In this paper we investigate whether there is a global and dynamic linkage between Monetary and Fiscal Policy in a global framework by using GVAR technique. Our results confirm that there is a significant connection between long-term nominal interest rate and fiscal balance in some industrial...

For Matías Vernengo and Esteban Pérez Caldentey (2020), the MMT literature overemphasizes the choice of the exchange rate regime and the relevance of a flexible exchange rate regime, as well as the ultimate effect of that choice upon the policy space. In addition, they argue that the role of...

This paper shows that debt-financed fiscal multipliers vary depending on the maturity of debt issued to finance spending. Utilizing state-dependent SVAR models and local projections for post-war US data, we show that a fiscal expansion financed with short term debt increases output more than one...

Factor modeling is a popular strategy to induce sparsity in multivariate models as they scale to higher dimensions. We develop Bayesian inference for a recently proposed latent factor copula model, which utilizes a pair copula construction to couple the variables with the latent factor. We use...

It is well-known that the estimated GARCH dynamics exhibit common patterns. Starting from this fact we extend the Dynamic Conditional Correlation (DCC) model by allowing for a clustering structure of the univariate GARCH parameters. The model can be estimated in two steps, the first devoted to...

In many financial applications it is important to classify time series data without any latency while maintaining persistence in the identified states. We propose a greedy online classifier that contemporaneously determines which hidden state a new observation belongs to without the need to...

We propose a dynamic factor state-space model for high-dimensional covariance matrices of asset returns. It uses observed risk factors and assumes that the latent covariance matrix of assets and factors is observed through their realized covariance matrix with a Wishart measurement density. The...

This paper develops and presents the prior adaptive group lasso for generalized linear models. The prior adaptive group lasso is an extension of the prior lasso developed by Jiang, He, and Zhang (2016), which allows for the use of existing information from previous or similar studies in the...

Jump models switch infrequently between states to fit a sequence of data while taking the ordering of the data into account. We propose a new framework for joint feature selection, parameter and state-sequence estimation in jump models. Feature selection is necessary in high-dimensional settings...

We propose a dynamic factor state-space model for the prediction of high-dimensional realized covariance matrices of asset returns. Using a block LDL decomposition of the joint covariance matrix of assets and factors, we express the realized covariance matrix of the individual assets similar to...