Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty
提出一种非参数多元时间序列模型（GP-VAR），用高斯过程先验刻画条件均值，结合随机波动处理异方差，通过高效MCMC估计，用于分析宏观经济不确定性及其传导机制的时变性和非对称性。
We develop a nonparametric multivariate time series model that remains agnostic on the precise relationship between a (possibly) large set of macroeconomic time series and their lagged values. The main building block of our model is a Gaussian process prior on the functional relationship that determines the conditional mean of the model, hence, the name of Gaussian process vector autoregression (GP-VAR). A flexible stochastic volatility specification is used to provide additional flexibility and control for heteroscedasticity. Markov chain Monte Carlo (MCMC) estimation is carried out through an efficient and scalable algorithm which can handle large models. The GP-VAR is used to analyze the effects of macroeconomic uncertainty, with a particular emphasis on time variation and asymmetries in the transmission mechanisms.