課程大綱

課程資訊

課程名稱

健康計量經濟學
Health Econometrics

開課學期

111-2

授課對象

公共衛生學院健康政策與管理研究所

授課教師

林青青

課號

HPM7112

課程識別碼

848 M0430

班次

學分

2.0

全/半年

半年

必/選修

選修

上課時間

星期三3,4(10:20~12:10)

上課地點

公衛210

備註

限碩士班以上
總人數上限：30人
外系人數限制：10人

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課程概述

本課程介紹健康政策分析中常用的計量方法，包括線性迴歸，一般線性模型，最大概似估計法，差異中差異法，固定效果，以及部分因果關係模型如工具變數與傾向分數。本課程討論如何選擇模型，模型設定，模型結果的推論以及呈現等等。本課程的實作案例以Stata軟體為主，但學生可自行選擇其他統計軟體完成作業。

This course is an introduction to the econometric methods in health policy analysis. We will cover commonly used models, such as linear regression, generalized linear models, maximum likelihood estimation (MLE), difference-in-difference, fixed effect, and selected causal inference models such as instrumental variable methods and propensity score. This course examines how to choose the appropriate model, check the model specification, and interpret and present the results. We will use Stata software in the course for examples and assignments. Students are welcome, however, to use software (such as SAS, R) of their own choice for their homework assignments.

課程目標

學生在修習本課程後，應該能夠『應用研究方法中的原理與技術於健康照護組織與健康體系議題』，並且具有以下能力：
• 進行健康政策分析時，能運用最小平方法，二元logit 與probit，以及其他類別變項模型
• 能夠推論更進階的模型分析結果。
• 在進行健康政策研究時，能夠運用合適的計量方法，包括描述資料，運用回歸模型，並正確而一致的描述分析結果。

At the end of the course the students are expected to apply the principles and technique of research methodology to address issues in health care organizations and systems. Students will learn the following quantitative skills:
• To estimate and interpret OLS, logit, probit, and other categorical data models for health policy analysis.
• To interpret results from advanced regression models.
• To conduct original research using appropriate analytical methods, including being able to describe a data set, application of a regression model, and the coherent, concise description of the results.

課程要求

本課程以四個項目評分：作業，期中考，期末考，以及一份10頁以內的期末實證研究報告。期中考在課堂上進行，期末考為take home, open book。教師藉由實證研究報告，可以評估學生是否已經熟悉各種基礎計量模型的運用，報告在第十五週上課前繳交。

The course grade will be a weighted average of performance in four areas: homework assignments, a midterm exam, a final exam, and an empirical paper (10-page limit). Homework assignments may include a mix of written problems and computer assignments. The midterm is in class, closed book, while the final exam is take home, open book. The empirical paper, due at the 15th week, allows the instructor to evaluate whether students master the skills learned throughout the semester.

預期每週課後學習時數

6 hours

Office Hours

另約時間備註： Wednesday 2-3pm

指定閱讀

Wooldridge, J. Introductory Econometrics: A Modern Approach. New York: South-Western College Publishing.
Cameron, A. Colin and Trivedi, Pravin K. Microeconometrics Using Stata, Revised Edition (2010).

Additional readings are listed under each week’s topic.

參考書目

Deb, P., Norton, E. C., & Manning, W. G. (2017). Health econometrics using Stata (Vol. 3). College Station, TX: Stata press

評量方式
(僅供參考)

No.	項目	百分比	說明
1.	作業Homework assignments	20%	There will be 7 assignments. Each is graded on a 0-3 points. The one with the least score will be excluded.
2.	期中考Midterm exam	20%	In class, closed book.
3.	實證研究報告Empirical paper	30%	Maximum 10 pages. Due at 15th week
4.	期末考Final exam	30%	Take home, open book.

課程進度

週次	日期	單元主題
第1週	2/22	Introduction to regression analysis
第2週	3/1	Linear regression, OLS estimator, and interpreting OLS results
第3週	3/8	Heteroskedasticity, specification tests
第4週	3/15	Introduction to non-linear models: Generalized Linear Models (GLM), Maximum Likelihood Estimation (MLE)
第5週	3/22	Binary Models and Tests
第6週	3/29	Multinomial logits and two-part models (OLS and non-linear)/ Other Categorical Models
第7週	4/5	No class
第8週	4/12	Count models
第9週	4/19	Mid-term exam (closed book, in class)
第10週	4/26	Endogeneity and instrument variables
第11週	5/3	Panel data, Difference-in-difference (DID) and Fixed effect estimators
第12週	5/10	Potential outcome framework, treatment effect, and propensity score
第13週	5/17	Utilizing administrative data (invited presentation)
第14週	5/24	Survey weights and design effects
第15週	5/31	Hold for review(Term paper due)
第16週	6/7	Final exam