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.

A Comparison of Approaches to Select the Informativeness of Priors in BVARs

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jan Prüser ◽  
Christoph Hanck
Keyword(s):  
Impulse Response ◽  
Prior Information ◽  
Hierarchical Modeling ◽  
Small Samples ◽  
Model Parameters ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Vector Autoregressions ◽  
The Rich ◽  
Bayesian Vars

Abstract Vector autoregressions (VARs) are richly parameterized time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, in small samples the rich parametrization of VAR models may come at the cost of overfitting the data, possibly leading to imprecise inference for key quantities of interest such as impulse response functions (IRFs). Bayesian VARs (BVARs) can use prior information to shrink the model parameters, potentially avoiding such overfitting. We provide a simulation study to compare, in terms of the frequentist properties of the estimates of the IRFs, useful strategies to select the informativeness of the prior. The study reveals that prior information may help to obtain more precise estimates of impulse response functions than classical OLS-estimated VARs and more accurate coverage rates of error bands in small samples. Strategies based on selecting the prior hyperparameters of the BVAR building on empirical or hierarchical modeling perform particularly well.

A Study on Akaike’s Bayesian Information Criterion in Wave Estimation

2011 ◽  
Author(s):  
Toshio Iseki
Keyword(s):  
Bayesian Modeling ◽  
Bayesian Information Criterion ◽  
Information Criterion ◽  
Response Functions ◽  
Local Minima ◽  
Normal Response ◽  
Wave Parameters ◽  
Optimum Estimates ◽  
Response Amplitude Operators ◽  
The Relationship

A feasibility study of Bayesian wave estimation was carried out to investigate the relationship between the minimum Akaike’s Bayesian information criterion (ABIC) and the estimated wave parameters. The ship response functions, which were used for the Bayesian wave estimation together with the ship motion cross spectra, were simply modified and compared with the normal response functions in connection with the accuracy of estimated wave parameters. Moreover, the concept of the ABIC surfaces was introduced to investigate the optimum estimates from the stochastic viewpoint and the physical viewpoint. As the result, it was revealed that the minimum ABIC did not always provide the best estimates from the viewpoint of wave estimation and the simply modified response functions could reduce the estimating errors in some cases. The reasons were considered that the estimating error at the sharp peak of response amplitude operators was closely related to existence of the local minima of the ABIC surface and the simply modified response functions had some effects to make the ABIC surface smoother. It is pointed out as the conclusion of this report that any estimating errors of the ship response functions were not considered in the Bayesian modeling.

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.

THE REGIONAL ASYMMETRIC RESPONSES TO CENTRAL BANK’S MONETARY POLICY IN PAKISTAN

2017 ◽  
Vol 65 (02) ◽  
pp. 351-364
Author(s):  
NASEEM FARAZ ◽  
ZAINAB IFTIKHAR
Keyword(s):  
Monetary Policy ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Structural Vector Autoregression ◽  
Monetary Policy Shocks ◽  
Limited Evidence ◽  
Svar Model ◽  
Policy Shocks ◽  
National Output ◽  
Structural Vector

Literature on differential impacts of monetary policy across regions discusses several factors which may be responsible for asymmetrical effects of monetary policy. As far as Pakistan is concerned, limited evidence is available for both mechanism and impact of monetary policy. In this study, we examine asymmetries in responses of real output of provinces to central bank’s monetary policy in Pakistan. We also attempt to explore the potential sources of these asymmetries. The Structural Vector Autoregression (SVAR) model is employed to examine each province’s response to unanticipated monetary policy shocks. The generalized impulse response functions from SVAR reveal that monetary policy has varied effects across the provinces. In two regions — Punjab and Sindh — monetary policy shocks cause variations in provincial outputs in similar ways. These responses are also comparable to the response of national output to changes in monetary policy but with considerable differences in magnitudes. While other provinces Khyber Pakhtunkhawa (KPK) and Balochistan show less sensitivity to unanticipated change in monetary policy. The less sensitive regions exhibit dissimilar responses both in timings and magnitudes. These dissimilarities in regional responses draw attention to devise an effective national monetary policy that might consider the cross-provincial differences in responses to central monetary policy in Pakistan.

Shocks, Information, and Structural VARs

2021 ◽  
Author(s):  
Luca Gambetti
Keyword(s):  
Small Scale ◽  
Implicit Assumption ◽  
Limited Information ◽  
Vector Autoregressions ◽  
Economic Shocks ◽  
New Techniques ◽  
Scale Models ◽  
Information Sets ◽  
Structural Vars ◽  
Structural Vector

Structural Vector Autoregressions (SVARs) have become one of the most popular tools to measure the effects of structural economic shocks. Several new techniques to “identify” economic shocks have been proposed in the literature in the last decades. Identification hinges on the implicit assumption that economic shocks are retrievable from the data. In other words, the data contain enough information to correctly estimate the shocks. SVAR models, however, are small-scale models, only a small number of variables can be handled, and this feature can forcefully limit the amount of information that variables can convey. Narrow information sets present problems for identification, but some theoretical results and empirical procedures can test whether such information is sufficient to estimate economic shocks. Additionally, there are possible solutions to the problem of limited information, such as Factor Augmented VAR or dynamic rotations.

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