Commit ⋅ mmeierer/REndo

#' no external instruments are available or to supplement external instruments to improve the efficiency of the
#' IV estimator.
#' Consider the model in the equation:
#'
#' \ifelse{html}{\out{<br><div style="text-align:center">y<sub>t</sub>=&beta;<sub>0</sub>+&beta;<sub>1</sub>P<sub>t</sub>+&beta;<sub>2</sub>X<sub>t</sub>+&epsilon;<sub>t</sub></div>}}{\deqn{ y_{t}=\beta_{0}+ \beta_{1} P_{t} + \beta_{2} X_{t} + \epsilon_{t}}}
#'
#' where \eqn{t=1,..,T} indexes either time or cross-sectional units.The endogeneity problem arises from the correlation of

@@ -13,6 +13,7 @@

#' \subsection{Method}{
#'
#' Consider the model:
#'
#' \ifelse{html}{\out{<br><div style="text-align:center">Y<sub>t</sub>=&beta;<sub>0</sub> + &beta;<sub>1</sub>X<sub>t</sub>+&alpha;P<sub>t</sub>+&epsilon;<sub>t</sub></div>}}{\deqn{ Y_{t} = \beta_{0}+ \beta_{1}X_{t} + \alpha P_{t}+\epsilon_{t} \hspace{0.3cm} (1) }}
#'
#' \ifelse{html}{\out{<div style="text-align:center">P<sub>t</sub>=Z<sub>t</sub>+&nu;<sub>t</sub></div>}}{\deqn{ P_{t} = \gamma Z_{t}+\nu_{t} \hspace{2.5 cm} (2)}}

@@ -26,6 +26,7 @@

#'
#'
#' Consider the model:
#'
#' \ifelse{html}{\out{<br><div style="text-align:center">Y<sub>t</sub>=&beta;<sub>0</sub>+&beta;<sub>1</sub>P<sub>t</sub>+&beta;<sub>2</sub>X<sub>t</sub>+&epsilon;<sub>t</sub></div>}}{\deqn{Y_{t}=\beta_{0}+ \beta_{1} P_{t} + \beta_{2} X_{t} + \epsilon_{t}}}
#'
#' where \eqn{t=1,..,T} indexes either time or cross-sectional units, \ifelse{html}{\out{Y<sub>t</sub>}}{\eqn{Y_{t}}} is a \eqn{1x1} response variable,

@@ -37,6 +38,7 @@

#'
#' The marginal distribution of the endogenous regressor \ifelse{html}{\out{P<sub>t</sub>}}{\eqn{P_{t}}} is obtained using the Epanechnikov
#' kernel density estimator (Epanechnikov, 1969), as below:
#'
#' \ifelse{html}{\out{<br><div style="text-align:center">h&#770;(p)=1/(T&#183;b) &sum;(K&#183;((p-P<sub>t</sub>)/b))</div>}}{\deqn{\hat{h}(p)=\frac{1}{T\cdot b}\sum_{t=1}^{T}K\left(\frac{p-P_{t}}{b}\right)}}
#'
#' where \ifelse{html}{\out{P<sub>t</sub>}}{\eqn{P_{t}}} is the endogenous regressor,

@@ -16,6 +16,7 @@

#' @details
#'
#' Let's consider the model:
#'
#' \ifelse{html}{\out{<br><div style="text-align:center">Y<sub>t</sub>=&beta;<sub>0</sub>+&alpha;P<sub>t</sub>+&epsilon;<sub>t</sub></div>}}{ \deqn{Y_{t} = \beta_{0} + \alpha P_{t} + \epsilon_{t}}}
#' \ifelse{html}{\out{<div style="text-align:center">P<sub>t</sub>=&pi;'Z<sub>t</sub>+&nu;<sub>t</sub></div>}}{ \deqn{P_{t}=\pi^{'}Z_{t} + \nu_{t}}}
#'

