Abstract
The existence of outliers and structural breaks in time series data offer
challenges to data analysts in model identification, estimation and
validation. Detection of outliers of a different nature and structure is the
focus of the current study. To analyze the impact of structural breaks and
outliers on model identification, estimation and their inferential analysis, we
use two data generating processes; MA (1) and ARMA (1, 1). The
performance of the test statistics for detecting additive outlier (AO),
innovative outlier (IO), level shift (LS) and transient change (TC) is
investigated using simulations. For evaluation, power of test, empirical level
of significance, empirical critical values, misspecification frequencies,
and sampling distribution of estimators for the two models are calculated.
The empirical critical values are found higher than the theoretical cut-off
(C); empirical power of the test statistics is not satisfactory for small sample
size, large C and large model coefficients. The confusion between LS, AO,
TC, and IO assuming different C and sample sizes is also explored. Further,
empirical evidence is noticed that for Pakistan using 3-stage iterative
procedure to detect multiple outliers and structural breaks. It is found that
neglecting shocks lead to wrong identification, biased estimation, and excess
kurtosis.
Amena Urooj, Zahid Asghar. (2020) Evaluation of Test Statistics for Detection of Outliers and Shifts, Journal of Quantitative Methods, Volume 4, Issue 2.
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