Conditional heteroskedasticity of the error terms is a common occurrence in financial factor models, such as the CAPM and Fama-French factor models, This feature necessitates the use of heteroskedasticity consistent (HC) standard errors to make valid inference for regression coefficients. In...

In this chapter, we trace the history of how endogeneity came to be recognized as an important issue in marketing analysis and the widespread adoption of methods to address endogeneity. To quantify the rise of econometric methods for addressing endogeneity in marketing, we begin with a...

This paper introduces a flexible local projection that generalises the model by Jorda (2005) to a non-parametric setting using Bayesian Additive Regression Trees. Monte Carlo experiments show that our BART-LP model is able to capture non-linearities in the impulse responses. Our first...

Mildly explosive autoregressions have been extensively employed in recent theoretical and applied econometric work to model the phenomenon of asset market bubbles. An important issue in this context concerns the construction of confidence intervals for the autoregressive parameter that...

We compare two approaches to using information about the signs of structural shocks at specific dates within a structural vector autoregression (SVAR): imposing ‘narrative restrictions’ (NR) on the shock signs in an otherwise set-identified SVAR; and casting the information about the shock...

Montiel Olea and Plagborg-Møller (2021) recently propose robust confidence intervals for impulse responses based on the lag-augmented local projection regression, under the full mean independence assumption on the shock process. We show that their uniformity result remains valid for a more...

We propose a new variational approximation of the joint posterior distribution of the log-volatility in the context of large Bayesian VARs. In contrast to existing approaches that are based on local approximations, the new proposal provides a global approximation that takes into account the...

Many marketing research projects aim not only to estimate the causal effect of an intervention but also to understand the mechanism by which the intervention affects outcomes. However, existing causal inference methods, such as difference-in-differences and synthetic control, do not include...

In many applications of network analysis, it is important to distinguish between observed and unobserved factors affecting network structure. We show that a network model with discrete unobserved link heterogeneity and binary (or discrete) covariates corresponds to a stochastic blockmodel (SBM)....

This paper introduces a simple and tractable sieve estimation of semiparametric conditional factor models with latent factors. We establish large-$N$-asymptotic properties of the estimators and the tests without requiring large $T$. We also develop a simple bootstrap procedure for conducting...