TY - JOUR
DP - National Agricultural Library
DB - PubAg
JO - Journal of econometrics
TI - Self-weighted LAD-based inference for heavy-tailed threshold autoregressive models
A1 - Yang, Yaxing
A4 - Yang, Yaxing
A4 - Ling, Shiqing
EP - 2017 v.197 no.2
KW - econometric models
KW - economic analysis
KW - economic theory
KW - least squares
KW - statistical inference
AN - 6107824
AB - The least squares estimator of the threshold autoregressive (TAR) model may not be consistent when its tail is less than or equal to 2. Neither theory nor methodology can be applied to model fitting in this case. This paper is to develop a systematic procedure of statistical inference for the heavy-tailed TAR model. We first investigate the self-weighted least absolute deviation estimation for the model. It is shown that the estimated slope parameters are n-consistent and asymptotically normal, and the estimated thresholds are n-consistent, each of which converges weakly to the smallest minimizer of a compound Poisson process. Based on this theory, the Wald test statistic is considered for testing the linear restriction of slope parameters and a procedure is given for inference of threshold parameters. We finally construct a sign-based portmanteau test for model checking. Simulations are carried out to assess the performance of our procedure and a real example is given.
PY - 2017
LA -
DA - 2017-04
VL - v. 197
IS - no. 2
SP - pp. 368-381
DO - 10.1016/j.jeconom.2016.11.009
ER -