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Identification of Mixed Causal-Noncausal Models : How Fat Should We Go?
A.W. Hecq
,
L.M. Lieb
, J.M.A. Telg
Quantitative Economics
Macro, International & Labour Economics
Research output
:
Working paper / Preprint
›
Working paper
486
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Keyphrases
Autoregressive Process
100%
LAD Estimator
100%
Noncausal Models
100%
Davis
100%
Belgium
50%
Error Term
50%
Solar Panel
50%
Error Distribution
50%
Financial Time Series
50%
T-distribution
50%
Knight
50%
Laplacian
50%
Nonlinear Features
50%
Gaussianity
50%
Noncausal Autoregression
50%
Bubble Phenomena
50%
Error Process
50%
Asymptotic Normality
50%
Infinite Variance
50%
Realized Volatility
50%
Tail Distribution
50%
Equity Indices
50%
INIS
errors
100%
fats
100%
distribution
100%
demand
33%
simulation
33%
performance
33%
panels
33%
nonlinear problems
33%
belgium
33%
capture
33%
bubbles
33%
asymptotic solutions
33%
volatility
33%
laplacian
33%
Mathematics
Autoregressive Model
100%
Autoregression
50%
Variance
50%
Error Term
50%
Asymptotic Normality
50%
Error Distribution
50%
Process Error
50%
Laplace Operator
50%
Empirical Illustration
50%
Economics, Econometrics and Finance
Volatility
100%
Time Series
100%
Autoregression
100%