1,723,678 research outputs found
Vector autoregressions as a tool for forecast evaluations
Full text linkIn his article, “Vector Autoregressions as a Tool for Forecast Evaluation,” Roy H. Webb proposes that VAR forecasts be used as a standard of comparison for other forecasts. He begins by explaining how conventional forecasting models are constructed and used, and summarizes a few common objections to these models. He then describes the VAR methodology and compares forecasts from a simple VAR model with those from a consulting firm that uses a conventional model and with a series of consensus forecasts. The VAR model holds its own in this competition; in fact, only the VAR model is able to predict the 1981-1982 recession one year before its occurrence.Forecasting
Christopher A. Sims and Vector Autoregressions
Full text linkAbstract Three decades ago, Christopher A. Sims suggested that vector autoregressions (VARs) are useful statistical devices for evaluating alternative macroeconomic models. His suggestion has stood the test of time well. In the early days, VARs played an important role in the evaluation of alternative models. They continue to play that role today
Structural vector autoregressions: theory of identification and algorithms for inference
Full text linkStructural vector autoregressions (SVARs) are widely used for policy analysis and to provide stylized facts for dynamic general equilibrium models. Yet there have been no workable rank conditions to ascertain whether an SVAR is globally identified. When identifying restrictions such as long-run restrictions are imposed on impulse responses, there have been no efficient algorithms for small-sample estimation and inference. To fill these important gaps in the literature, this paper makes four contributions. First, we establish general rank conditions for global identification of both overidentified and exactly identified models. Second, we show that these conditions can be checked as a simple matrix-filling exercise and that they apply to a wide class of identifying restrictions, including linear and certain nonlinear restrictions. Third, we establish a very simple rank condition for exactly identified models that amounts to a straightforward counting exercise. Fourth, we develop a number of efficient algorithms for small-sample estimation and inference.Vector autoregression
Monetary Transmission in Three Central European Economies: Evidence from Time-Varying Coefficient Vector Autoregressions
Get PDFThis paper studies the transmission of monetary policy to macroeconomic variables in three new EU Member States in comparison with that in the euro area with structural time-varying coefficient vector autoregressions. In line with the Lucas Critique reduced-form models like standard VARs are not invariant to changes in policy regimes. The countries we study have experienced changes in monetary policy regimes and went through substantial structural changes, which call for the use of a time-varying parameter analysis. Our results indicate that in the euro area the impact on output of a monetary shock have decreased in time while in the new member states of the EU both decreases and increases can be observed. At the last observation of our sample, the second quarter of 2008, monetary policy was the most powerful in Poland and comparable in strength to that in the euro area, the least powerful responses were observed in Hungary while the Czech Republic lied in between. We explain these results by the credibility of monetary policy, openness and the share of foreign currency loans.monetary transmission, time-varying coefficient vector autoregressions, Kalman-filter
A topological view on the identification of structural vector autoregressions
Full text linkThe notion of the group of orthogonal matrices acting on the set of all feasible identification schemes is used to characterize the identification problem arising in structural vector autoregressions. This approach presents several conceptual advantages. First, it provides a fundamental justification for the use of the normalized Haar measure as the natural uninformative prior. Second, it allows to derive the joint distribution of blocks of parameters defining an identification scheme. Finally, it provides a coherent way for studying perturbations of identification schemes becomes relevant, among other things, for the specification of vector autoregressions with time-varying covariance matrice
Structural Vector Autoregressions with Markov Switching
Full text linkIt is argued that in structural vector autoregressive (SVAR) analysis a Markov regime switching (MS) property can be exploited to identify shocks if the reduced form error covariance matrix varies across regimes. The model setup is formulated and discussed and it is shown how it can be used to test restrictions which are just-identifying in a standard structural vector autoregressive analysis. The approach is illustrated by two SVAR examples which have been reported in the literature and which have features which can be accommodated by the MS structure.Cointegration, Markov regime switching model, vector error correction model, structural vector autoregression, mixed normal distribution
Vector Autoregressions with Machine Learning
Full text linkI develop three new types of vector autoregressions that use supervised
machine learning models to estimate coefficients in place of ordinary least
squares. I use these models to estimate the effects of monetary policy on the
real economy. Overall, I find that the machine learning vector autoregressions
produce impulse responses that are well behaved and similar to their ordinary
least squares counterparts. In practice, the machine learning vector autoregressions
produce more conservative estimates than the traditional ordinary
least squares vector autoregressions. Additionally, I establish a simulation
scheme to compare the relative efficiency of impulse responses generated from
machine learning and ordinary least squares vector autoregressions. To calculate
condence intervals, I use a bias corrected bootstrapping method from
Politis and Romano (1994) called the stationary bootstrap. In future work, I
intend to compare these impulse responses using simulated data from Killian
and Kim (2011)
Stock Prices and Economic Fluctuations: A Markov Switching Structural Vector Autoregressive Analysis
Full text linkThe role of expectations for economic fluctuations has received considerable attention in recent business cycle analysis. We exploit Markov regime switching models to identify shocks in cointegrated structural vector autoregressions and investigate different identification schemes for bivariate systems comprising U.S. stock prices and total factor productivity. The former variable is viewed as re°ecting expectations of economic agents about future productivity. It is found that some previously used identification schemes can be rejected in our model setup. The results crucially depend on the measure used for total factor productivity.Cointegration, Markov regime switching model, vector error correction model, structural vector autoregression, mixed normal distribution
Bayesian vector autoregressions : applications
Full text linkBayesian vector autoregressions (BVARs) are standard multivariate autoregressive models routinely used in empirical macroeconomics and finance for structural analysis, forecasting, and scenario analysis in an ever-growing number of applications
Analysis of the use of vector autoregressions in economic forecasting
Full text linkUsing vector autoregressions is a promising direction in short-term economic forecasting. They do not simply model the relationship between different factors, but also model the time-distributed relationship of these factors. Vector autoregressions are suitable for modeling complex dynamic economic multifactor processes. The complexity of the problem of estimating coefficients, which increases with the dimensionality of vectors, prevents the widespread use of autoregressions in practice. Vector autoregressions in complex-valued form with the same dimensionality as the modeled vector contain a much smaller number of coefficients. This facilitates the estimation of the coefficients of vector autoregressions. Some problems requiring further investigation arise when using vector autoregressions in complex form. Among them is the problem of selecting the best model. The information criteria used for this purpose limit the variety of vector autoregressions, reducing them to elementary models. The study was supported by the Russian Science Foundation grant No. 23-28-01213, https://rscf.ru/project/23-28-01213
- …
