Open AccessDP 0253 - Are Fiscal VAR’s Non-Fundamentalness Easily Reversible Through the Addition of Informative Variables?
Author(s) -
Christian Vonbun,
Elcyon Caiado Rocha Lima
Publication year - 2021
Publication title -
texto para discussão
Language(s) - English
Resource type - Journals
ISSN - 1415-4765
DOI - 10.38116/dp253
Subject(s) - dynamic stochastic general equilibrium , autoregressive model , econometrics , vector autoregression , representation (politics) , lag , measure (data warehouse) , economics , computer science , macroeconomics , monetary policy , computer network , database , politics , political science , law
The VAR/SVAR (Vector Autoregressive and Structural Vector Autoregressive) models are the cornerstone of the contemporaneous empirical macroeconomic research, in particular for being able to measure the impact of fiscal policy shocks. They may be employed as atheoretical models, as well as a mean to support the estimation and testing of DSGE (Dynamic Stochastic General Equilibrium) models – the main theoretical tool for modern macroeconomics. Nevertheless, VAR models may be subject to pathologies, such as the non-fundamentalness. It is capable of biasing the estimates in any direction or intensity, and it consists of the non-invertibility of the MA (Moving Average) representation on the positive powers of the lag operator. This is associated with the insufficiency of the econometrician’s data to estimate the model’s correct parameters or with model misspecification. This study is the first to employ the latest and most efficient tests for non-fundamentalness on fiscal data for the USA: the Forni and Gambetti’s (2014) and Canova and Sahneh (2018) tests. The data and model were found to be non-fundamental.