Econometric Time Series
http://lists.repec.org/mailman/listinfo/nep-ets
Econometric Time Series
2020-09-14
Robust inference intime-varying structural VAR models: The DC-Cholesky multivariate stochasticvolatility model
http://d.repec.org/n?u=RePEc:zbw:bubdps:342020&r=ets
This paper investigates how the ordering of variables affects properties of the time-varying covariance matrix in the Cholesky multivariate stochastic volatility model.It establishes that systematically different dynamic restrictions are imposed whenthe ratio of volatilities is time-varying. Simulations demonstrate that estimated co-variance matrices become more divergent when volatility clusters idiosyncratically.It is illustrated that this property is important for empirical applications. Specifically, alternative estimates on the evolution of U.S. systematic monetary policy andinflation-gap persistence indicate that conclusions may critically hinge on a selectedordering of variables. The dynamic correlation Cholesky multivariate stochasticvolatility model is proposed as a robust alternative.
Hartwig, Benny
Model uncertainty,Multivariate stochastic volatility,Dynamic correlations,Monetary policy,Structural VAR
2020
BGVAR: Bayesian Global Vector Autoregressions with Shrinkage Priors in R
http://d.repec.org/n?u=RePEc:fip:feddgw:88639&r=ets
This document introduces the R library BGVAR to estimate Bayesian global vector autoregressions (GVAR) with shrinkage priors and stochastic volatility. The Bayesian treatment of GVARs allows us to include large information sets by mitigating issues related to overfitting. This improves inference and often leads to better out-of-sample forecasts. Computational efficiency is achieved by using C++ to considerably speed up time-consuming functions. To maximize usability, the package includes numerous functions for carrying out structural inference and forecasting. These include generalized and structural impulse response functions, forecast error variance and historical decompositions as well as conditional forecasts.
Maximilian Böck
Martin Feldkircher
Florian Huber
Global Vector Autoregressions; Bayesian inference; time series analysis; R
2020-08-20
On cointegration for processes integrated at different frequencies
http://d.repec.org/n?u=RePEc:pra:mprapa:102611&r=ets
This paper explores the possibility of cointegration existing between processes integrated at di¤erent frequencies. Using the demodulator operator, we show that such cointegration can exist and explore its form using both complex- and real-valued representations. A straightforward approach to test for the presence of cointegration between processes integrated at di¤erent frequencies is proposed, with a Monte Carol study and an application showing that the testing approach works well.
del Barrio Castro, Tomás
Cubada, Ginaluca
Osborn, Denise R.
Periodic Cointegration, Polynomial Cointegration, Demodulator Operator.
2020-08-25
Spectral Targeting Estimation of $\lambda$-GARCH models
http://d.repec.org/n?u=RePEc:arx:papers:2007.02588&r=ets
This paper presents a novel estimator of orthogonal GARCH models, which combines (eigenvalue and -vector) targeting estimation with stepwise (univariate) estimation. We denote this the spectral targeting estimator. This two-step estimator is consistent under finite second order moments, while asymptotic normality holds under finite fourth order moments. The estimator is especially well suited for modelling larger portfolios: we compare the empirical performance of the spectral targeting estimator to that of the quasi maximum likelihood estimator for five portfolios of 25 assets. The spectral targeting estimator dominates in terms of computational complexity, being up to 57 times faster in estimation, while both estimators produce similar out-of-sample forecasts, indicating that the spectral targeting estimator is well suited for high-dimensional empirical applications.
Simon Hetland
2020-07
Structural Gaussian mixture vector autoregressive model
http://d.repec.org/n?u=RePEc:arx:papers:2007.04713&r=ets
A structural version of the Gaussian mixture vector autoregressive model is introduced. The shocks are identified by combining simultaneous diagonalization of the error term covariance matrices with zero and sign constraints. It turns out that this often leads to less restrictive identification conditions than in conventional SVAR models, while some of the constraints are also testable. The accompanying R-package gmvarkit provides easy-to-use tools for estimating the models and applying the introduced methods.
