Vector autoregressive models: A Gini approach

Vector Autoregressive Models

Estimating Structured Vector Autoregressive Models

While considerable advances have been made in estimating high-dimensional structured models from independent data using Lasso-type models, limited progress has been made for settings when the samples are dependent. We consider estimating structured VAR (vector auto-regressive model), where the structure can be captured by any suitable norm, e.g., Lasso, group Lasso, order weighted Lasso, etc. I...

Factor vector autoregressive estimation: a new approach

In this paper a new approach to factor vector autoregressive estimation, based on Stock and Watson (Implications of dynamic factor models for VAR analysis, NBER Working Paper, no. 11467, 2005), is introduced. In addition to sharing all the relevant features of the Stock–Watson approach, in its static formulation, the proposed method has the advantage of allowing for a more clear-cut interpretat...

The Effect of Macroeconomic Shocks on Inflation in Iran: A Vector Autoregressive Approach with Dynamic Parameters

Given the effects of inflation on the decline of household welfare and its impact on production and investment, identifying the factors affecting it in order to adjust inflation and achieve price stability is necessary. Therefore, using the TVP-FAVAR model, which differentiates the fluctuations in factors affecting inflation, we try to identify the effects of different shocks such as liquidity,...

Model Uncertainty in Panel Vector Autoregressive Models

We develop methods for Bayesian model averaging (BMA) or selection (BMS) in Panel Vector Autoregressions (PVARs). Our approach allows us to select between or average over all possible combinations of restricted PVARs where the restrictions involve interdependencies between and heterogeneities across cross-sectional units. The resulting BMA framework can find a parsimonious PVAR specification, t...