Publication Date: 2022/09/17
Abstract: One of the most critical roles of macroeconometricians is to provide advice to policymakers by describing and summarizing macroeconomic data, making macroeconomic forecasts, estimating how much stakeholders understand concerning the macroeconomy's underlying structure. Various methods were used in to perform these activities in the 1900s. The most notable ones were policy analysis, forecasting and inference structure. Researchers investigated a wide scale of these techniques, from vast frameworks with numerous complex equations to simple one-variable time series models and single-equation interactions models. However, none of these methods proven to be particularly reliable after the macroeconomic instability of the 1970s. Christopher Sims (1980) introduced a novel macroeconometric paradigm, vector autoregressions (VARs), two decades ago, and it was immediately well received. As the name suggests, the current value of a single variable is explained by the residual of that same variable, which is known as a univariate autoregression. To put it another way, it constitutes a linear regression of n-equation and variable in which each component is described based on its individual lagged numbers, as well as present and previous values of the rest of the n - 1 additional variables. The VAR statistical toolset proved to be effective for use and analysis and it provided a systematic technique to detect dynamics in many time series. In a series of significant early works, Sims (1980) and others claimed that VAR had the promise of providing a consistent and plausible model for policy analysis, structural inferences, and data description.
Keywords: vector autoregressions; univariate autoregression; macro-econometric analysis.
DOI: https://doi.org/10.5281/zenodo.7087975
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT22JUL1527.pdf
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