Structural Vector Autoregressive Models

2020 ◽  
Author(s):  
Luca Gambetti
Keyword(s):  
Linear Functions ◽  
Impulse Response Functions ◽  
Vector Autoregressions ◽  
Structural Shocks ◽  
Vector Autoregressive ◽  
Varying Coefficients ◽  
Macroeconomic Analysis ◽  
Long Run ◽  
Time Varying Coefficients ◽  
Structural Vector

Structural vector autoregressions (SVARs) represent a prominent class of time series models used for macroeconomic analysis. The model consists of a set of multivariate linear autoregressive equations characterizing the joint dynamics of economic variables. The residuals of these equations are combinations of the underlying structural economic shocks, assumed to be orthogonal to each other. Using a minimal set of restrictions, these relations can be estimated—the so-called shock identification—and the variables can be expressed as linear functions of current and past structural shocks. The coefficients of these equations, called impulse response functions, represent the dynamic response of model variables to shocks. Several ways of identifying structural shocks have been proposed in the literature: short-run restrictions, long-run restrictions, and sign restrictions, to mention a few. SVAR models have been extensively employed to study the transmission mechanisms of macroeconomic shocks and test economic theories. Special attention has been paid to monetary and fiscal policy shocks as well as other nonpolicy shocks like technology and financial shocks. In recent years, many advances have been made both in terms of theory and empirical strategies. Several works have contributed to extend the standard model in order to incorporate new features like large information sets, nonlinearities, and time-varying coefficients. New strategies to identify structural shocks have been designed, and new methods to do inference have been introduced.

scholarly journals Sources of economic fuctuations in France: A structural VAR model

2012 ◽  
Vol 1 (1) ◽  
pp. 66
Author(s):  
Nabil Ben Arfa
Keyword(s):  
Open Economy ◽  
Trade Balance ◽  
Dynamic Properties ◽  
Structural Var ◽  
Impulse Response Functions ◽  
External Shocks ◽  
Structural Shocks ◽  
Vector Autoregressive ◽  
French Economy ◽  
Long Run

This paper studies the economic fluctuations of an open economy such as the French economy. A system of variables containing output, price level, trade balance, real exchange rate and oil prices is analyzed by applying the structural vector autoregressive (SVAR) methodology initiated by Sims (1980). This set of variables allows to evaluate the main sources of impulses of the French economy fluctuations. The results show that five structural shocks are identified using the long-run constraints implemented by Blanchard and Quah (1989). From the SVAR dynamic properties, impulse response functions and variance decomposition, the French economy is shown to be particularly vulnerable to supply and oil price shocks, where these two shocks respectively contribute to 40% and 35% of the economic disturbance. France is also hit by important external shocks which damage its trade balance position. Finally, it is found that shocks related to economic policy (demand shocks) have a quite limited impact on the economic activity.

Multivariate Markov-switching score-driven models: an application to the global crude oil market

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Szabolcs Blazsek ◽  
Alvaro Escribano ◽  
Adrian Licht
Keyword(s):  
Crude Oil ◽  
Moving Average ◽  
Local Level ◽  
Markov Switching ◽  
Impulse Response Functions ◽  
Common Trends ◽  
Vector Autoregressive ◽  
Oil Market ◽  
Long Run ◽  
Special Cases

Abstract A new class of multivariate nonlinear quasi-vector autoregressive (QVAR) models is introduced. It is a Markov switching score-driven model with stochastic seasonality for the multivariate t-distribution (MS-Seasonal-t-QVAR). As an extension, we allow for the possibility of having common-trends and nonlinear co-integration. Score-driven nonlinear updates of local level and seasonality are used, which are robust to outliers within each regime. We show that VAR integrated moving average (VARIMA) type filters are special cases of QVAR filters. Using exclusion, sign, and elasticity identification restrictions in MS-Seasonal-t-QVAR with common-trends, we provide short-run and long-run impulse response functions for the global crude oil market.

scholarly journals Choosing between identification schemes in noisy-news models

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Joshua C. C. Chan ◽  
Eric Eisenstat ◽  
Gary Koop
Keyword(s):  
Bayesian Methods ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Structural Shocks ◽  
Vector Autoregressive ◽  
Identification Scheme ◽  
Identification Schemes ◽  
The One ◽  
Small Models ◽  
Structural Vector

AbstractThis paper is about identifying structural shocks in noisy-news models using structural vector autoregressive moving average (SVARMA) models. We develop a new identification scheme and efficient Bayesian methods for estimating the resulting SVARMA. We discuss how our identification scheme differs from the one which is used in existing theoretical and empirical models. Our main contributions lie in the development of methods for choosing between identification schemes. We estimate specifications with up to 20 variables using US macroeconomic data. We find that our identification scheme is preferred by the data, particularly as the size of the system is increased and that noise shocks generally play a negligible role. However, small models may overstate the importance of noise shocks.

scholarly journals Co-integration with score-driven models: an application to US real GDP growth, US inflation rate, and effective federal funds rate

2021 ◽  
pp. 1-21
Author(s):  
Szabolcs Blazsek ◽  
Alvaro Escribano ◽  
Adrian Licht
Keyword(s):  
Inflation Rate ◽  
Predictive Accuracy ◽  
Moving Average ◽  
Impulse Response Functions ◽  
Federal Funds ◽  
Federal Funds Rate ◽  
Vector Autoregressive ◽  
Autoregressive Moving Average Model ◽  
Long Run ◽  
The Us

