全球智库信息集成服务系统(G2TT): DP17123 Forecasting with panel data: estimation uncertainty versus parameter heterogeneity

G2TT

来源类型	Discussion paper
规范类型	论文
来源ID	DP17123
	DP17123 Forecasting with panel data: estimation uncertainty versus parameter heterogeneity
	M. Hashem Pesaran; Andreas Pick; Henry Allan Timmermann
发表日期	2022-03-19
出版年	2022
语种	英语
摘要	We develop novel forecasting methods for panel data with heterogeneous parameters and examine them together with existing approaches. We conduct a systematic comparison of their predictive accuracy in settings with different cross-sectional (N) and time (T) dimensions and varying degrees of parameter heterogeneity. We investigate conditions under which panel forecasting methods can perform better than forecasts based on individual estimates and demonstrate how gains in predictive accuracy depend on the degree of parameter heterogeneity, whether heterogeneity is correlated with the regressors, the goodness of fit of the model, and, particularly, the time dimension of the data set. We propose optimal combination weights for forecasts based on pooled and individual estimates and develop a novel forecast poolability test that can be used as a pretesting tool. Through a set of Monte Carlo simulations and three empirical applications to house prices, CPI inflation, and stock returns, we show that no single forecasting approach dominates uniformly. However, forecast combination and shrinkage methods provide better overall forecasting performance and offer more attractive risk profiles compared to individual, pooled, and random effects methods.
主题	Financial Economics
关键词	Panel data Heterogeneity Forecast evaluation Forecast combination Shrinkage Pooling
URL	https://cepr.org/publications/dp17123
来源智库	Centre for Economic Policy Research (United Kingdom)
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/546100
推荐引用方式 GB/T 7714	M. Hashem Pesaran,Andreas Pick,Henry Allan Timmermann. DP17123 Forecasting with panel data: estimation uncertainty versus parameter heterogeneity. 2022.