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Found 13 papers, 0 papers with code
Spectral identification and estimation of mixed causal-noncausal invertible-noninvertible models
This paper introduces new techniques for estimating, identifying and simulating mixed causal-noncausal invertible-noninvertible models.
Optimization of the Generalized Covariance Estimator in Noncausal Processes
When the number and type of nonlinear autocovariances included in the objective function of a GCov estimator is insufficient/inadequate, or the error density is too close to the Gaussian, identification issues can arise.
Inference in Non-stationary High-Dimensional VARs
We combine this lag augmentation with a post-double-selection procedure in which a set of initial penalized regressions is performed to select the relevant variables for both the Granger causing and caused variables.
Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions
Reverse Unrestricted MIxed DAta Sampling (RU-MIDAS) regressions are used to model high-frequency responses by means of low-frequency variables.
Spectral estimation for mixed causal-noncausal autoregressive models
This paper investigates new ways of estimating and identifying causal, noncausal, and mixed causal-noncausal autoregressive models driven by a non-Gaussian error sequence.
Detecting common bubbles in multivariate mixed causal-noncausal models
This paper proposes methods to investigate whether the bubble patterns observed in individual series are common to various series.
Is climate change time reversible?
This paper proposes strategies to detect time reversibility in stationary stochastic processes by using the properties of mixed causal and noncausal models.
A short term credibility index for central banks under inflation targeting: an application to Brazil
This paper uses predictive densities obtained via mixed causal-noncausal autoregressive models to evaluate the statistical sustainability of Brazilian inflation targeting system with the tolerance bounds.
Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions
Mixed-frequency Vector AutoRegressions (MF-VAR) model the dynamics between variables recorded at different frequencies.
Adaptive Random Bandwidth for Inference in CAViaR Models
This paper investigates the size performance of Wald tests for CAViaR models (Engle and Manganelli, 2004).
Inference in mixed causal and noncausal models with generalized Student's t-distributions
The properties of Maximum Likelihood estimator in mixed causal and noncausal models with a generalized Student's t error process are reviewed.
Dimension Reduction for High Dimensional Vector Autoregressive Models
This paper aims to decompose a large dimensional vector autoregessive (VAR) model into two components, the first one being generated by a small-scale VAR and the second one being a white noise sequence.
Predicting crashes in oil prices during the COVID-19 pandemic with mixed causal-noncausal models
This paper aims at shedding light upon how transforming or detrending a series can substantially impact predictions of mixed causal-noncausal (MAR) models, namely dynamic processes that depend not only on their lags but also on their leads.
