This paper provides some test cases, called circuits, for the evaluation of Gaussian likelihood maximization algorithms of the cointegrated vector autoregressive model. Both I(1) and I(2) models are considered. The performance of algorithms is compared first in terms of effectiveness, defined as...

Estimating covariance matrices using high-frequency data is crucial for market makers, investors in newly-issued securities, and risk managers. These estimations often handle the asynchrony of high-frequency trades by using returns for periods between when all instruments have traded (refresh...

Using a panel data set for OECD countries we replicate the typical features of the New Keynesian Phillips curve models (NPCs) that have been estimated on country data. While this corroborates the NPC also on the macro panel data set, a different conclusion is reached when we test whether the NPC...

We propose an easy technique to test for time-variation in coefficients and volatilities. Specifically, by using a noncentered parameterization for state space models, we develop a method to directly calculate the relevant Bayes factor using the Savage-Dickey density ratio — thus avoiding the...

In this paper we present various techniques to estimate Sri Lanka's potential output and output gap, including statistical and model-based approaches. Compared to conventional statistical filters that rely exclusively on information in a single series, the model-based approaches allow potential...

We consider unobserved components time series models where the components are stochastically evolving over time and are subject to stochastic volatility. It enables the disentanglement of dynamic structures in both the mean and the variance of the observed time series. We develop a simulated...

Several recent studies have expressed concern that the Haar prior typically imposed in estimating sign-identified VAR models may be unintentionally informative about the implied prior for the structural impulse responses. This question is indeed important, but we show that the tools that have...

We introduce a new and general methodology for analyzing vector autoregressive models with time-varying coefficient matrices and conditionally heteroskedastic disturbances. Our proposed method is able to jointly treat a dynamic latent factor model for the autoregressive coefficient matrices and...

We introduce a new and general methodology for analyzing vector autoregressive models with time-varying coefficient matrices and conditionally heteroskedastic disturbances. Our proposed method is able to jointly treat a dynamic latent factor model for the autoregressive coefficient matrices and...

This paper provides closed-form formulae for computing the asymptotic standard errors of the estimated autocovariance and autocorrelation functions for stable VAR models by means of the d-method. These standard errors can be used to construct asymptotic confidence bands for the estimated...