회귀, OLS, argmin Σε², 행렬 닫힌형, 상수항/noconstant, 외생성/직교, 표준오차/t값/p값/신뢰구간, R², 다중공선성/풀랭크, 더미변수/트랩, 상호작용/교차항, 패널/고정효과/xtreg/vce(robust), 도구변수/IV/2SLS/GMM, DiD/이중차분/사전평행추세/이벤트 스터디, RD/회귀단절/cutoff/placebo, 다항식/로그/DGP/탄성, MLE/최우추정/로짓/프로빗, LPM.
  4. 사용자가 강의자 비유(맛 비유, 단물, 강북-강남, 마티즈/에쿠스, '아쉽게/재수 좋게', 찌그러진 동전, 3인방)나 호명한 학자(David Card, David Lee, Card-Krueger, Cameron & Trivedi, KCTDI 매뉴얼)를 언급할 때.
  5. 사용자가 시리즈 전체의 페다고지 패턴, 누적 흐름, 강의자 화법, 흔한 오해 카탈로그를 묻거나, 한 회차의 narrative 요약을 요청할 때.

이 skill 은 한국어 강의 도메인에 특화된 라우터다. 일반적인 영어 계량 질문(예: 'how to run feols in R')은 이 skill 보다 r-econometrics 또는 regression-modeling skill 이 더 적합하다.

Topic Index

references/ 디렉터리의 15개 파일. 각 파일은 1500~2500단어이며 첫 문단이 한 문장 정의로 시작한다.

  • references/regression-basics-and-ovb.md — 회귀분석의 좌표 직관, 산점도, 단순회귀와 다중회귀, mpg-price 부호 반전을 통한 누락변수 편향(OVB) 메커니즘, auto.dta 표준 시연. 시리즈 진입점.
  • references/correlation-vs-causation.md — 시리즈 전체의 메타 메시지 '상관 ≠ 인과'. KCTDI 매뉴얼의 내생성 4분류(OVB·역인과·측정오류·선택편향), David Card 호명, 인과 ⊂ 상관 다이어그램.
  • references/ols-mechanics-and-matrix-form.md — β̂=argmin Σε² 정의식과 행렬 닫힌형 β̂=(X'X)⁻¹X'y, n=3 가상 DGP 손계산, STATA·Python·Microsoft Mathematics 멀티-도구 일치 검증, 표본 직교 X'ê=0으로부터의 자기완결적 도출.
  • references/intercept-and-noconstant.md — 상수항 β₀를 '항상 1인 보이지 않는 설명변수'로 재정의, _cons의 기하학, STATA noconstant 옵션이 mpg 계수 부호를 -238.89→+253.63으로 반전시키는 시연.
  • references/exogeneity-assumption-hierarchy.md — 외생성/직교성 가정의 3단계 위계(독립 ⊃ 평균조건부 독립 ⊃ 직교), 단물 비유, 패널의 Strict Exogeneity 강화, 강의자 자기교정('독립 → 직교') 패턴, 가정은 약할수록 좋다는 메타 명제.
  • references/inference-se-t-p-ci.md — 회귀계수의 표본분포 직관, SE·t값·신뢰구간(0 포함 여부), p값='임계 유의수준' 통찰, set level 시연, '유의(有意)' 어원, R²·F·N의 직관적 해석.
  • references/multicollinearity-and-rank.md — card.dta의 age=6+exper+educ 항등식으로 완전 다중공선성, STATA omit 메시지, near multicollinearity의 SE 폭증, '단맛을 두고 두 X가 싸운다'는 맛 비유, 3D 회귀평면 인터랙티브 시각화.
  • references/dummy-variables-and-trap.md — 0/1 더미 인코딩, k-1 원칙, 더미변수 트랩, R_data9_1.dta 인종 더미의 평행선 회귀(같은 기울기·다른 절편), OVB 시연(D2 계수 -0.17→-0.09).
  • references/interaction-terms.md — 더미×연속변수 곱항으로 두 집단의 절편차+기울기차를 분리, 더미값 0/1 대입 산술, 인종 부채꼴 그래프, STATA 4가지 동치 표기(수동 곱 / i.race#c.ttl_exp / i.race##c.ttl_exp / xi:), 기준범주 변경의 거울 대칭.
  • references/panel-data-and-fixed-effects.md — i×t 패널 구조, grunfeld.dta xtset, LSDV(reg i.company)와 within(xtreg fe)의 슬로프 동치성, sigma_u/sigma_e/rho 분산 분해, 샌드위치 분산행렬과 vce(robust), 양방향 고정효과(TWFE).
  • references/iv-2sls-gmm.md — 도구변수 Z의 두 조건(연관성·외생성), 2SLS의 '교육 핵' 추출, IV DAG, mus207mepspresdrugs로 OLS→단일 IV→over-identified 2SLS→GMM 4단계 위계, 약한 도구 진단, Cameron & Trivedi 정당화.
  • references/did-difference-in-differences.md — Card-Krueger 최저임금 자연실험, 4셀 i×t 매트릭스, 잠재결과 프레임워크, 사전평행추세 가정, '3인방'(α_i + λ_t + β·교차항) 회귀식 산술 도출, 이벤트 스터디, 다항 t로의 확장, 연속 처치변수 DiD.
  • references/rd-regression-discontinuity.md — cutoff 근처 국소 무작위화 가정, 장학금-수능 가상 사례, David Lee(2008) RD.dta 미국 하원 선거 replicate, t-1 placebo test, 4차 다항 RD, 비모수 RD(WLS+대역폭 h의 편의-분산 상충관계).
  • references/functional-forms-polynomial-log.md — 다항식 차수='굽은 횟수+1', log/log-log 모형의 탄성·반탄성 해석, cubic/quartic DGP 시뮬레이션 진짜 vs OLS 추정 비교, x³ 누락에 의한 OVB 시연, '쓸데없는 파라미터'와 overfitting.
  • references/mle-logit-probit-gmm.md — OLS 너머의 추정법. 찌그러진 동전 비유로 MLE 역발상, 베르누이 PMF 압축표기, 로그우도함수 도출, F 함수형 선택(로지스틱 CDF=로짓, 정규 CDF=프로빗), LPM vs Logit S자 곡선, STATA mylogit ml 사용자 정의, GMM이 OLS 적률조건의 일반화임.

Read the full file on GitHub · 233 lines

Files

What ships with it

32 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 10d ago First seen · 233 lines · 201 tokens per session scan A 7648addc4cc8

Subscribe to this mod's changes

econometrics-phd-level is a skill published in the GitHub repository jayjeo/econometrics-phd-level-skill (2 stars, last pushed 4mo ago), licensed MIT. It adds 201 tokens to every session and 8,498 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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