This paper examines the joint dynamics of a system of asset returns by describing and implementing a factor multivariate stochastic volatility (factor MSV) model. The foundation for the model discussed here is the work of Doz and Renault (2006). Despite its attractive design, that model has not...

This chapter presents a unified set of estimation methods for fitting a rich array of models describing dynamic relationships within a longitudinal data setting. The discussion surveys approaches for characterizing the micro dynamics of continuous dependent variables both over time and across...

We propose the use of state-space models (SSMs) to estimate dynamic spatial relationships from time series data. At each time step, the weight matrix, capturing the latent state, is updated by a spatial autoregressive model. Specifically, we consider two types of SSM: the first one calibrates...

Bivariate duration data frequently arise in economics, biostatistics and other areas. In "bivariate frailty models", dependence between the frailties (i.e., unobserved determinants) induces dependence between the durations. Using notions of quadrant dependence, we study restrictions that this...

This paper reviews recent developments in nonparametric identi.cation of mea- surement error models and their applications in applied microeconomics, in particular, in empirical industrial organization and labor economics. Measurement error models describe mappings from a latent distribution to...

We introduce a simple nonparametric approach to compute impulse response functions. We first search for clusters of recurrent patterns of observations resembling two sets of given initial conditions, one of which contains the impact effect of the structural shock of interest. Then, to trace out...

We introduce a high-dimensional structural time series model, where co-movement between the components is due to common factors. A two-step estimation strategy is presented, which is based on principal components in differences in a first step and state space methods in a second step. The...

We propose a Bayesian infinite hidden Markov model to estimate time- varying parameters in a vector autoregressive model. The Markov structure allows for heterogeneity over time while accounting for state-persistence. By modelling the transition distribution as a Dirichlet process mixture model,...

We study the estimation, the dynamics, and the predictability of option-implied risk-neutral moments (variance, skewness, and kurtosis) for individual stocks from various perspectives. We first show that it is in the estimation of the higher moments essential to use an interpolation with a...

This paper introduces a high-dimensional factor model with time-varying factor loadings. We show that both the factors and the time-varying loadings can be consistently estimated without rotations. We also propose a model-selection approach to determine the constancy of each factor loading for...