Improving GDP Error Term Modeling: Application of the Combine White Noise Model to Australia | Chapter 6 | Science and Technology: Developments and Applications Vol. 5

The objective of the study is to improve the Gross Domestic Product (GDP) error term modeling, with the Combine White Noise (CWN) Model in Australia. The estimation of the Combine White Noise model passes the stability condition, stationary, serial correlation, and the CWN model estimation yields the best results with minimum information criteria and higher log-likelihood values than EGARCH  and VAR models. The determinant of the residual of the covariance matrix value indicates that CWN is efficient while the determinant of the residual of the covariance matrix value indicates that VAR is not efficient. CWN has the least forecast errors which are indications of the best results when compared with the EGARCH and VAR models dynamic evaluation forecast errors. The minimum forecast error values indicate forecast accuracy. The total results testified that CWN is the right model. To model conditional heteroscedasticity data with leverage effect in Australia and other nations powerfully, CWN is acclaimed. The contribution of this study to the scientific community is that the CWN gives suitable results that improve the weaknesses of the existing models. The CWN forecast output is more reasonable for effective policy making. Implementation of this CWN will boost the economy of the society.

 

Author (s) Details

 

Ayodele Abraham Agboluaje
Department of Mathematics Sciences, Faculty of Natural Sciences, Ibrahim Badamasi Babangida University, Lapai, Nigeria and School of Quantitative Sciences, College of Arts and Sciences, Universiti Utara Malaysia, Malaysia.

 

Suzilah Ismail
School of Quantitative Sciences, College of Arts and Sciences, Universiti Utara Malaysia, Malaysia.

 

Chee Yin Yip
Department of Economics, Faculty of Business and Finance, Universiti Tuanku Abdul Rahman, Malaysia.

 

Please see the book here:- https://doi.org/10.9734/bpi/stda/v5/4274

Extrusion Technology: Transforming Food Processing | Chapter 1 | Extrusion Technology: Transforming Food Processing

Extrusion technology has revolutionized the landscape of food processing, offering a versatile and efficient means of transforming raw ingredients into a diverse array of value-added food products. This book, "Extrusion Technology: Transforming Food Processing," provides a comprehensive overview of the principles, applications, and advancements in food extrusion, catering to the needs of students, researchers, and professionals in the food industry. The book begins by elucidating the fundamental principles underlying extrusion technology, including the mechanisms of mixing, shearing, heating, and shaping that characterize the extrusion process. Readers will gain a deeper understanding of the various types of extruders, their design features, and operational parameters, laying the groundwork for exploring the diverse applications of extrusion in food processing.
 
Subsequent chapters delve into the application of extrusion technology across different food categories, ranging from traditional staples like pasta and breakfast cereals, and confectioneries to emerging products such as meat analogs, functional ingredients, and plant-based alternatives. Moreover, the book explores the latest advancements and innovations in extrusion technology, including the incorporation of novel ingredients, the use of advanced processing techniques, and the integration of sustainability principles into extrusion processes.
 
In conclusion, "Extrusion Technology: Transforming Food Processing" serves as valuable resource for anyone seeking to deepen their understanding of food extrusion technology and harness its transformative potential in food product development.

Author(s) Details:

Dr. Prerana Shere,
MIT School of Food Technology, MIT Art, Design and Technology University, Pune, Maharashtra, India.

Dr. Rinku Agrawal,
MIT School of Food Technology, MIT Art, Design and Technology University, Pune, Maharashtra, India.

Please see the link here: https://stm.bookpi.org/ETTFP/article/view/14295