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    Title: 通貨膨脹目標機制的政策效果估計 : 機器學習與模型平均方法之實證應用
    Estimating Treatment Effects of Inflation Targeting with Machine Learning and Model Combination
    Authors: 羅婉蘋
    LO, Wan-Ping
    Contributors: 廖仁哲
    Liao, Jen-Che
    羅婉蘋
    LO, Wan-Ping
    Keywords: 因果推論
    通貨膨脹目標機制
    機器學習
    模型平均方法
    實證蒙地卡羅模擬
    Causal Inference
    Inflation Targeting
    Machine Learning
    Model Averaging
    Empirical Monte Carlo Simulations
    Date: 2023
    Issue Date: 2023-08-02 13:42:03 (UTC+8)
    Abstract: 本研究旨在應用機器學習與模型平均方法估計通膨目標機制(inflation targeting, IT）的政策效果。我們採用7種機器學習方法估計傾向分數（propensity scores），亦即採行IT政策的條件機率；不同於機器學習模型的傳統作法，我們納入組間變數均衡(covariate balance)、共同支撐區間(common support)等攸關政策效果認定（identification）條件的相關指標，用以調校機器學習相關超參數（hyperparameters），以反映估計「因果」關係，而非「預測」的研究焦點；最後，我們以機率倒數加權法（inverse probability weighting）估計實施IT對於降低通膨率的平均處理效果（average treatment effect, ATE）與處理組平均處理效果（average treatment effect on the treated, ATT）。
    本文的另一研究重點在於利用模型平均（model averaging）方法合併（或線性組合）前述各種機器學習模型所得之ATE與ATT 估計值，以期進一步改善單一個別機器學習模型的政策效果估計。我們的模型平均方法包括模型簡單平均（Equal Weight, EQ）與模型加權平均（Combined），這兩種模型平均方法皆非常直觀與簡易計算，前者的模型組合權重固定、不需估計，而後者的模型組合權重由資料決定，可用最小平方法估計而得。
    我們利用實證蒙地卡羅模擬法 (empirical Monte Carlo simulation) 比較前述之機器學習與模型平均方法對於安慰劑ATE/ATT的估計表現，實證結果發現相對於單一個別機器學習模型，Combined與EQ之模型平均方法皆可顯著降低安慰劑ATE/ATT的估計偏誤與變異數，進而改善以均方差（mean squared error) 衡量的整體估計誤差。此外，Combined的估計表現較EQ更為穩定，且隨著模擬樣本數的增加，其相對改善幅度益見顯著。此外，我們於模擬後得出的另一個實證發現在於：相較於以準確率 (accuracy) 為機器學習調參的績效指標，以組間變量平衡、共同支撐及重疊區相關指標進行調參可產生較為穩定之IT政策效果估計值。
    有關實施IT政策對於降低通膨率的效果估計，我們的主要結果係利用1980 - 2007年60個開發中國家，應用兩種模型平均法所得之降低通膨率的政策效果估計值分別為1.425~1.764百分點（ATE）與3.177~3.909百分點（ATT），後者具1%統計顯著性。我們進一步比較早期與後期（以1999年為界）實施IT的政策效果，結果顯示在前期實施IT政策更能有效降低通膨率，其ATE與ATT估計值分別為0.609~1.006百分點與1.994~2.291百分點，而後期的對應估計值為0.576~0.880百分點與0.882~2.030百分點，此一發現與Bhalla et al. (2023)的實證結果一致。最後，我們變動不同IT認定標準、是否剔除惡性通膨國家、不同資料起始時間、增加變數維度等，前述的實證結果皆具穩健性。
    This study applies machine learning (ML) and model averaging methods to estimating the treatment effects of adopting inflation targeting (IT) on lowering inflation levels. We use various machine learning methods to estimate the propensity score---the conditional probability of adopting an IT policy. To reflect our focus on estimating causal effects rather than predicting, as targets of tuning ML hyperparameters, we include covariate balance, common support, and other indicators that are relevant to identification conditions of treatment effects. We then use the inverse probability weighting approach to estimate average treatment effects (ATE) and average treatment effects on the treated (ATT).
    Another main focus of this study is to use the idea of model averaging to combine the ATE/ATT estimates obtained from our ML methods. Our model averaging methods are simple and easy to implement. Specifically, we consider simple averaging with equal weights (EQ) across models and weighted averaging (Combined) with data-driven weights, which can simply be estimated by least squares.
    To compare the performance of the ML and model averaging methods, we conduct an empirical Monte Carlo simulation study to estimate the placebo ATE/ATT. Our simulation results reveal that our Combined and EQ methods tend to improve upon the ML approaches in terms of mean-squared errors of the placebo ATE/ATT estimates. Moreover, Combined outperforms EQ in most cases, with more noticeable improvements as the simulation sample size increases. We also find that relative to using accuracy as a ML tuning target, using the aforementioned ATE/ATT identification-related targets can yield more stable ATE/ATT estimates.
    Lastly, we empirically apply our proposed methods to estimate the ATE/ATT of adopting the IT policy based on a data set consisting of 60 developing countries from 1980 to 2007. Our main results from the proposed Combined and EQ methods show that IT-adoption reduces inflation rates by 1.425-1.764 percentage points (ATE) and 3.177-3.909 percentage points (ATT), with the latter statistically significant at the 1% level. Our results are robust across changing identification standards of IT adoption, whether to exclude countries with hyperinflation, using different data sets, and increasing the dimension of confounding factors.
    We also find that early IT adopters (pre-1999) enjoyed more success in reducing inflation rates than late adopters, which is consistent with the finding of Bhalla et al. (2023). Specifically, our respective ATE and ATT estimates for early IT-adoption are 0.609-1.006 and 1.994-2.291 percentage points, while the corresponding estimates for late IT-adoption are 0.576-0.880 and 0.882-2.030 percentage points.
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    Description: 碩士
    國立政治大學
    經濟學系
    110258010
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0110258010
    Data Type: thesis
    Appears in Collections:[Department of Economics] Theses

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