Inference in Bayesian additive vector autoregressive tree models

نویسندگان

چکیده

Vector autoregressive (VAR) models assume linearity between the endogenous variables and their lags. This assumption might be overly restrictive could have a deleterious impact on forecasting accuracy. As solution we propose combining VAR with Bayesian additive regression tree (BART) models. The resulting vector (BAVART) model is capable of capturing arbitrary nonlinear relations covariates without much input from researcher. Since controlling for heteroscedasticity key producing precise density forecasts, our allows stochastic volatility in errors. We apply to two datasets. first application shows that BAVART yields highly competitive forecasts U.S. term structure interest rates. In second estimate using moderately sized Eurozone dataset investigate dynamic effects uncertainty economy.

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Bayesian Estimates for Vector - Autoregressive Models

This paper examines frequentist risks of Bayesian estimates of VAR regression coefficient and error covariance matrices under competing loss functions, under a variety of non-informative priors, and in the normal and Student-t models. Simulation results show that for the regression coefficient matrix an asymmetric LINEX estimator does better overall than the posterior mean. For the error covari...

متن کامل

Statistical Inference in Autoregressive Models with Non-negative Residuals

Normal residual is one of the usual assumptions of autoregressive models but in practice sometimes we are faced with non-negative residuals case. In this paper we consider some autoregressive models with non-negative residuals as competing models and we have derived the maximum likelihood estimators of parameters based on the modified approach and EM algorithm for the competing models. Also,...

متن کامل

Single-Index Additive Vector Autoregressive Time Series Models

We study a new class of nonlinear autoregressive models for vector time series, where the current vector depends on single-indexes defined on the past lags and the effects of different lags have an additive form. A sufficient condition is provided for stationarity of such models. We also study estimation of the proposed model using P-splines, hypothesis testing, asymptotics, selection of the or...

متن کامل

Comparison of Neural Network Models, Vector Auto Regression (VAR), Bayesian Vector-Autoregressive (BVAR), Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) Process and Time Series in Forecasting Inflation in ‎Iran‎

‎This paper has two aims. The first is forecasting inflation in Iran using Macroeconomic variables data in Iran (Inflation rate, liquidity, GDP, prices of imported goods and exchange rates) , and the second is comparing the performance of forecasting vector auto regression (VAR), Bayesian Vector-Autoregressive (BVAR), GARCH, time series and neural network models by which Iran's inflation is for...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: The Annals of Applied Statistics

سال: 2022

ISSN: ['1941-7330', '1932-6157']

DOI: https://doi.org/10.1214/21-aoas1488