【主题】Forecasting Inflation Rates: A Large Panel Micro-Level Data Approach
【报告人】黄乃静（副教授，中央财经大学经济学院）
【时间】2024年4月23日周二14:00-15:30
【地点】经济学院701会议室
【语言】英文
【摘要】Economists and econometricians typically use aggregate macroeconomic and financial data for inflation prediction. However, aggregation often results in a loss of valuable information, diminishing key features like heterogeneity, interactions, nonlinearity, and structural breaks. We propose a novel microeconometric approach to inflation forecasting, making use of a large panel of individual stock prices. By employing machine learning algorithms, we can effectively exploit this micro-level information to achieve substantially more accurate inflation forecasts. Our findings highlight the advantages and potential of utilizing micro-level data for macro prediction, diverging from conventional macro-forecasting approaches that rely on aggregate data to forecast macro variables.
【报告人简介】黄乃静，中央财经大学经济学院长聘副教授，博士生导师，国民经济系主任。2015年取得美国波士顿学院经济学博士学位。以金融计量经济学和货币经济学为主要研究方向，先后在《管理科学学报》、《经济学动态》、《中国软科学》、Applied Economic等国内外学术刊物发表多篇论文。获得2017年厦门大学金融工程与量化金融学术会议最佳论文奖、2014年美国国家科学基金会旅行奖励（US National Science Foundation Travel Grant）等学术奖项。主持并参与多项国家自然科学基金，国家社科重大基金。
