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NBER WORKING PAPER SERIESUSING MATCHING, INSTRUMENTAL VARIABLES AND CONTROL FUNCTIONS TO ESTIMATE ECONOMIC CHOICE MODELS James Heckman Salvador NavarroLozano Working Paper 9497 http://www.nber.org/papers/w9497

How to fill out using matching instrumental variables

How to fill out using matching instrumental variables

1. To fill out using matching instrumental variables, follow the steps below:
2. Identify the variables that act as instrumental variables in your study. These variables should satisfy the conditions of relevance and exogeneity.
3. Determine the treatment variable and outcome variable that you want to analyze.
4. Estimate the propensity scores for each observation using a suitable method, such as logistic regression.
5. Implement the matching process using one of the common matching techniques, such as nearest neighbor matching or propensity score matching.
6. Assess the balance of covariates between the treated and control groups after matching to ensure that the matching process was successful.
7. Estimate the treatment effect using appropriate statistical techniques, such as difference-in-differences or instrumental variable regression.
8. Perform sensitivity analyses and robustness checks to evaluate the validity of the results.
9. Interpret and report the findings, taking into account any limitations or assumptions made during the analysis.

Who needs using matching instrumental variables?

1. Using matching instrumental variables is especially relevant for researchers and analysts in the field of causal inference and program evaluation.
2. It is used when studying the causal impact of an intervention or treatment on an outcome variable, while accounting for potential confounding variables.
3. Researchers who want to estimate the causal effect of a program or policy in situations where randomized controlled trials are not feasible or ethical may benefit from using matching instrumental variables.
4. It is also applicable in observational studies where the treatment assignment is not random, but instrumental variables can be used to approximate randomization.
5. Economists, social scientists, and analysts in various fields may find using matching instrumental variables helpful in addressing causal questions and understanding the impact of interventions.

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What is using matching instrumental variables?

Using matching instrumental variables refers to a statistical method employed to estimate causal relationships when an experiment is not feasible, often in observational studies. It involves using instruments that are correlated with the treatment but not directly with the outcome to control for unobserved confounders.

Who is required to file using matching instrumental variables?

Researchers conducting studies that use instrumental variable analysis may be required to report their findings, often in specific formats mandated by journals or academic institutions.

How to fill out using matching instrumental variables?

Filling out a report or submission using matching instrumental variables typically involves providing a detailed methodology section that explains the choice of instruments, data sources, and the statistical techniques used. It's important to include validation checks and sensitivity analysis results.

What is the purpose of using matching instrumental variables?

The purpose of using matching instrumental variables is to reduce bias in the estimation of causal effects by appropriate adjustment for unobserved confounders, thereby providing a clearer view of the relationship between treatment and outcome.

What information must be reported on using matching instrumental variables?

Information that must be reported includes the identification of instrumental variables, the assumptions underlying their use, data description, estimates of the causal effect, and results of robustness checks and sensitivity analyses.