Exact Inference Methods for First-Order Autoregressive Distributed Lag Models

Factor Augmented Autoregressive Distributed Lag Models

This paper proposes a factor augmented autoregressive distributed lag (FADL) framework for analyzing the dynamic effects of common and idiosyncratic shocks. We first estimate the common shocks from a large panel of data with a strong factor structure. Impulse responses are then obtained from an autoregression, augmented with a distributed lag of the estimated common shocks. The approach has thr...

Exact likelihood inference for autoregressive gamma stochastic volatility models

Affine stochastic volatility models are widely applicable and appear regularly in empirical finance and macroeconomics. The likelihood function for this class of models is in the form of a high-dimensional integral that does not have a closed-form solution and is difficult to compute accurately. This paper develops a method to compute the likelihood function for discrete-time models that is acc...

Autoregressive Gaussian Random Vectors of First Order

Fast Distributed First-Order Methods

This thesis provides a systematic framework for the development and analysis of distributed optimization methods for multi-agent networks with time-varying connectivity. The goal is to optimize a global objective function which is the sum of local objective functions privately known to individual agents. In our methods, each agent iteratively updates its estimate of the global optimum by optimi...

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...