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Lingyun QU
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Digitalization has long been regarded as a driving force and engine for the promotion of countries’ regional and national economy. However, in countries where regional inequalities of digitalization development prevail, the impact of the spillover effect is worth discussing. Based on prefectural panel data of Japan from 2011 to 2019, this paper utilizes the Principal Component Analysis (PCA) method for constructing the digitalization development index and Directed Acyclic Graphs (DAGs) for measuring the direction of digitalization spillover effects, I empirically investigate impacts of digitalization development and digitalization spillover on economic performance. The results from OLS regressions with fixed effects show that in general, digitalization development positively influences the overall prefectural economic behavior, but the spillover effect negatively impacts overall production and production per capita. Population aging and geographical closeness to countries with advanced digitalization such as China and South Korea worsen the negative influences of the spillover effect, but entertainment and life-related service industry development mitigate instead. Therefore, policies on digitalization development should take into account local realities and coordinate with the prefecture’s industrial structure, population characteristics, and geographical location to improve economic performance.
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This paper constructed regression models on exchange rate prediction and conducted predicting process with different combinations of regressors. Identification was based on KRW-USD exchange rate data from June 2001 to December 2020. Comparisons of regression results suggest that the fitness of the model is significantly improved when the exchange rate in the former period is introduced as a regressor, but the fitted model does not seem to be desirable for prediction due to the lag of time. Whereas the both domestic and foreign inflation rates are significant for exchange rate prediction, the long run exchange rate is less important than short run exchange rate in most of the models. With respect to above findings and the failure of constructing a model with smaller RMSE than the benchmark model, this paper proposed that the undesirable result of exchange rate prediction may be attributed to ignoring short shocks and reference dependence.
Short description of portfolio item number 1
Short description of portfolio item number 2 
Published in Journal 1, 2009
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1). http://academicpages.github.io/files/paper1.pdf
Published in Journal 1, 2010
This paper is about the number 2. The number 3 is left for future work.
Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2). http://academicpages.github.io/files/paper2.pdf
Published in Journal 1, 2015
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3). http://academicpages.github.io/files/paper3.pdf
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Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.