Econometrics and Models - рабочие бумаги

Econometric Time Series ForeComp: An R Package for Comparing Predictive Accuracy Using Fixed-Smoothing Asymptotics #forecast_comparison_Diebold–Mariano_test_fixed_b_asymptotics_fixed_m_asymptotics_long_run_variance_estimation_R_package We introduce ForeComp, an R package for comparing predictive accuracy using Diebold–Mariano type tests of equal predictive ability with standard and fixed-smoothing inference. The package provides a common interface for loss-differential based testing and includes Plot Tradeoff, a visual diagnostic for bandwidth sensitivity and the size–power tradeoff. We illustrate the toolkit with Survey of Professional Forecasters applications and Monte Carlo evidence on finite-sample performance. (author: Minchul Shin)

Econometric Time Series Forecasting the Covid Surge in Inflation The persistent surge in U.S. inflation that began in 2021 caught forecasters and policymakers by surprise. The 2021 inflation shocks were viewed as transitory, not persistent, leading to large forecast errors in late 2021 and 2022. This paper asks whether time series models – using only data on current and past inflation, but incorporating stochastic volatility and exhibiting time-varying persistence – performed better. Univariate models, using real-time data, did not. Multivariate models, incorporating sectoral inflation measures, did. (author: Mark W. Watson)

Econometric Time Series Misspecification-Robust Shrinkage and Selection for VAR Forecasts and IRFs #Forecasting_Local_projections_Model_misspecification_Shrinkage_estimation VARs are often estimated with Bayesian techniques to cope with model dimensionality. The posterior means define a class of shrinkage estimators, indexed by hyperparameters that determine the relative weight on maximum likelihood estimates and prior means. In a Bayesian setting, it is natural to choose these hyperparameters by maximizing the marginal data density. However, this is undesirable if the VAR is misspecified. In this paper, we derive asymptotically unbiased estimates of the multi-step forecasting risk and the impulse response estimation risk to determine hyperparameters in settings where the VAR is (potentially) misspecified. The proposed criteria can be used to jointly select the optimal shrinkage hyperparameter, VAR lag length, and to choose among different types of multi-step-ahead predictors; or among IRF estimates based on VARs and local projections. The selection approach is illustrated in a Monte Carlo study and an empirical application. (author: Schorfheide, Frank)