Description Usage Arguments Value Methods (by generic) Author(s) References See Also Examples
lsprobust
implements partitioning-based least squares point estimators for the regression function and its derivatives. It also provides robust bias-corrected (pointwise and uniform) inference, including simulation-based confidence bands. Three series methods are supported: B-splines, compact supported wavelets, and piecewise polynomials.
See Cattaneo and Farrell (2013) and Cattaneo, Farrell and Feng (2019a) for complete details.
Companion commands: lspkselect
for data-driven IMSE-optimal selection of the number of knots on rectangular partitions; lsprobust.plot
for plotting results; lsplincom
for multiple sample estimation and inference.
A detailed introduction to this command is given in Cattaneo, Farrell and Feng (2019b).
For more details, and related Stata and R packages useful for empirical analysis, visit https://sites.google.com/site/nppackages/.
1 2 3 4 5 6 7 8 9 10 11 12 13 | lsprobust(y, x, eval = NULL, neval = NULL, method = "bs", m = NULL,
m.bc = NULL, deriv = NULL, smooth = NULL, bsmooth = NULL,
ktype = "uni", knot = NULL, nknot = NULL, same = TRUE,
bknot = NULL, bnknot = NULL, J = NULL, bc = "bc3", proj = TRUE,
kselect = "imse-dpi", vce = "hc2", level = 95, uni.method = NULL,
uni.grid = NULL, uni.ngrid = 50, uni.out = FALSE, band = FALSE,
B = 1000, subset = NULL, rotnorm = TRUE)
## S3 method for class 'lsprobust'
print(x, ...)
## S3 method for class 'lsprobust'
summary(object, ...)
|
y |
Outcome variable. |
x |
Independent variable. A matrix or data frame. |
eval |
Evaluation points. A matrix or data frame. |
neval |
Number of quantile-spaced evaluating points. |
method |
Type of basis used for expansion. Options are |
m |
Order of basis used in the main regression. Default is |
m.bc |
Order of basis used to estimate leading bias. Default is |
deriv |
Derivative order of the regression function to be estimated. A vector object of the same
length as |
smooth |
Smoothness of B-splines for point estimation. When |
bsmooth |
Smoothness of B-splines for bias correction. Default is |
ktype |
Knot placement. Options are |
knot |
A list of numeric vectors giving the knot positions (including boundary knots) for each dimension
which are used in the main regression. The length of the list is equal to |
nknot |
A numeric vector of the same length as |
same |
If |
bknot |
A list of numeric vectors giving knot positions used for bias correction. If not
specified and |
bnknot |
A numeric vector of the same length as |
J |
A numeric vector containing resolution levels of father wavelets for each dimension. |
bc |
Bias correction method. Options are |
proj |
If |
kselect |
Method for selecting the number of inner knots used by |
vce |
Procedure to compute the heteroskedasticity-consistent (HCk) variance-covariance matrix estimator with plug-in residuals. Options are
|
level |
Confidence level used for confidence intervals; default is |
uni.method |
Method used to implement uniform inference. Options are |
uni.grid |
A matrix containing all grid points used to implement uniform inference. Each row correponds to the coordinates of one grid point. |
uni.ngrid |
A numeric vector of the same length as |
uni.out |
If |
band |
If |
B |
Number of simulated samples used to obtain the critical value for confidence bands.
Default is |
subset |
Optional rule specifying a subset of observations to be used. |
rotnorm |
If |
... |
further arguments |
object |
class |
|
A matrix containing eval (grid points), N (effective sample sizes),
tau.cl (point estimates with a basis of order |
|
A matrix containing the number of inner partitioning knots used in the main regression and bias correction for each covariate. |
|
A list of knots for point estimation. |
|
A list of knots for bias correction. |
|
Critical value for constructing confidence band. |
|
A list containing quantities used to implement uniform inference. |
|
A list containing options passed to the function. |
print
: print
method for class "lsprobust
"
summary
: summary
method for class "lsprobust
"
Matias D. Cattaneo, Princeton University, Princeton, NJ. cattaneo@princeton.edu.
Max H. Farrell, University of Chicago, Chicago, IL. max.farrell@chicagobooth.edu.
Yingjie Feng (maintainer), Princeton University, Princeton, NJ. yingjief@princeton.edu.
Cattaneo, M. D., and M. H. Farrell (2013): Optimal convergence rates, Bahadur representation, and asymptotic normality of partitioning estimators. Journal of Econometrics 174(2): 127-143.
Cattaneo, M. D., M. H. Farrell, and Y. Feng (2019a): Large Sample Properties of Partitioning-Based Series Estimators. Annals of Statistics, forthcoming. arXiv:1804.04916.
Cattaneo, M. D., M. H. Farrell, and Y. Feng (2019b): lspartition: Partitioning-Based Least Squares Regression. R Journal, forthcoming. arXiv:1906.00202.
Cohen, A., I. Daubechies, and P.Vial (1993): Wavelets on the Interval and Fast Wavelet Transforms. Applied and Computational Harmonic Analysis 1(1): 54-81.
lspkselect
, lsprobust.plot
, lsplincom
1 2 3 4 |
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