Application of Intervention Analysis  Model in Yu Ebao Yield Prediction

Su, Jian Xin; Deng, Gaofeng

doi:10.4236/me.2014.58079

Cited by 2 publications

(2 citation statements)

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“…Su J. amd Deng G. [10] carried out a study on application of Intervention Analysis Model in Yu Ebao (monetary fund) Yield Prediction and found out that the effect of Niu's comment on decline in the yield was significant (0.148% decline in Yu Ebao's yield). The intervention model was given as…”

Section: Literature Reviewmentioning

confidence: 99%

Intervention Time Series Modeling of Infant Mortality: Impact of Free Maternal Health Care

Kisabuli

Jacob

Odero

2020

AJPAS

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Infant mortality is an important marker of the overall society health. The 3rd goal of the Sustainable Development Goals aims at reducing infant deaths that occur due to preventable causes by 2030. Due to increased infant mortality the Kenyan government introduced Free Maternal Health Care as an intervention towards reducing infant mortality through elimination of the cost burden of accessing medical care by the mother and the infant. The study examines the impact of Free Maternal Health Care on infant mortality using Intervention time series analysis particularly the intervention Box Jenkins ARIMA (Autoregressive Integrated Moving Average) model. There was significant support that Free Maternal Health Care had a significant impact on infant mortality which was estimated to be a decrease of 10.15% in infant deaths per month.

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Section: Literature Reviewmentioning

confidence: 99%

Intervention Time Series Modeling of Infant Mortality: Impact of Free Maternal Health Care

Kisabuli

Jacob

Odero

2020

AJPAS

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“…Intervention analysis has been used in other disciplines as well. Su and Deng [13] used time series regression with intervention term to predict the yield of Yu Ebao. Huang [14] has used intervention analysis to show that government intervention improved a firm's investment efficiency.…”

Section: Introductionmentioning

confidence: 99%

Using HAC Estimators for Intervention Analysis

Singh¹,

Dalpatadu²

2020

OJS

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The purpose of this article is to present an alternative method for intervention analysis of time series data that is simpler to use than the traditional method of fitting an explanatory Autoregressive Integrated Moving Average (ARIMA) model. Time series regression analysis is commonly used to test the effect of an event on a time series. An econometric modeling method, which uses a heteroskedasticity and autocorrelation consistent (HAC) estimator of the covariance matrix instead of fitting an ARIMA model, is proposed as an alternative. The method of parametric bootstrap is used to compare the two approaches for intervention analysis. The results of this study suggest that the time series regression method and the HAC method give very similar results for intervention analysis, and hence the proposed HAC method should be used for intervention analysis, instead of the more complicated method of ARIMA modeling. The alternative method presented here is expected to be very helpful in gaming and hospitality research.

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Application of Intervention Analysis Model in Yu Ebao Yield Prediction

Cited by 2 publications

References 1 publication

Intervention Time Series Modeling of Infant Mortality: Impact of Free Maternal Health Care

Intervention Time Series Modeling of Infant Mortality: Impact of Free Maternal Health Care

Using HAC Estimators for Intervention Analysis

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