This paper studies the computational complexity of Bayesian and quasi-Bayesian estimation in large samples carried out using a basic Metropolis random walk. The framework covers cases where the underlying likelihood or extremum criterion function is possibly non-concave, discontinuous, and of...

Quantile and least-absolute deviations (LAD) methods are popular robust statistical methods but have not generally been applied to state filtering and sequential parameter learning. This paper introduces robust state space models whose error structure coincides with quantile estimation...

This paper develops particle-based methods for sequential inference in nonlinear models. Sequential inference is notoriously difficult in nonlinear state space models. To overcome this, we use auxiliary state variables to slice out nonlinearities where appropriate. This induces a Fixed-dimension...

This paper develops a semi-parametric Bayesian regression model for estimating heterogeneous treatment effects from observational data. Standard nonlinear regression models, which may work quite well for prediction, can yield badly biased estimates of treatment effects when fit to data with...

Growing evidence suggests that many social and economic networks are scale free in that their degree distribution has a power-law tail. A common explanation for this phenomenon is a random network formation process with preferential attachment. For a general version of such a process, we develop...

This paper initiates a non-linear fractional unit root test also known as autoregressive neural network–fractional integration (ARNN–FI) test. The test is based on a new multilayer perceptron of a neural network process which is applied in Yaya et al. (2021). Further, to investigate the...

We introduce two neural network models designed for application in statistical learning. The mean-variance neural network regression model allows us to simultaneously model the mean and the variance of a response variable. In case of a two-dimensional response vector, the...

We present a new robust bootstrap method for a test when there is a nuisance parameter under the alternative, and some parameters are possibly weakly or non-identified. We focus on a Bierens (1990)-type conditional moment test of omitted nonlinearity for convenience, and because of difficulties...

Growing evidence suggests that many social and economic networks are scale free in that their degree distribution P(d) is approximately proportional to d^{-γ}. The most widespread explanation for this phenomenon is a random network formation process with preferential attachment. For a general...

In this paper, I show how gradient-based optimization methods can be used to estimate stochastic dynamic models in economics. By extending the state space to include all model parameters, I show that we need to solve the model only once to do structural estimation. Parameters are then estimated...