Abstract Nonlinear co-integration is studied for score-driven models, using a new multivariate dynamic conditional score/generalized autoregressive score model. The model is named t-QVARMA (quasi-vector autoregressive moving average model), which is a location model for the multivariate t-distribution. In t-QVARMA, I(0) and co-integrated I(1) components of the dependent variables are included. For t-QVARMA, the conditions of the maximum likelihood estimator and impulse response functions (IRFs) are presented. A limiting special case of t-QVARMA, named Gaussian-QVARMA, is a Gaussian-VARMA specification with I(0) and I(1) components. As an empirical application, the US real gross domestic product growth, US inflation rate, and effective federal funds rate are studied for the period of 1954 Q3 to 2020 Q2. Statistical performance and predictive accuracy of t-QVARMA are superior to those of Gaussian-VAR. Estimates of the short-run IRF, long-run IRF, and total IRF impacts for the US data are reported.

scholarly journals The Long-Run Superneutrality of Money Revised: the Extended European Evidence

2016 ◽  
Vol 16 (3) ◽  
pp. 187-203 ◽  
Author(s):  
Oleg Deev ◽  
Martin Hodula
Keyword(s):  
Endogenous Growth ◽  
Output Growth ◽  
Real Output ◽  
Vector Autoregressive ◽  
Additional Hypothesis ◽  
Long Run ◽  
Large Panel ◽  
European Economies ◽  
Structural Vector

Abstract This article investigates the validity of the money superneutrality concept for the large panel of European economies. While focusing exclusively on endogenous growth theories including the Mundell-Tobin effect, we examine the long-run response of real output to a permanent inflation shock in every studied country using a structural vector autoregressive framework. For the majority of countries in our sample, the longrun superneutrality concept is confirmed since the original increase/decrease in output growth fades in time. We also test the additional hypothesis of whether the group of countries with smaller in-sample inflation mean forms the exception to the long-run money superneutrality. As the result, modern economies might be better described from the viewpoint of Sidrauski.

VALIDATING DSGE MODELS WITH SVARS AND HIGH-DIMENSIONAL DYNAMIC FACTOR MODELS

2021 ◽  
pp. 1-19
Author(s):  
Marco Lippi
Keyword(s):  
Measurement Errors ◽  
Factor Models ◽  
High Dimensional ◽  
Dynamic Factor ◽  
Dsge Models ◽  
Impulse Response Functions ◽  
Vector Autoregressions ◽  
Dynamic Factor Models ◽  
Structural Shocks ◽  
Dynamic Stochastic

A popular validation procedure for Dynamic Stochastic General Equilibrium (DSGE) models consists in comparing the structural shocks and impulse-response functions obtained by estimation-calibration of the DSGE with those obtained in an Structural Vector Autoregressions (SVAR) identified by means of some of the DSGE restrictions. I show that this practice can be seriously misleading when the variables used in the SVAR contain measurement errors. If this is the case, for generic values of the parameters of the DSGE, the shocks estimated in the SVAR are not “made of” the corresponding structural shocks plus measurement error. Rather, each of the SVAR shocks is contaminated by noncorresponding structural shocks. We argue that High-Dimensional Dynamic Factor Models are free from this drawback and are the natural model to use in validation procedures for DSGEs.

Structural VARs, deterministic and stochastic trends: how much detrending matters for shock identification

2015 ◽  
Vol 0 (0) ◽  
Author(s):  
Varang Wiriyawit ◽  
Benjamin Wong
Keyword(s):  
Bayesian Information Criterion ◽  
Unit Roots ◽  
Information Criterion ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Vector Autoregressions ◽  
Common Information ◽  
Stochastic Trends ◽  
Structural Vars ◽  
Structural Vector

AbstractDetrending within structural vector autoregressions (SVAR) is directly linked to the shock identification. We investigate the consequences of trend misspecification in an SVAR using both standard real business cycle models and bi-variate SVARs as data generating processes. Our bias decomposition reveals biases arising directly from trend misspecification are not trivial when compared to other widely studied misspecifications. Misspecifying the trend also distorts impulse response functions of even the correctly detrended variable within the SVAR system. Pretesting for unit roots mitigates trend misspecification to some extent. We also find that while practitioners can specify high lag order VARs to mitigate trend misspecification, relying on common information criterion such as the Akaike information criterion (AIC) or Bayesian information criterion (BIC) may choose a lag order that is too low.

DOES FEAR LEAD TO RECESSIONS?

2015 ◽  
Vol 20 (5) ◽  
pp. 1247-1263 ◽  
Author(s):  
Shiu-Sheng Chen ◽  
Yu-Hsi Chou
Keyword(s):  
Transition Probability ◽  
Autoregressive Models ◽  
Consumer Confidence ◽  
Time Varying ◽  
Structural Shocks ◽  
Vector Autoregressive Models ◽  
Vector Autoregressive ◽  
Economic Downturns ◽  
Economic Recessions ◽  
Structural Vector

This paper investigates the link between consumer pessimism and U.S. economic recessions empirically. First we use structural vector autoregressive models to identify negative structural shocks to consumer confidence, which are used as a proxy for recession fear. We then apply probit models and time-varying-transition-probability Markov-switching autoregressive models to investigate how the lack of consumer confidence affects the probability of recession. We find that recession fear leads to a higher probability of economic downturns. Furthermore, strong evidence exists that an increase in market pessimism may push the economy from an expansion state to a recession state. We also find weaker evidence suggesting that a lack of consumer confidence may trap the economy in the depressed regime longer. We conclude that a lack of confidence can push the economy into recession.

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