Results 271 to 280 of about 1,911,183 (308)

Restrictions search for panel VARs

SSRN Electronic Journal, 2016
As panel vector autoregressive (PVAR) models can include several countries and variables in one system, they are well suited for global spillover analyses. However, PVARs require restrictions to ensure the feasibility of the estimation. The present paper uses a selection prior for a data-based restriction search.
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Drugs and Violence in Afghanistan: A Panel Var With Unobserved Common Factor Analysis [PDF]

Defence and Peace Economics, 2013
This paper addresses the relationship between the level of violence and the opium market in Afghanistan’s provinces. We first provide an overview of the nature and extent of the Afghan drug trafficking. This is followed by a vector autoregressive analysis of the nexus opium-insurgency activities using monthly time-series data on opium prices and the ...
Vincenzo Bove, Leandro Elia
exaly   +5 more sources

Fiscal and monetary policies in the BRICS: A panel VAR approach

Economic Modelling, 2016
Abstract Using a Panel Vector Auto-Regressive (PVAR) model, we assess the macroeconomic impact of fiscal policy and monetary policy shocks for five key emerging market economies—Brazil, Russia, India, China and South Africa (BRICS). We show that monetary contractions lead to a fall in real economic activity and tighten liquidity market conditions ...
Jawadi, F, Mallick, SK, Sousa, RM
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Factor Augmented Vector Autoregressions, Panel VARs, and Global VARs

2019
This chapter provides a thorough introduction to panel, global, and factor augmented vector autoregressive models. These models are typically used to capture interactions across units (i.e., countries) and variable types. Since including a large number of countries and/or variables increases the dimension of the models, all three approaches aim to ...
Feldkircher, Martin, Huber, Florian, Pfarrhofer, Michael   +2 more
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Improved GMM estimation of panel VAR models

Computational Statistics & Data Analysis, 2013
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Wealth Effects and Consumption: A Panel VAR Approach

International Review of Applied Economics, 2014
We provide new evidence on the comparison between the stock and housing wealth effects on consumption. Using a panel VAR approach applied to OECD data, we find evidence that the stock market wealth effect is generally the larger. However, with regard to the evolution of asset wealth effects over time, our findings show that the housing wealth effect ...
Xin Shen, Mark J. Holmes, Steven Lim
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Variational Inference for Bayesian Panel VAR Models

SSRN Electronic Journal
We study the application of approximate mean field variational inference algorithms to Bayesian panel VAR models in which an exchangeable prior is placed on the dynamic parameters and the residuals follow either a Gaussian or a Student-t distribution.
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VAR Analysis of Heterogeneous Panel Data

Vector-autoregressive (VAR) analysis is a central instrument of empirical macroeconomics and the starting point for numerous extensions of multivariate time series analysis. For example, structural VAR models allow to identify causal relationships in dynamic systems and thus to quantify the effects of political interventions.
Empting, Lennart Friedrich Thomas, Herwartz, Helmut, Herwartz, Helmut, Georg-August-Universität Göttingen, Herwartz, Helmut, Kneib, Thomas, Maxand, Simone   +6 more
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Panel VAR models with spatial dependence [PDF]

, 2002
I consider a panel vector-autoregressive model with cross-sectional dependence of the disturbances characterized by a spatial autoregressive process. I propose a three-step estimation procedure. Its first step is an instrumental variable estimation that ignores the spatial correlation.
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A New IV Estimator of a Panel VAR(p) Model

We propose a novel dynamic panel estimator. Different from the commonly used difference and system GMM, our proposed estimator requires only one of the crosssectional dimension (N) or the time dimension (T) to grow large to be asymptotically unbiased.
Adrian Mehic, Marcus Nordström
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