Predicting gross domestic product using autoregressive models

Roush, John; Siopes, Keith; Hu, Gongzhu

doi:10.1109/sera.2017.7965745

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…The criteria compares the residuals of the models and estimates the relative information loss of representing the original data using each of the model. In addition, the criteria weighs the quality of fit (covariance of residuals) against the complexity (number of free parameters) and therefore the model with least criterion value is considered as seen in Roush et. al.…”

Section: Methodsmentioning

confidence: 99%

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Application of the Vector Autoregressive Model Incorporating New Measurements Using the Bayesian Approach

Musyoki,

Alilah,

Angwenyi

2023

AJERNET

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In this paper, an application of the updated vector autoregresive model incorporating new information or measurements is considered. We consider secondary data obtained from the Kenya National Bureau of statistics, Statistical Abstract reports from 2000-2021 which is on monetary value marketed at current prices from crops, horticulture, livestock and related products, fisheries and forestry. A VAR(1) model is fitted to the data and then the model updated to incorporate the measurements. From the results, it is found that the updated model performs well on the simulated data based on the values of the root mean square error obtained.

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Section: Methodsmentioning

confidence: 99%

“…Recently, the vector autoregressive models have gained much application in a wide range of disciplines such as medicine, epidemiology, economics, biology and macroeconomics among others. For instance, an application of the VAR models is given by Roush et. al.…”

Section: Introductionmentioning

confidence: 99%

Application of the Vector Autoregressive Model Incorporating New Measurements Using the Bayesian Approach

Musyoki,

Alilah,

Angwenyi

2023

AJERNET

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“…Weather is inseparable from our daily life. Roush et al tried to predict gross domestic product by using autoregressive models [14]. The model predicts a current state from previous values in time-series data.…”

Section: Considerationsmentioning

confidence: 99%

Estimation of Daily Life Time Series Data Affected by Rainfall

Hochin¹,

Nomiya²

2018

IJET

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The amount of sewage flow, which is one of daily life data, was estimated for their efficient management. The amounts of flow of a typical day were tried to be adjusted to those of a day. The values for the adjustment were tried to be estimated by using the multiple regression analysis. This method is applied to the estimation of the ammonia nitrogen concentration, which is the major factor of the quality of sewage flow. The estimation results show that this method is applicable to the estimation of the ammonia nitrogen concentration, and that the amount of rainfall is dominant in estimating the ammonia nitrogen concentration.

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