Advances in Longitudinal Data Methods in Applied Economic Research: 2020 International Conference on Applied Economics (ICOAE)

This book presents new methods and applications in longitudinal data estimation methodology in applied economic, featuring selected papers from the 2020 International Conference on Applied Economics (ICOAE 2020).

Format: Paperback / softback
Length: 552 pages
Publication date: 02 April 2022
Publisher: Springer Nature Switzerland AG

This comprehensive volume offers cutting-edge approaches and innovative applications in the field of longitudinal data estimation methodology, particularly in the realm of applied economics. Drawing from a selection of outstanding papers presented at the 2020 International Conference on Applied Economics (ICOAE 2020), which was held virtually due to the unprecedented challenges posed by the COVID-19 pandemic, this book delves into a wide range of interdisciplinary topics, including financial economics, international economics, agricultural economics, marketing, and management. Additionally, it showcases captivating country-specific case studies, providing valuable insights into the diverse economic landscapes across the globe.

The importance of longitudinal data analysis in applied economics cannot be overstated. By studying the evolution of economic variables over time, researchers can gain a deeper understanding of the underlying mechanisms driving economic phenomena and develop more effective policies and strategies to address economic challenges.

One of the key challenges in longitudinal data analysis is the presence of missing data. Missing data can occur for a variety of reasons, such as survey errors, data collection issues, or the unavailability of certain variables. When dealing with missing data, it is essential to develop robust methods that can handle the missing information effectively.

Several methods have been developed to address the issue of missing data in longitudinal data analysis. These methods include imputation, multiple imputation, and the use of machine learning techniques. Imputation involves replacing the missing values with estimates based on other available data or statistical models. Multiple imputation involves creating multiple imputations of the missing data and combining them to obtain a more accurate estimate. Machine learning techniques, such as neural networks and random forests, can be used to predict the missing values based on the available data.

Another important aspect of longitudinal data analysis is the consideration of temporal effects. Economic variables may exhibit temporal dependencies, such as seasonality or trends. It is essential to account for these temporal effects when conducting longitudinal data analysis to obtain accurate estimates and avoid biases.

Several methods have been developed to account for temporal effects in longitudinal data analysis. These methods include the use of seasonal adjustment techniques, such as the X-12-ARIMA model, and the use of time-series models, such as the ARMA model. Seasonal adjustment techniques involve removing the seasonal effects from the data and then regressing the residuals on the time-series variables. Time-series models involve modeling the dynamics of the economic variables over time and accounting for the autoregressive and moving average components of the data.

In addition to the methods discussed above, longitudinal data analysis also involves the use of other statistical techniques, such as regression analysis, panel data analysis, and spatial data analysis. Regression analysis involves modeling the relationship between the dependent variable and the independent variables using linear or nonlinear regression models. Panel data analysis involves analyzing data from multiple individuals or groups over time. Spatial data analysis involves analyzing data that are spatially related, such as economic variables at different geographic locations or time periods.

Longitudinal data analysis has numerous applications in applied economics, including in the fields of finance, economics, agriculture, marketing, and management. For example, in finance, longitudinal data analysis can be used to study the relationship between stock prices and economic indicators, such as GDP or inflation. In economics, longitudinal data analysis can be used to study the effects of economic policies on economic growth and development. In agriculture, longitudinal data analysis can be used to study the effects of agricultural policies on crop yields and food security. In marketing, longitudinal data analysis can be used to study the effects of marketing campaigns on consumer behavior and sales. In management, longitudinal data analysis can be used to study the effects of management practices on organizational performance and employee satisfaction.

In conclusion, this comprehensive volume offers cutting-edge approaches and innovative applications in the field of longitudinal data estimation methodology, particularly in the realm of applied economics. By examining interdisciplinary topics such as financial economics, international economics, agricultural economics, marketing, and management, as well as showcasing captivating country-specific case studies, this book provides valuable insights into the diverse economic landscapes across the globe. The importance of longitudinal data analysis in applied economics cannot be overstated, and the development of robust methods to handle missing data and account for temporal effects is crucial to obtaining accurate estimates and avoiding biases. The use of other statistical techniques, such as regression analysis, panel data analysis, and spatial data analysis, further enhances the capabilities of longitudinal data analysis in applied economics. With its wide range of applications, longitudinal data analysis has the potential to revolutionize the way we understand and analyze economic phenomena and develop effective policies and strategies to address economic challenges.

Weight: 854g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030639723
Edition number: 1st ed. 2021