This paper develops a new framework and tools, and reexamines Fama-French regressions. For Fama-French portfolios, we consider a continuous-time factor model with a specific error component structure implied by the underlying asset pricing theory. The model is then analyzed as a continuous-time multivariate regression with a general martingale differential error, allowing for time-varying and s...

Motivated by the need of a positive-semidefinite estimator of multivariate realized covariance matrices, we model noisy and asynchronous ultra-high-frequency asset prices in a state-space framework with missing data. We then estimate the covariance matrix of the latent states through a Kalman smoother and Expectation Maximization (KEM) algorithm. Iterating between the two EM steps, we obtain a ...

When analyzing economic games, researchers frequently estimate quantities describing group outcomes, such as the expected revenue in an auction. For such applications, we propose an improved statistical estimation technique called “recombinant estimation.” The technique takes observations of players’ strategies and recombines them to compute all possible group outcomes that could have resulted ...

Data on the life-cycle profiles of inequality in wages, earnings, hours worked and consumption contains precious information for answering questions about the ability of households to insure labor market risk and about the sources of this risk. This paper demonstrates that the choice of whether to control for cohort effects or for time effects has a drastic impact on the estimated age profiles ...

Nonlinearities in the drift and diffusion coefficients influence temporal dependence in scalar diffusion models. We study this link using two notions of temporal dependence: β − mixing and ρ − mixing. We show that β − mixing and ρ − mixing with exponential decay are essentially equivalent concepts for scalar diffusions. For stationary diffusions that fail to be ρ−mixing, we show that they are s...

Recent literature on semiparametric copula models focused on the situation when the marginals are specified nonparametrically and the copula function is given a parametric form. For example, this setup is used in Chen, Fan and Tsyrennikov (2006) [Efficient Estimation of Semiparametric Multivariate Copula Models, JASA] who focus on the efficient estimation of copula parameters. We consider a rev...

Structural change has been conjectured to lead to an upward bias in the estimated forward expectations coefficient in New-Keynesian Phillips curves. We present a simple New-Keynesian model that enables us to assess this proposition. In particular, we investigate the issue of upward bias in the estimated coefficients of the expectations variable in the New-Keynesian Phillips curve based on a mod...

In this paper we consider estimation and inference of common breaks in panel data models via adaptive group fused lasso. We consider two approaches — penalized least squares (PLS) for firstdifferenced models without endogenous regressors, and penalized GMM (PGMM) for first-differenced models with endogeneity. We show that with probability tending to one both methods can correctly determine the ...

Using many moment conditions can improve efficiency but makes the usual GMM inferences inaccurate. Two step GMM is biased. Generalized empirical likelihood (GEL) has smaller bias but the usual standard errors are too small in instrumental variable settings. In this paper we give a new variance estimator for GEL that addresses this problem. It is consistent under the usual asymptotics and under ...

We model the conditional mean and volatility of stock returns as a latent vector autoregressive (VAR) process to study the contemporaneous and intertemporal relationship between expected returns and risk in a flexible statistical framework and without relying on exogenous predictors. We find a strong and robust negative correlation between the innovations to the conditional moments that leads t...