mmeierer / REndo
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R/f_latentIV.R changed.
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R/f_highermomentsIV.R changed.
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R/f_copulacorrection_interface.R changed.
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R/f_heterrorsIV.R changed.
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DESCRIPTION has changed.
NEWS.md has changed.
man/latentIV.Rd has changed.

@@ -16,6 +16,7 @@
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 16 16 #' @details 17 17 #' 18 18 #' Let's consider the model: 19 + #' 19 20 #' \ifelse{html}{\out{
Yt0+αPtt
}}{ \deqn{Y_{t} = \beta_{0} + \alpha P_{t} + \epsilon_{t}}} 20 21 #' \ifelse{html}{\out{
Pt=π'Ztt
}}{ \deqn{P_{t}=\pi^{'}Z_{t} + \nu_{t}}} 21 22 #'
@@ -33,6 +34,7 @@
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 33 34 #' \code{latentIV} considers \ifelse{html}{\out{Zt'}}{\eqn{Z_{t}^{'}}} to be a latent, discrete, exogenous variable with an unknown number of groups \eqn{m} and \eqn{\pi} is a vector of group means. 34 35 #' It is assumed that \eqn{Z} is independent of the error terms \eqn{\epsilon} and \eqn{\nu} and that it has at least two groups with different means. 35 36 #' The structural and random errors are considered normally distributed with mean zero and variance-covariance matrix \eqn{\Sigma}: 37 + #' 36 38 #' \ifelse{html}{\out{
Σ=(σε2, σ02, 37 39 #'
σ02, σν2)
}}{ 38 40 #' \deqn{\Sigma = \left(

@@ -13,6 +13,7 @@
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 13 13 #' \subsection{Method}{ 14 14 #' 15 15 #' Consider the model: 16 + #' 16 17 #' \ifelse{html}{\out{
Yt0 + β1Xt+αPtt
}}{\deqn{ Y_{t} = \beta_{0}+ \beta_{1}X_{t} + \alpha P_{t}+\epsilon_{t} \hspace{0.3cm} (1) }} 17 18 #' 18 19 #' \ifelse{html}{\out{
Pt=Ztt
}}{\deqn{ P_{t} = \gamma Z_{t}+\nu_{t} \hspace{2.5 cm} (2)}}

@@ -26,6 +26,7 @@
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 26 26 #' 27 27 #' 28 28 #' Consider the model: 29 + #' 29 30 #' \ifelse{html}{\out{
Yt01Pt2Xtt
}}{\deqn{Y_{t}=\beta_{0}+ \beta_{1} P_{t} + \beta_{2} X_{t} + \epsilon_{t}}} 30 31 #' 31 32 #' where \eqn{t=1,..,T} indexes either time or cross-sectional units, \ifelse{html}{\out{Yt}}{\eqn{Y_{t}}} is a \eqn{1x1} response variable,
@@ -37,6 +38,7 @@
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 37 38 #' 38 39 #' The marginal distribution of the endogenous regressor \ifelse{html}{\out{Pt}}{\eqn{P_{t}}} is obtained using the Epanechnikov 39 40 #' kernel density estimator (Epanechnikov, 1969), as below: 41 + #' 40 42 #' \ifelse{html}{\out{
ĥ(p)=1/(T·b) ∑(K·((p-Pt)/b))
}}{\deqn{\hat{h}(p)=\frac{1}{T\cdot b}\sum_{t=1}^{T}K\left(\frac{p-P_{t}}{b}\right)}} 41 43 #' 42 44 #' where \ifelse{html}{\out{Pt}}{\eqn{P_{t}}} is the endogenous regressor,

@@ -13,6 +13,7 @@
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 13 13 #' no external instruments are available or to supplement external instruments to improve the efficiency of the 14 14 #' IV estimator. 15 15 #' Consider the model in the equation: 16 + #' 16 17 #' \ifelse{html}{\out{
yt01Pt2Xtt
}}{\deqn{ y_{t}=\beta_{0}+ \beta_{1} P_{t} + \beta_{2} X_{t} + \epsilon_{t}}} 17 18 #' 18 19 #' where \eqn{t=1,..,T} indexes either time or cross-sectional units.The endogeneity problem arises from the correlation of
Files Coverage
R 97.43%
src/f_copulacorrection_LL_rcpp.cpp 95.65%
Project Totals (37 files) 97.40%

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