Savi Virolainen
2020-07
Nowcasting in a Pandemic using Non-Parametric Mixed Frequency VARs
http://d.repec.org/n?u=RePEc:arx:papers:2008.12706&r=ets
This paper develops Bayesian econometric methods for posterior and predictive inference in a non-parametric mixed frequency VAR using additive regression trees. We argue that regression tree models are ideally suited for macroeconomic nowcasting in the face of the extreme observations produced by the pandemic due to their flexibility and ability to model outliers. In a nowcasting application involving four major countries in the European Union, we find substantial improvements in nowcasting performance relative to a linear mixed frequency VAR. A detailed examination of the predictive densities in the first six months of 2020 shows where these improvements are achieved.
Florian Huber
Gary Koop
Luca Onorante
Michael Pfarrhofer
Josef Schreiner
2020-08
Variable Selection and Forecasting in High Dimensional Linear Regressions with Structural Breaks
http://d.repec.org/n?u=RePEc:ces:ceswps:_8475&r=ets
This paper is concerned with problem of variable selection and forecasting in the presence of parameter instability. There are a number of approaches proposed for forecasting in the presence of breaks, including the use of rolling windows or exponential down-weighting. However, these studies start with a given model specification and do not consider the problem of variable selection. It is clear that, in the absence of breaks, researchers should weigh the observations equally at both variable selection and forecasting stages. In this study, we investigate whether or not we should use weighted observations at the variable selection stage in the presence of structural breaks, particularly when the number of potential covariates is large. Amongst the extant variable selection approaches we focus on the recently developed One Covariate at a time Multiple Testing (OCMT) method that allows a natural distinction between the selection and forecasting stages, and provide theoretical justification for using the full (not down-weighted) sample in the selection stage of OCMT and down-weighting of observations only at the forecasting stage (if needed). The benefits of the proposed method are illustrated by empirical applications to forecasting output growths and stock market returns.
Alexander Chudik
M. Hashem Pesaran
Mahrad Sharifvaghefi
time-varying parameters, structural breaks, high-dimensionality, multiple testing, variable selection, one covariate at a time multiple testing (OCMT), forecasting
2020
Time-Varying Parameters as Ridge Regressions
http://d.repec.org/n?u=RePEc:arx:papers:2009.00401&r=ets
Time-varying parameters (TVPs) models are frequently used in economics to model structural change. I show that they are in fact ridge regressions. Instantly, this makes computations, tuning, and implementation much easier than in the state-space paradigm. Among other things, solving the equivalent dual ridge problem is computationally very fast even in high dimensions, and the crucial "amount of time variation" is tuned by cross-validation. Evolving volatility is dealt with using a two-step ridge regression. I consider extensions that incorporate sparsity (the algorithm selects which parameters vary and which do not) and reduced-rank restrictions (variation is tied to a factor model). To demonstrate the usefulness of the approach, I use it to study the evolution of monetary policy in Canada. The application requires the estimation of about 4600 TVPs, a task well within the reach of the new method.
Philippe Goulet Coulombe
2020-09
Bellman filtering for state-space models
http://d.repec.org/n?u=RePEc:tin:wpaper:20200052&r=ets
This article presents a new filter for state-space models based on Bellman's dynamic programming principle applied to the posterior mode. The proposed Bellman filter generalises the Kalman filter including its extended and iterated versions, while remaining equally inexpensive computationally. The Bellman filter is also (unlike the Kalman filter) robust under heavy-tailed observation noise and applicable to a wider range of models. Simulation studies reveal that the mean absolute error of the Bellman-filtered states using estimated parameters typically falls within a few percent of that produced by the mode estimator evaluated at the true parameters, which is optimal but generally infeasible.
Rutger Jan Lange
Bellman filter, dynamic programming, Kalman filter, maximum a posteriori (MAP) estimate, posterior mode, state-space model
2020-08-27