API

API

FamaMacBeth.adjr2 — Method.
adjr2(m::FMBModel) -> Float64

The adjusted R2.

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FamaMacBeth.fmb — Method.
fmb(X, y, g) -> FMBModel()

Short-hand for fit(FMBModel(X,y,g))

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FamaMacBeth.group — Method.
group(m::FMBResp)
group(m::FMBModel)

Returns the group labels used.

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FamaMacBeth.nobs — Method.
nobs(m::FMBModel) -> Vector{Int}

Returns the number of observations, per group as identified by g. See also group(m::FMBResp).

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FamaMacBeth.predict — Method.
predict(m::FMBModel) -> Vector
predict(m::FMBModel, X) -> Vector

Predicts the outcome using the estimated coefficients and provided independent variables. If independent variables are not provided, uses those from the model.

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FamaMacBeth.r2 — Method.
r2(m::FMBModel) -> Float64

The un-adjusted R2.

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FamaMacBeth.residuals — Method.
residuals(m::FMBModel) -> Vector
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FamaMacBeth.tstat — Method.
tstat(m::FMBModel) -> Vector
tstat(m::FMBModel) -> Vector

The t-statistic per coefficient whether the coefficient equals zero or not.

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StatsBase.coef — Method.
coef(m::FMBResp) -> Vector
coef(m::FMBModel) -> Vector

The estimated coefficients.

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StatsBase.stderror — Method.
stderror(m::FMBResp) -> Vector
stderror(m::FMBModel) -> Vector

The standard error around the estimated coefficients.

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FamaMacBeth.FMBModel — Type.
FMBModel(X,y,g,resp::FMBResp) <: StatsBase.RegressionModel
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FamaMacBeth.FMBModel — Method.
FMBModel(X,y,g) -> FMBModel()
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FamaMacBeth.fit — Method.
fit(m::FMBModel) -> FMBModel()

Runs a Fama-MacBeth (1973) panel regression by first conducting OLS of y on X per group indicated by g, and subsequently computes the mean and standard error using the resulting coefficients. An intercept is not added to X.

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FamaMacBeth.vcov — Method.
vcov(m::FMBResp) -> Matrix
vcov(m::FMBModel) -> Matrix

Variance-covariance matrix of the estimated coefficients.

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