*! version 1.0.8 22Mar2017

Title

thsearch -- Threshold search model for non-linear models based on information criterion

Syntax

thsearch
depvar
[indepvars]
[if] [in]
, thvar(varname) [intvar(varname)]
thnum(numlist) emodel(string)
[other_options]

Main
step(#) specify the size/interval of the threshold, default is 1

criteria(string) choose information criteria for model selection, default is BIC (Schwarz, 1978)

savefile(filename) save a dataset with the results from all the iterations in the file filename.dta

replace replace the dataset specified in savefile(filename) if it already exists

minth(#) specify the value for the first threshold

maxth(#) specify the value for the last threshold

Description

thsearch implements the threshold search model based on information criterion for optimal threshold model selection. See Gannon, Harris and Harris (2014) for details.

Required Settings

depvar the outcome variable.

thvar the explanatory variable where thresholds are to be defined.

thnum(#) the number of threshold to be determined, e.g. thnum(2) estimates the threshold models with 3 subpopulations.

emodel the estimation model. Currently supports regress, xtreg, fe probit, oprobit. To specifiy options for regression models, type the regression options followed by comma, e.g. emodel(oprobit, vce(robust)). For xtreg, fe, dataset must be declared as panel dataset.

Options

Main

indepvars the list of explanatory variables.

intvar constructs interaction terms between intvar and the dummy variables defined by the thresholds.

stepsize(#) the increment of threshold value in each iteration. Positive real number.

criteria(string) AIC, BIC, CAIC, HQIC. Default is BIC (Schwarz, 1978).

savefile(filename) saves a dataset with the information criterion values of each iterations. If savefile(filename) is specified, filename.dta will contain the following variables:

emodel: regression model specified in emodel.

bic: Bayesian information criterion or Schwarz criterion (BIC).

aic: Akaike information criterion (AIC).

aicc: AIC with a correction for finite sample sizes.

hqic: Hannan–Quinn information criterion (HQIC).

num_threshold: number of thresholds specified in thnum(#).

tau_#: value of threshold #.

replace replaces the dataset specified in savefile(filename) if it already exists.

minth(#) Default is the minimum value of thvar.

maxth(#) Default is the maximum value of thvar.

Remarks

thsearch supports at most 6 thresholds. Depending on the uses, we generally do not recommend users to specify more than 4 thresholds as the estimation process quickly becomes computationally prohibitive.

Saved Results

thsearch stores the ereturn of the optimal threshold model. In addition, thsearch also stores the following in r():

r(tau_#) value of threshold #

r(mincri) value of the minimum information criterion

r(cri) information criterion specified

r(emodel) regression model specified

Reference(s)

Gannon B., Harris D., and Harris M. (2014), Threshold effects in nonlinear models with an application to the social capital-retirement-health relationship, Health Econ., 23, pp. 1072-1083.

Author(s)

Ho Fai Chan

Queensland University of Technology

Brisbane, Queensland, Australia

hofai.chan@qut.edu.au

Brenda Gannon

Centre for the Business and Economics of Health

University of Queensland

Brisbane, Australia

brenda.gannon@uq.edu.au

David Harris

Department of Economics

University of Melbourne

Melbourne, Australia

harrisd@unimelb.edu.au

Mark Harris

School of Economics and Finance

Curtin Business School

Curtin University of Technology

Perth, Australia

mark.harris@curtin.edu.au