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| Title: | 平均邊際效果中處理連續自變數相關之方法
Addressing the Correlation between Continuous Covariates in Average Marginal Effects |
| Authors: | 蔡岳倫
Tsai, Yueh-Lun |
| Contributors: | 蔡宗漢
Tsai, Tsung-Han
蔡岳倫
Tsai, Yueh-Lun |
| Keywords: | 廣義線性模型
平均邊際效果
異質性效果
次群體比較
經濟投票
GLM model
Average marginal effect
Heterogeneous effect
Subgroup comparison
Economic voting |
| Date: | 2024 |
| Issue Date: | 2024-08-05 14:34:02 (UTC+8) |
| Abstract: | 在政治學研究的討論中，經常涉及不同自變數對同一個依變數的影響，而這些自變數之間時常是彼此相關的。舉例來說，在選舉研究的文獻中發現，選民對政府經濟表現的評估與其政黨認同都會對投票選擇產生影響。然而亦有許多研究發現，選民的經濟表現評估和政黨認同存在相關性，當認同的政黨執政時，選民傾向給予較好的經濟表現評估。若要在這樣的架構下分析自變數的影響效果，必然需要將彼此的相關性納入估計，並於解釋效果時排除相關性的影響，使比較可以在控制變數相同的情況下進行。近年來，許多研究者在對模型結果進行詮釋時，不是直接觀察迴歸係數估計值，而是計算「平均邊際效果」（average marginal effect），特別是採用非線性模型時更是如此，認為這樣的詮釋更為直觀，也可以避免研究者設定變數時必然面臨的代表性問題。然而，前述所提及自變數間的相關性，卻可能對於平均邊際效果的計算產生影響，進而導致錯誤推論的可能性。由於在計算平均邊際效果時將代入資料中的實際觀察值，則產生該筆資料背後的變數間相關性便會同時透過估計係數與個別觀察值的變數分布對平均邊際效果產生影響，這使得平均邊際效果的解釋並不能如同係數解釋般排除變數間的相關性。若未辨明兩者的差異，可能不經意地在解釋上強加了變數間相互獨立的假定。從自變數聯合分布的觀點，本文認為邊際效果作為預測結果解釋途徑，選擇如何於計算過程中設定變數是檢驗理論與推論母體上的取捨。檢驗理論要求符合控制其他因素相同的條件，而推論母體則要求變數設定符合真實情形，於是如何選擇設定變數將視研究目的而定。本文透過效果平均，將既有文獻提到的幾種邊際效果解釋囊括於設定自變數於特定水準與否的架構中，一方面釐清平均邊際效果的意義，另一方面也檢視變數相關性所帶來的影響，並嘗試提出消彌變數相關性在平均邊際效果影響的方法。本文透過模擬分析證實相關性的影響，並分別檢視其影響在不同的變數分布、平均預測機率、以及效果規模中有何不同。最後，本文以經濟投票為例，說明文中提出的效果平均如何應用於不同研究目的。
Political science research always aims to explore the influence of multiple correlated variables. For example, both voters' evaluations of government on economic performance and their party identification significantly impact voting choices, and these variables often exhibit correlations: voters tend to assess the economy more positively when their preferred party holds power. To estimate the effect of variable, it is crucial to account for these correlations. When interpreting the effects, the influence of these correlations must be excluded to ensure that comparisons are made under all-else-equal conditions. While many researchers advocate for using "average marginal effect" (AME) as an alternative to interpreting model instead of coefficients in complex model settings such as nonlinear model, I argue that the role of correlations differs between these approaches. Failure to distinguish between them can lead to unintended assumptions of independence. From the perspective of the joint distribution of variables, this paper argues that using marginal effects as a method of interpreting predictive values involves trade-offs in how variables are set. On the one hand, testing theory requires controlling for other variables, while inferring population requires setting variables as they would be in real situations. The choice of how to set variables depends on the research purpose. I incorporate different marginal effect interpretations into the framework of setting variables at specific levels through average of effects, and I use this to clarify the meaning of AMEs, examine the impact of variable correlations, and propose methods to mitigate these effects. I demonstrate these effects by simulation and illustrate how they vary across different variable distributions, average predicted probabilities, and effect sizes. Using an example of economic voting, I show how the proposed methods can be applied to various research purposes. |
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| Description: | 碩士
國立政治大學
政治學系
110252019 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0110252019 |
| Data Type: | thesis |
| Appears in Collections: | [政治學系] 學位論文
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