@@ -33,6 +34,7 @@

#' \code{latentIV} considers \ifelse{html}{\out{Z<sub>t</sub>'}}{\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.
#' 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.
#' The structural and random errors are considered normally distributed with mean zero and variance-covariance matrix \eqn{\Sigma}:
#'
#' \ifelse{html}{\out{<div style="text-align:center">&Sigma;=(&sigma;<sub>&epsilon;</sub><sup>2</sup>, &sigma;<sub>0</sub><sup>2</sup>,
#' <br>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&sigma;<sub>0</sub><sup>2</sup>, &sigma;<sub>&nu;</sub><sup>2</sup>)</div>}}{
#' \deqn{\Sigma = \left(

Files		•	•	Coverage
R	1,634	1,588	46	97.18%
src/f_copulacorrection_LL_rcpp.cpp	23	22	1	95.65%
Project Totals (37 files)	1,657	1,610	47	97.16%

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26	26		#'
27	27		#'
28	28		#' Consider the model:
	29	+	#'
29	30		#' \ifelse{html}{\out{<br><div style="text-align:center">Y<sub>t</sub>=β<sub>0</sub>+β<sub>1</sub>P<sub>t</sub>+β<sub>2</sub>X<sub>t</sub>+ε<sub>t</sub></div>}}{\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{Y<sub>t</sub>}}{\eqn{Y_{t}}} is a \eqn{1x1} response variable,

37	38		#'
38	39		#' The marginal distribution of the endogenous regressor \ifelse{html}{\out{P<sub>t</sub>}}{\eqn{P_{t}}} is obtained using the Epanechnikov
39	40		#' kernel density estimator (Epanechnikov, 1969), as below:
	41	+	#'
40	42		#' \ifelse{html}{\out{<br><div style="text-align:center">ĥ(p)=1/(T·b) ∑(K·((p-P<sub>t</sub>)/b))</div>}}{\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{P<sub>t</sub>}}{\eqn{P_{t}}} is the endogenous regressor,

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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{<br><div style="text-align:center">y<sub>t</sub>=β<sub>0</sub>+β<sub>1</sub>P<sub>t</sub>+β<sub>2</sub>X<sub>t</sub>+ε<sub>t</sub></div>}}{\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

13	13		#' \subsection{Method}{
14	14		#'
15	15		#' Consider the model:
	16	+	#'
16	17		#' \ifelse{html}{\out{<br><div style="text-align:center">Y<sub>t</sub>=β<sub>0</sub> + β<sub>1</sub>X<sub>t</sub>+αP<sub>t</sub>+ε<sub>t</sub></div>}}{\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{<div style="text-align:center">P<sub>t</sub>=Z<sub>t</sub>+ν<sub>t</sub></div>}}{\deqn{ P_{t} = \gamma Z_{t}+\nu_{t} \hspace{2.5 cm} (2)}}

16	16		#' @details
17	17		#'
18	18		#' Let's consider the model:
	19	+	#'
19	20		#' \ifelse{html}{\out{<br><div style="text-align:center">Y<sub>t</sub>=β<sub>0</sub>+αP<sub>t</sub>+ε<sub>t</sub></div>}}{ \deqn{Y_{t} = \beta_{0} + \alpha P_{t} + \epsilon_{t}}}
20	21		#' \ifelse{html}{\out{<div style="text-align:center">P<sub>t</sub>=π'Z<sub>t</sub>+ν<sub>t</sub></div>}}{ \deqn{P_{t}=\pi^{'}Z_{t} + \nu_{t}}}
21	22		#'

33	34		#' \code{latentIV} considers \ifelse{html}{\out{Z<sub>t</sub>'}}{\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{<div style="text-align:center">Σ=(σ<sub>ε</sub><sup>2</sup>, σ<sub>0</sub><sup>2</sup>,
37	39		#' <br>     σ<sub>0</sub><sup>2</sup>, σ<sub>ν</sub><sup>2</sup>)</div>}}{
38	40		#' \deqn{\Sigma = \left(

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