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Found 15124 results for '"Vector Autoregression"', showing 1-10
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  1. Uhlig, H.F.H.V.S. (1996): Bayesian Vector Autoregressions with Stochastic Volatility
    This paper proposes a Bayesian approach to a vector autoregression with stochastic volatility, where the multiplicative evolution of the precision matrix is driven by a multivariate beta variate. ... Estimation of the autoregressive parameters requires numerical methods: an importance-sampling-based approach is explained here.
    RePEc:tiu:tiucen:4fd55395-6830-46a2-9d18-efb61c0ed3f3  Save to MyIDEAS
  2. David E. Runkle (1987): Vector autoregressions and reality
    The statistical significance of variance decompositions and impulse response functions for unrestricted vector autoregressions is questionable.
    RePEc:fip:fedmsr:107  Save to MyIDEAS
  3. Helmut Luetkepohl (2007): Econometric Analysis with Vector Autoregressive Models
    Vector autoregressive (VAR) models for stationary and integrated variables are reviewed. ... For integrated and cointegrated variables it is argued that vector error correction models offer a particularly convenient parameterization both for model specification and for using the models for economic analysis.
    RePEc:eui:euiwps:eco2007/11  Save to MyIDEAS
  4. John C. Robertson & Ellis W. Tallman (1999): Vector autoregressions: forecasting and reality
    . ; The authors of this article describe a particular model-based forecasting approach, a vector autoregression comprising six U.S. macroeconomic variables.
    RePEc:fip:fedaer:y:1999:i:q1:p:4-18:n:v.84no.1  Save to MyIDEAS
  5. Pierre-Daniel G. Sarte (1997): On the identification of structural vector autoregressions
    No abstract is available for this item.
    RePEc:fip:fedreq:y:1997:i:sum:p:45-68  Save to MyIDEAS
  6. John W. Keating (1992): Structural approaches to vector autoregressions
    No abstract is available for this item.
    RePEc:fip:fedlrv:y:1992:i:sep:p:37-57  Save to MyIDEAS
  7. Renato E. Reside, Jr. (2001): Two Decades of Vector Autoregression (VAR) Modeling
    A vector autoregression (VAR) is defined as a vector of endogenous variables regressed against its own lags. ... The following is a survey of the literature on vector autoregressions (VARs) in the last twenty years since it was first used for policy analysis by Christopher Sims [1980].
    RePEc:phs:prejrn:v:38:y:2001:i:2:p:83-121  Save to MyIDEAS
  8. Juan F. Rubio-Ramirez & Daniel F. Waggoner & Tao Zha (2008): Structural vector autoregressions: theory of identification and algorithms for inference
    Structural vector autoregressions (SVARs) are widely used for policy analysis and to provide stylized facts for dynamic general equilibrium models.
    RePEc:fip:fedawp:2008-18  Save to MyIDEAS
  9. Lütkepohl, Helmut & Schlaak, Thore (2021): Heteroskedastic Proxy Vector Autoregressions
    In proxy vector autoregressive models, the structural shocks of interest are identified by an instrument.
    RePEc:zbw:vfsc21:242399  Save to MyIDEAS
  10. Bannikov, V. (2006): Vector Autoregression and Error Correction Models
    Here we are presenting or, to be more exact, describing how to analyze the vector autoregression (VAR) and vector error correction (VEC) models by using the EViews (Version 5) package.
    RePEc:ris:apltrx:0159  Save to MyIDEAS
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