Maximum Likelihood Inference in Weakly Identified DSGE Models

Predictive Likelihood Comparisons with DSGE and DSGE-VAR Models

In this paper we treat the issue of forecasting with DSGE and DSGE-VAR models, with particular attention to Bayesian estimation of the predictive distribution and its mean and covariance. As a novel contribution to the forecasting literature, which extends beyond (log-linearized) DSGE models and DSGE-VARs, we show how the value of the h-step-ahead marginal and joint predictive likelihood for a ...

Analytical quasi maximum likelihood inference in multivariate volatility models

Quasi maximum likelihood estimation and inference in multivariate volatility models remains a challenging computational task if, for example, the dimension is high. One of the reasons is that typically numerical procedures are used to compute the score and the Hessian, and often they are numerically unstable. We provide analytical formulae for the score and the Hessian and show in a simulation ...

The Inference for Weakly Identified State-Space Models: A Bayesian Analysis of Affine Term Structure Models

Imposing identifying restrictions in empirical work is sometimes more difficult than is commonly appreciated. In the classical framework, they can introduce bias in the estimators and produce confidence intervals that are too conservative. In the Bayesian framework, as long as priors are proper, posteriors are proper and the model is identified in that specific sense, but they can introduce num...

Local Maximum Likelihood Estimation and Inference

Modified Maximum Likelihood Estimation in First-Order Autoregressive Moving Average Models with some Non-Normal Residuals

When modeling time series data using autoregressive-moving average processes, it is a common practice to presume that the residuals are normally distributed. However, sometimes we encounter non-normal residuals and asymmetry of data marginal distribution. Despite widespread use of pure autoregressive processes for modeling non-normal time series, the autoregressive-moving average models have le...