| 研究生: |
林芳伃 Lin, Fang-Yu |
|---|---|
| 論文名稱: |
行動寬頻普及率與人均國家生產毛額之地理依存性 Geographical Dependence Observed between the Mobile Broadband Penetration Rate and Gross Domestic Product Per Capita |
| 指導教授: |
林珮珺
Lin, Pei-Chun |
| 共同指導教授: |
黃郁雯
Huang, Yu-Wen |
| 學位類別: |
碩士 Master |
| 系所名稱: |
管理學院 - 電信管理研究所 Institute of Telecommunications Management |
| 論文出版年: | 2019 |
| 畢業學年度: | 107 |
| 語文別: | 中文 |
| 論文頁數: | 80 |
| 中文關鍵詞: | 行動寬頻普及率 、人均GDP 、空間計量經濟模型 、空間外溢效果 |
| 外文關鍵詞: | Mobile Broadband Penetration, GDP per capita, Spatial econometrics analysis, Spatial Spillover Effect |
| 相關次數: | 點閱:172 下載:1 |
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本研究探討之主題為全球行動寬頻普及率是否具有空間依存性,同時以人均GDP做為一國富裕程度之資料,驗證與行動寬頻普及率間之相關性。根據國家電信聯盟(ITU)公布之資料顯示,全球行動寬頻普及率偏高之國家多彼此相鄰,群聚於特定區域,反之亦然,為了能夠實證行動寬頻普及率空間依存性,本研究除了使用統計之傳統迴歸模型外,更使用地理資訊分析軟體ArcGIS進行探索性空間資料分析驗證行動寬頻普及率是否符合本研究之假設,確立行動寬頻普及率與人均GDP皆具有空間依存性後,使用國家距離與電信產業之特性建立空間權重矩陣,並運用空間計量經濟學中之空間迴歸模型分析國家間行動寬頻普及率間之空間交互作用與模型之配適程度,而使用之空間迴歸模型包括空間落遲模型(Spatial Lag Model)、空間自變數落遲模型(Spatial Lag of X Model)以及空間Durbin模型(Spatial Durbin Model)。研究結果顯示無論於傳統迴歸模型、空間落遲模型、空間自變數落遲模型及空間Durbin模型皆說明行動寬頻普及率與人均GDP之間具有相關性,且迴歸模型相互比較之下顯示,空間迴歸模型之配適度表現更甚於傳統迴歸模型,避免行動寬頻普及率與人均GDP相關係數高估之情形,說明將空間因素納入研究之考量後更可呈現研究資料之特性,進而驗證了空間效果之重要性。本研究藉由一國之富裕程度解釋行動寬頻普及率尚未充分,建議後續研究可探討尚未被發掘之變數,並提供政府單位制定行動寬頻相關政策之建議方向。
This research aims to explore whether mobile broadband penetration has spatial dependence and use the GDP per capita as a country's wealth level to verify the correlation with mobile broadband penetration. Based on the data which published by International Telecommunication Union(ITU) indicated the country has a high mobile broadband penetration rate, the adjacent countries also has a high mobile broadband penetration rate and vice versa. This study uses ArcGIS for exploratory spatial data analysis to verify that the mobile broadband penetration rate is consistent with the hypothesis of this study. The spatial weight matrix is established by using the country's distance and the characteristics of the telecom industry then using the spatial regression model to analyze the spatial interaction. As a result, the spatial regression model is better than a linear regression model to avoid the overestimation of the coefficient. Explain that take the spatial factors into account evidencing the importance of spatial effects. Suggested that follow-up researchers can explore the hidden variables that and observe the changes in the results of the variables after adding the spatial regression model try to provide the direction of the policymakers to enhance the performance of the mobile broadband.
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