• The Journal of Asian Finance, Economics and Business
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• v.8 no.10
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• pp.239-247
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• 2021

Central Bank authorities should carefully manage inflation rate uncertainties to achieve economic growth and development not only in the short-run but also in the long-run. Since inflation is a key macroeconomic variable, an increased understanding about its behavior is undoubtedly important. Thus, paper employs unit root with breakpoints to examine the mean reverting behavior of inflation rate in the Philippines using monthly data from 2002 to 2020. Empirically, the unit root breakpoint innovational and additive outlier tests favor the stationarity or mean reverting behavior of inflation in the Philippines. Also, results of standard unit root tests, ADF, PP, GLS-Dickey-Fuller, KPSS and NP, provide strong evidence of mean reverting processes. The mean reverting behavior of inflation rate reveals that the monetary policy using inflation targeting framework has succeeded in reducing chronic inflation persistence in the Philippines. Thus, this research supports inflation targeting policy that aims to maintain general price level stability for the Philippine economy's long-term growth and development prospects. The findings of this research remain important for the central bankers for not only providing them better understanding about the behavior of inflation rate, but also helping them formulate and implement policy reforms related to money, credit and banking.

• Journal of Korean Institute of Industrial Engineers
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• v.38 no.1
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• pp.17-24
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• 2012

We address some potential problems with the existing procedures of outlier detection in time series. Also we propose modifications in estimating model parameters and outlier effects in order to reduce the number of tests and to increase the detection accuracy. Experiments with some artificial data sets show that the proposed procedure significantly reduces the number of tests and enhances the accuracy of estimated parameters as well as the detection power.

• Journal of the Korean Statistical Society
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• v.22 no.2
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• pp.283-306
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• 1993

An outlier detection diagnostic for the detection of k-consecutive atypical observations is considered. The proposed diagnostic is based on the innovational variance estimate utilizing both the interpolated and the predicted residuals. We adopt the interpolation method to construct the proposed diagnostic by replacing atypical observations. The perfomance of the proposed diagnositc is investigated by simulation. A real example is presented.