Summary of Joshua Angrist & Jörn-Steffen Pischke's Mastering 'Metrics

Everest Media

Description

Please note: This is a companion version & not the original book.
Sample Book Insights:
#1 Without universal health insurance, Americans are less healthy than Canadians, who spend just two-thirds as much on care.
#2 Americans with health insurance are healthier than those without. Table 1. 1 Health of Uninsured and Insured Americans Note: NHIS 2009 data. Health index is based on the 5-to-1 scale: excellent health is given a score of 5, while poor health is given a score of 1. But the story isn’t so simple. First, a word about the statistical technique that produced this table: logistic regression. logistic regression is one method of statistical analysis that attempts to explain differences in an outcome variable between two groups by considering the variables in an inclusion/exclusion list: variables that predict whether a person has health insurance, or whether they are in the treatment group, and variables that predict the health index itself. The logistic regression model includes variables from the list in addition to other independent variables, and it models the dependent variable as a linear function of all these independent variables and their interactions. 4 The model is based on statistical theory and can be estimated using software like Stata, SPSS, or R. -> The National Health Interview Survey asks Americans if their health is excellent, very good, good, fair, or poor. Those with health insurance are healthier than those without, a difference of about.
#3 If you want to understand the link between health insurance and health, you must compare people with and without insurance in a way that takes other factors into account.
#4 To understand the link between health insurance and health, you must compare people with and without insurance in a way that takes other factors into account.

Sujets

• Business
• Economics
• Econometrics

Informations

• Publié par: Everest Media LLC
• Date de parution: 18 septembre 2022
• Nombre de lectures: 0
• EAN13: 9798350029291
• Langue: English
• Poids de l'ouvrage: 1 Mo

Extrait

Insights on Joshua Angrist & Jörn-Steffen Pischke's Mastering Metrics
Contents Insights from Chapter 1 Insights from Chapter 2 Insights from Chapter 3 Insights from Chapter 4 Insights from Chapter 5 Insights from Chapter 6
Insights from Chapter 1

#1

Without universal health insurance, Americans are less healthy than Canadians, who spend just two-thirds as much on care.

#2

Americans with health insurance are healthier than those without. Table 1. 1 Health of Uninsured and Insured AmericansNote: NHIS 2009 data. Health index is based on the 5-to-1 scale: excellent health is given a score of 5, while poor health is given a score of 1. But the story isn’t so simple. First, a word about the statistical technique that produced this table: logistic regression. logistic regression is one method of statistical analysis that attempts to explain differences in an outcome variable between two groups by considering the variables in an inclusion/exclusion list: variables that predict whether a person has health insurance, or whether they are in the treatment group, and variables that predict the health index itself. The logistic regression model includes variables from the list in addition to other independent variables, and it models the dependent variable as a linear function of all these independent variables and their interactions. 4 The model is based on statistical theory and can be estimated using software like Stata, SPSS, or R. -> The National Health Interview Survey asks Americans if their health is excellent, very good, good, fair, or poor. Those with health insurance are healthier than those without, a difference of about.

#3

If you want to understand the link between health insurance and health, you must compare people with and without insurance in a way that takes other factors into account.

#4

To understand the link between health insurance and health, you must compare people with and without insurance in a way that takes other factors into account.

#5

The causal effect of insurance on health is 1, but you can’t directly observe it since it is a hidden variable. To understand the link between health insurance and health, you must compare people with and without insurance in a way that takes other factors into account.

#6

The average causal effect of insurance on health is 1, but you can’t observe it since it is a hidden variable. To understand the link between health insurance and health, you must compare people with and without insurance in a way that takes other factors into account.

#7

The causal effect of insurance on health is 1, but you can’t observe it since it is a hidden variable. To understand the link between health insurance and health, you must compare people with and without insurance in a way that takes other factors into account.

#8

The Law of Large Numbers states that the average value of a sample, when large enough, approaches the average value of its parent population.

#9

The Law of Large Numbers states that the average value of a sample, when large enough, approaches the average value of its parent population. In randomized trials, experimental samples are created by sampling from a population we’d like to study rather than by repeating a game.

#10

The Law of Large Numbers states that the average value of a sample, when large enough, approaches the average value of its parent population. In randomized trials, experimental samples are created by sampling from a population we’d like to study rather than by repeating a game.

#11

The Law of Large Numbers states that the average value of a sample, when large enough, approaches the average value of its parent population. In randomized trials, experimental samples are created by sampling from a population we’d like to study rather than by repeating a game.

#12

The Law of Large Numbers states that the average value of a sample, when large enough, approaches the average value of its parent population. In randomized trials, experimental samples are created by sampling from a population we’d like to study rather than by repeating a game.

#13

The Law of Large Numbers states that the average value of a sample, when large enough, approaches the average value of its parent population. In randomized trials, experimental samples are created by sampling from a population we’d like to study rather than by repeating a game.

#14

The Law of Large Numbers states that the average value of a sample, when large enough, approaches the average value of its parent population. In randomized trials, experimental samples are created by sampling from a population we’d like to study rather than by repeating a game.

#15

The Law of Large Numbers states that the average value of a sample, when large enough, approaches the average value of its parent population. In randomized trials, experimental samples are created by sampling from a population we’d like to study rather than by repeating a game. In the HIE, subjects assigned to more generous insurance plans used substantially more health care.

#16

The HIE’s subjects were middle-class families, not poor Americans. Medicaid expansion would have a different cost-benefit profile if it were offered to the currently uninsured.

#17

The Law of Large Numbers states that the average value of a sample, when large enough, approaches the average value of its parent population. In randomized trials, experimental samples are created by sampling from a population we’d like to study rather than by repeating a game.

#18

The Law of Large Numbers states that the average value of a sample, when large enough, approaches the average value of its parent population. In randomized trials, experimental samples are created by sampling from a population we’d like to study rather than by repeating a game.