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...

The following note proposes a simple procedure to estimate k parameters of interest in a linear model with potentially k conditionally endogenous variables of interest and m endogenous control variables in the presence of at least one instrumental variable under the assumption of conditional...

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...

In multiple regressions, explanatory variables with simple correlation coefficients with the dependent variable below 0.1 in absolute value (such as aid/gross domestic product (GDP) with GDP growth) face a problem of parameter identification. They may have very large, statistically significant,...

Financial network models are a useful tool to model interconnectedness and systemic risks in financial systems. They are essentially descriptive, and based on highly correlated networks. In this paper we embed them in a stochastic framework, aimed at a more parsimonious and more realistic...

In multiple regressions, explanatory variables with simple correlation coefficients with the dependent variable below 0.1 in absolute value (such as aid/gross domestic product (GDP) with GDP growth) face a problem of parameter identification. They may have very large, statistically significant,...

We find the asymptotic distribution of the multi-dimensional multi-scale and kernel estimators for high-frequency financial data with microstructure. Sampling times are allowed to be asynchronous. The central limit theorem is shown to have a feasible version. In the process, we show that the...