Do methods of estimation matter in detecting outliers and forecasting macroeconomic variables?

ABSTRACT

Outliers in time-series data are crucial in model estimation and forecasting. Understanding the importance of false detection of outliers in various autoregressive processes, the present study aims to evaluate test statistics’ performance by utilizing multiple models through robust estimation of errors. In this study, numerous data-generating techniques have been employed via simulation at different values of the estimates, location, and size of an outlier, sample sizes, and classical cutoff to access the power of the test statistics for false detection of outlier type. The findings of the simulation reveal that the location and size of outliers, parameter values, and, to some extent, the size of the series influence the behavior of test statistics in detecting the type of outliers. Overall, the estimation method of residual standard deviation influences the sampling behavior of the test statistics. Outliers also affect the forecasting performance in the model. All results are also validated empirically.

KEYWORDS:

• Time-series outliers
• Monte-Carlo simulation
• forecasting
• Inflation
• Test statistic

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Tanzeela Yaqoob

Tanzeela Yaqoob is PhD scholar, researcher and faculty member at the Department of Statistics, Universiy of Karachi.

Arfa Maqsood

Dr Arfa Maqsood is faculty member and researcher at the Department of Statistics, Universiy of Karachi.