AR.power | R Documentation |
AR.power
computes the power of Anderson-Rubin (1949) test based on the given values of parameters.
AR.power(n, k, l, beta, gamma, Zadj_sq,
sigmau, sigmav, rho, alpha = 0.05)
n |
Sample size. |
k |
Number of exogenous variables. |
l |
Number of instrumental variables. |
beta |
True causal effect minus null hypothesis causal effect. |
gamma |
Regression coefficient for effect of instruments on treatment. |
Zadj_sq |
Variance of instruments after regressed on the observed variables. |
sigmau |
Standard deviation of potential outcome under control. (structural error for y) |
sigmav |
Standard deviation of error from regressing treatment on instruments. |
rho |
Correlation between u (potential outcome under control) and v (error from regressing treatment on instrument). |
alpha |
Significance level. |
Power of the Anderson-Rubin test based on the given values of parameters.
Yang Jiang, Hyunseung Kang, and Dylan Small
Anderson, T.W. and Rubin, H. (1949). Estimation of the parameters of a single equation in a complete system of stochastic equations. Annals of Mathematical Statistics, 20, 46-63.
See also ivmodel
for details on the instrumental variables model.
# Assume we calculate the power of AR test in a study with one IV (l=1)
# and the only one exogenous variable is the intercept (k=1).
# Suppose the difference between the null hypothesis and true causal
# effect is 1 (beta=1).
# The sample size is 250 (n=250), the IV variance is .25 (Zadj_sq =.25).
# The standard deviation of potential outcome is 1(sigmau= 1).
# The coefficient of regressing IV upon exposure is .5 (gamma= .5).
# The correlation between u and v is assumed to be .5 (rho=.5).
# The standard deviation of first stage error is .4 (sigmav=.4).
# The significance level for the study is alpha = .05.
# power of Anderson-Rubin test:
AR.power(n=250, k=1, l=1, beta=1, gamma=.5, Zadj_sq=.25,
sigmau=1, sigmav=.4, rho=.5, alpha = 0.05)
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