19 research outputs found
Prior selection for panel vector autoregressions
Bayesian shrinkage priors have been very popular in estimating vector autoregressions (VARs) of possibly large dimensions. Many of these priors are not appropriate for multi-country settings, as they cannot account for the type of restrictions typically met in panel vector autoregressions (PVARs). With this in mind, new parametric and semi-parametric priors for PVARs are proposed, which perform valuable shrinkage in large dimensions and also allow for soft clustering of variables or countries which are homogeneous. The implication of these new priors for modelling interdependencies and heterogeneities among different countries in a panel VAR setting, is discussed. Monte Carlo evidence and an empirical forecasting exercise show clear and important gains from the new priors compared to existing popular priors for VARs and PVARs
Prior selection for panel vector autoregressions
There is a vast literature that specifies Bayesian shrinkage priors for vector autoregressions (VARs) of possibly large dimensions. In this paper I argue that many of these priors are not appropriate for multi-country settings, which motivates me to develop priors for panel VARs (PVARs). The parametric and semi-parametric priors I suggest not only perform valuable shrinkage in large dimensions, but also allow for soft clustering of variables or countries which are homogeneous. I discuss the implications of these new priors for modelling interdependencies and heterogeneities among different countries in a panel VAR setting. Monte Carlo evidence and an empirical forecasting exercise show clear and important gains of the new priors compared to existing popular priors for VARs and PVARs
Bayesian nonparametric sparse VAR models
High dimensional vector autoregressive (VAR) models require a large number of
parameters to be estimated and may suffer of inferential problems. We propose a
new Bayesian nonparametric (BNP) Lasso prior (BNP-Lasso) for high-dimensional
VAR models that can improve estimation efficiency and prediction accuracy. Our
hierarchical prior overcomes overparametrization and overfitting issues by
clustering the VAR coefficients into groups and by shrinking the coefficients
of each group toward a common location. Clustering and shrinking effects
induced by the BNP-Lasso prior are well suited for the extraction of causal
networks from time series, since they account for some stylized facts in
real-world networks, which are sparsity, communities structures and
heterogeneity in the edges intensity. In order to fully capture the richness of
the data and to achieve a better understanding of financial and macroeconomic
risk, it is therefore crucial that the model used to extract network accounts
for these stylized facts.Comment: Forthcoming in "Journal of Econometrics" ---- Revised Version of the
paper "Bayesian nonparametric Seemingly Unrelated Regression Models" ----
Supplementary Material available on reques
Dealing with heterogeneity in panel VARs using sparse finite mixtures
In this paper, we provide a parsimonious means of estimating panel VARs with stochastic volatility. We assume that coefficients associated with domestic lagged endogenous variables arise from a finite mixture of Gaussian distribution. Shrinkage on the cluster size is introduced through suitable priors on the component weights and cluster-relevant quantities are identified through novel normal-gamma shrinkage priors. To assess whether dynamic interdependencies between units are needed, we moreover impose shrinkage priors on the coefficients related to other countries' endogenous variables. Finally, our model controls for static interdependencies by assuming that the reduced form shocks of the model feature a factor stochastic volatility structure. We assess the merits of the proposed approach by using synthetic data as well as a real data application. In the empirical application, we forecast Eurozone unemployment rates and show that our proposed approach works well in terms of predictions.Series: Department of Economics Working Paper Serie
Bayesian nonparametric graphical models for time-varying parameters VAR
Over the last decade, big data have poured into econometrics, demanding new
statistical methods for analysing high-dimensional data and complex non-linear
relationships. A common approach for addressing dimensionality issues relies on
the use of static graphical structures for extracting the most significant
dependence interrelationships between the variables of interest. Recently,
Bayesian nonparametric techniques have become popular for modelling complex
phenomena in a flexible and efficient manner, but only few attempts have been
made in econometrics. In this paper, we provide an innovative Bayesian
nonparametric (BNP) time-varying graphical framework for making inference in
high-dimensional time series. We include a Bayesian nonparametric dependent
prior specification on the matrix of coefficients and the covariance matrix by
mean of a Time-Series DPP as in Nieto-Barajas et al. (2012). Following Billio
et al. (2019), our hierarchical prior overcomes over-parametrization and
over-fitting issues by clustering the vector autoregressive (VAR) coefficients
into groups and by shrinking the coefficients of each group toward a common
location. Our BNP timevarying VAR model is based on a spike-and-slab
construction coupled with dependent Dirichlet Process prior (DPP) and allows
to: (i) infer time-varying Granger causality networks from time series; (ii)
flexibly model and cluster non-zero time-varying coefficients; (iii)
accommodate for potential non-linearities. In order to assess the performance
of the model, we study the merits of our approach by considering a well-known
macroeconomic dataset. Moreover, we check the robustness of the method by
comparing two alternative specifications, with Dirac and diffuse spike prior
distributions
Forecasting with Global Vector Autoregressive Models: a Bayesian Approach
This paper develops a Bayesian variant of global vector autoregressive (B-GVAR) models to forecast an international set of macroeconomic and financial variables. We propose a set of hierarchical priors and compare the predicive performance of B-GVAR models in terms of point and density forecasts for one-quarter-ahead and four-quarter-ahead forecast horizons. We find that forecasts can be improved by employing a global framework and hierarchical priors which induce country-specific degrees of shrinkage on the coefficients of the GVAR model. Forecasts from various B-GVAR specifications tend to outperform forecasts from a naive univariate model, a global model without shrinkage on the parameters and country-specific vector autoregressions
Penalized Estimation of Panel Vector Autoregressive Models
This paper proposes LASSO estimation specific for panel vector autoregressive
(PVAR) models. The penalty term allows for shrinkage for different lags, for shrinkage
towards homogeneous coeficients across panel units, for penalization of lags of
variables belonging to another cross-sectional unit, and for varying penalization across
equations. The penalty parameters therefore build on time series and cross-sectional
properties that are commonly found in PVAR models. Simulation results point towards
advantages of using the proposed LASSO for PVAR models over ordinary least
squares in terms of forecast accuracy. An empirical forecasting application with five
countries support these findings