Application of Cobb-Douglas Model in Forecasting Potential GDP Growth - Dang, Quyet Pham
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For a more comprehensive assessment of the economy's health, it is helpful to compare its actual production with its potential production. Economists achieve this by comparing the economy's actual output (revealed by real GDP) to its potential output. The Cobb-Douglas production function analyzes GDP growth in terms of various production factors, including labor, human capital, productive capital, and total factor productivity (TFP). This book employs the Cobb-Douglas production function to estimate Vietnam's potential GDP growth, utilizing statistical and machine learning models for…mehr

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Produktbeschreibung
For a more comprehensive assessment of the economy's health, it is helpful to compare its actual production with its potential production. Economists achieve this by comparing the economy's actual output (revealed by real GDP) to its potential output. The Cobb-Douglas production function analyzes GDP growth in terms of various production factors, including labor, human capital, productive capital, and total factor productivity (TFP). This book employs the Cobb-Douglas production function to estimate Vietnam's potential GDP growth, utilizing statistical and machine learning models for forecasting input factor growth, such as capital stock, human capital, labor, and TFP. Through this, we estimate their potential values and forecast potential GDP growth for the period 2023-2030. In this book, we offer a comparison of the performance of a statistical regression model and a machine learning regression model using a dataset from the period 1984-2022. We utilize R2, MSE, and the Bayesian Model Average (BMA) method as criteria to evaluate and select the optimal regression models.
Autorenporträt
Pham Dang Quyet: Member of Vietnam Statistical Association, consultant, lecturer at Kinh Bac University, National Academy of Public Administration, senior researcher at Institute of Statistical Science, Ministry of Labour, Invalids and Social Affairs with over 35 years of experience specializing in statistics and labour economics.