Description Usage Arguments Value Methods (by generic) Author(s) References See Also Examples
lspkselect
implements IMSEoptimal datadriven procedures to select the number of partitioning knots for partitioningbased least squares regression estimators. Three series methods are supported: Bsplines, compact supported wavelets, and piecewise polynomials (generalized regressograms).
See Cattaneo and Farrell (2013) and Cattaneo, Farrell and Feng (2018a) for more technical details and further references.
Companion command: lsprobust
for partitioningbased least squares regression estimation and inference; 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 (2018b).
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 
y 
Outcome variable. 
x 
Independent variable. A matrix or data frame. 
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 
method 
Type of basis used for expansion. Options are 
ktype 
Knot placement. Options are 
kselect 
Method for selecting the number of inner knots used by 
proj 
If true, projection of leading approximation error onto the lowerorder approximating space
is included for bias correction (splines and piecewise polynomial only). Default is 
bc 
Bias correction method. Options are 
vce 
Procedure to compute the variancecovariance matrix estimator. Options are

subset 
Optional rule specifying a subset of observations to be used. 
... 
further arguments 
object 
class 

A matrix may contain 

A list containing options passed to the function. 
print
: print
method for class "lspkselect
".
summary
: summary
method for class "lspkselect
".
Matias D. Cattaneo, University of Michigan, Ann Arbor, MI. [email protected].
Max H. Farrell, University of Chicago, Chicago, IL. [email protected].
Yingjie Feng (maintainer), University of Michigan, Ann Arbor, MI. [email protected].
Cattaneo, M. D., and M. H. Farrell (2013): Optimal convergence rates, Bahadur representation, and asymptotic normality of partitioning estimators. Journal of Econometrics 174(2): 127143.
Cattaneo, M. D., M. H. Farrell, and Y. Feng (2018a): Large Sample Properties of PartitioningBased Series Estimators. Working paper.
Cattaneo, M. D., M. H. Farrell, and Y. Feng (2018b): lspartition: PartitioningBased Least Squares Regression. Working paper.
Cohen, A., I. Daubechies, and P.Vial (1993): Wavelets on the Interval and Fast Wavelet Transforms. Applied and Computational Harmonic Analysis 1(1): 5481.
lsprobust
, lsprobust.plot
, lsplincom
1 2 3 4  x < data.frame(runif(500), runif(500))
y < sin(4*x[,1])+cos(x[,2])+rnorm(500)
est < lspkselect(y, x)
summary(est)

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