Question on package thres_infer
by Andreas Zervas
Hi all, especially Sven,
I was playing with the package thres_infer, and it appears that the threshold estimates from functions H_thresh_test() and H_thresh_estim() differ. Is it intented? Should they be the same? In the particular example from the sample script, which I pasted below, the values are similar, but I run it with data that give totally different results.
Any thought - suggestions?
Best regards,
Andreas
gretl version 2024d
Current session: 2025-04-08 16:37
# Sample script for thresh_infer
Read datafile C:\Program Files\gretl\data\misc\denmark.gdt
periodicity: 4, maxobs: 55
observations range: 1974:1 to 1987:3
Listing 5 variables:
0) const 1) LRM 2) LRY 3) IBO 4) IDE
Test of Null of No Threshold Against Alternative of Threshold
Under Maintained Assumption of Homoskedastic Errors
Number of Bootstrap Replications: 1000
Trimming percentage: 0.150000
Threshold Estimate: 0.088000
(46 % of obs in 1st regime)
LM-test for no threshold: 4.154898
Bootstrap p-value: 0.923000
*******************************************
Threshold regression based on Hansen (2000)
User choice: assume homoskedasticity
*******************************************
Global OLS Estimation, Without Threshold
Dependent Variable: mg
OLS Standard Errors Reported
coefficient std. error z p-value
-------------------------------------------------------
Constant 0.0705718 0.0272334 2.591 0.0096 ***
IBO -0.469693 0.229408 -2.047 0.0406 **
IDE 0.110332 0.500081 0.2206 0.8254
Observations = 54
Degrees of Freedom = 51
Sum of Squared Errors = 0.0487311
Residual Variance = 0.000955512
R-squared = 0.162774
Heterosked. test p-val = 0.365732
*************************************************************
Threshold Estimation, dependent variable: mg
Threshold Variable: IDE
Threshold Estimate = 0.074 90% CI: [0.074, 0.11]
Sum of Sq. Errors = 0.0435625 Residual Var. = 0.000907553
Joint R-squared = 0.252
Heterosked. test p-value: 0.225
*************************************************************
Regime 1: IDE <= 0.074000
(standard errors do not take into account threshold uncertainty)
coefficient std. error z p-value
-------------------------------------------------------
Constant -0.833454 0.386015 -2.159 0.0308 **
IBO -0.774593 0.915857 -0.8458 0.3977
IDE 13.4116 6.06844 2.210 0.0271 **
Observations = 5
Degrees of Freedom = 2
Sum of Squared Errors = 0.00612267
Residual Variance = 0.00306133
R-squared = 0.425631
Regime 2: IDE > 0.074000
(standard errors do not take into account threshold uncertainty)
coefficient std. error z p-value
-------------------------------------------------------
Constant 0.0727286 0.0303053 2.400 0.0164 **
IBO -0.487763 0.233237 -2.091 0.0365 **
IDE 0.116314 0.505722 0.2300 0.8181
Observations = 49
Degrees of Freedom = 46
Sum of Squared Errors = 0.0374399
Residual Variance = 0.000813911
R-squared = 0.178561
1 year, 1 month
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Gretl in format snap or flatpak
by Carlos Andrade
Hello, friends.
I am a Debian 12 user and the version of gretl installed is 2022c. How do I
install a newer version? Is there a version in snap or flatpak format?
Atenciosamente,
Carlos Antonio Soares de Andrade
Economista e Consultor de Empresas
Telefone: (83) 9 98117113
1 year, 1 month
- 3
- 4
Soft threshold operator
by Riccardo (Jack) Lucchetti
Hi all,
yesterday me and a colleague found ourselves writing, for the millionth
time, a hansl function implementing the so-called "soft thresholding"
operator, and I was thinking that, although it's very easy, it'd be nice
if it were a builtin function. So I wrote a hansl function that IMO is
quite nice and general (works with scalars, series and matrices) and is
in the attached script, along with a few usage examples[*].
Note that the function could be made more general, for example
introducing the scalar rho as in
https://www.sciencedirect.com/topics/computer-science/soft-thresholding
, eq. (11.46) or allowing for "hard thresholding", like in the
Mathematica function "wthresh", but maybe not for now.
What I'm asking the community is:
1) Is it worthwhile having a dedicated function for this at all?
2) If so, should we have this as a builtin function[**], or should it
just be in extra?
3) Does anyone have better ideas on the function's signature?
[*] I know, it could have written in a cooler way by usign the sgn()
function and conditional assignment, but hey.
[**] We already have a C version for scalars in plugins/regls.c.
-------------------------------------------------------
Riccardo (Jack) Lucchetti
Dipartimento di Scienze Economiche e Sociali (DiSES)
Università Politecnica delle Marche
(formerly known as Università di Ancona)
r.lucchetti(a)univpm.it
http://www2.econ.univpm.it/servizi/hpp/lucchetti
-------------------------------------------------------
1 year, 1 month
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