Forecasting with panel data: Estimation uncertainty versus parameter heterogeneity
We provide a comprehensive examination of the predictive performance of panel forecasting methods based on individual, pooling, fixed effects, and empirical Bayes estimation, and propose optimal weights for forecast combination schemes. We consider linear panel data models, allowing for weakly exogenous regressors and correlated heterogeneity. We quantify the gains from exploiting panel data and demonstrate how forecasting performance depends on the degree of parameter heterogeneity, whether such heterogeneity is correlated with the regressors, the goodness of fit of the model, and the dimensions of the data. Monte Carlo simulations and empirical applications to house prices and CPI inflation show that empirical Bayes and forecast combination methods perform best overall and rarely produce the least accurate forecasts for individual series.
Code (1)
Similar Papers 제목 키워드 기반
Panel Data Estimation and Inference: Homogeneity versus Heterogeneity
In this paper, we define an underlying data generating process that allows for different magnitudes of cross-sectional dependence, along with time series autocorrelation. This is achieved via high-dimensional moving aver…
Time SeriesMemory, Roughness, and Information Persistence in Financial Markets: A Structural Approach to Volatility Forecasting
This paper studies the joint role of long-memory dynamics,rough-volatility behavior, and persistence-based forecasting features in equity volatility modeling. We combine semiparametric long-memory estimation, rough-volat…
Investigating Growth at Risk Using a Multi-country Non-parametric Quantile Factor Model
We develop a Bayesian non-parametric quantile panel regression model. Within each quantile, the response function is a convex combination of a linear model and a non-linear function, which we approximate using Bayesian A…
regressionEstimation of Cross-Sectional Dependence in Large Panels
Accurate estimation for extent of cross{sectional dependence in large panel data analysis is paramount to further statistical analysis on the data under study. Grouping more data with weak relations (cross{sectional depe…
Dimensionality ReductionTime SeriesTime Series AnalysisDeep Neural Network Estimation in Panel Data Models
In this paper we study neural networks and their approximating power in panel data models. We provide asymptotic guarantees on deep feed-forward neural network estimation of the conditional mean, building on the work of …
Time Series