We empirically identify the effect of inflation on relative price distortions, using a novel identification approach derived from sticky price theories with time or state-dependent adjustment frictions. Our approach can be directly applied to micro price data, does not rely on estimating the gap...

The literature on panel models has made considerable progress in the last few decades, integrating non-stationary data both in the time and spatial domain. However, there remains a gap in the literature that simultaneously models non-stationarity and cointegration in both the time and spatial...

In this paper, we introduce a method of generating bootstrap samples with unknown patterns of cross-sectional/spatial dependence, which we call the spatial dependent wild bootstrap. This method is a spatial counterpart to the wild dependent bootstrap of Shao (2010) and generates data by...

Purpose: This study investigates the return co-movements associated with investor sentiment shifts in the cross-sections under a setting where market-wide sentiment interacts with short-sale impediments. Design/methodology/approach: This study estimates the return sensitivity to market sentiment...

We improve on the Instrumented Principal Component Analysis (IPCA) model developed in Kelly, Pruitt and Su (2019) by providing more efficient Generalized Least Square (GLS) estimators with a closed-form limiting distribution allowing for a more consistent (mis)pricing inference. The IPCA model...

This chapter surveys new development in causal inference using time-series cross-sectional (TSCS) data. I start by clarifying two identification regimes for TSCS analysis: one under the strict exogeneity assumption and one under the sequential ignorability assumption. I then review three most...

Making use of restrictions imposed by equilibrium, theoretical progress has been made on the nonparametric and semiparametric estimation and identification of scalar additive hedonic models (Ekeland, Heckman, and Nesheim, 2002) and scalar nonadditive hedonic models (Heckman, Matzkin, and...

This paper considers semiparametric identification of structural dynamic discrete choice models and models for dynamic treatment effects. Time to treatment and counterfactual outcomes associated with treatment times are jointly analyzed. We examine the implicit assumptions of the dynamic...

This paper considers the identification and estimation of hedonic models. We establish that in an additive version of the hedonic model, technology and preferences are generically identified up to affine transformations from data on demand and supply in a single hedonic market. For a very...

This paper evaluates the informativeness of eight micro pricing moments for monetary non-neutrality. Frequency of price changes is the only robustly informative moment. The ratio of kurtosis over frequency is significant only because of frequency, and insignificant when non-pricing moments are...

We confront prominent asset pricing models with the classical out-of-sample cross-sectional test of Fama and MacBeth (1973). For all models, we uncover three main findings: (i) the intercept coefficients are economically large and highly statistically significant; (ii) the cross-sectional factor...

The synthetic control method (SCM) is a popular approach for estimating the impact of a treatment on a single unit in panel data settings. The "synthetic control" is a weighted average of control units that balances the treated unit's pre-treatment outcomes and other covariates as closely as...

This paper considers testing the hypothesis that errors in a panel data model are weakly Cross-sectionally dependent (CD), using the exponent of cross-sectional dependence introduced recently in Bailey, Kapetanios and Pesaran (2012). It is shown that the implicit null of the CD test depends on...

This paper generalizes the approach to estimating a first-order spatial autoregressive model with spatial autoregressive disturbances (SARAR(1,1)) in a cross-section with heteroskedastic innovations by Kelejian and Prucha (2008) to the case of spatial autoregressive models with spatial...

This paper considers semiparametric identification of structural dynamic discrete choice models and models for dynamic treatment effects. Time to treatment and counterfactual outcomes associated with treatment times are jointly analyzed. We examine the implicit assumptions of the dynamic...

This paper considers the identification and estimation of hedonic models. We establish that in an additive version of the hedonic model, technology and preferences are generically identified up to affine transformations from data on demand and supply in a single hedonic market. For a very...

Making use of restrictions imposed by equilibrium, theoretical progress has been made on the nonparametric and semiparametric estimation and identification of scalar additive hedonic models (Ekeland, Heckman, and Nesheim, 2002) and scalar nonadditive hedonic models (Heckman, Matzkin, and...

Errors-in-variables (EIV) biases plague asset pricing tests. We offer a new perspective on ad-dressing the EIV issue: instead of viewing EIV biases as estimation errors that potentiallycontaminate next-stage risk premium estimates, we consider them to be return innovationsthat follow a...

This paper evaluates a specification for conditional beta models following Fama and French (2019). In this paper, I reject the Fama and French model that assumes characteristics are conditional betas in favor of a linear conditional beta model following Shanken (1990). Model-implied zero-beta...

The Supplemental Appendix to "Factors that Fit the Time Series and Cross-Section of Stock Returns" provides additional tables and figures supporting the main text. Among others it includes robustness results for the large cross-section of all decile portfolios and the extended cross-section with...

Cross-sectional variation in micro data can be used to empirically evaluate sufficient statistics for the response of aggregate variables to policy shocks of interest. We demonstrate an easy-to-use approach through a detailed example. We evaluate the sufficiency of micro pricing moments for the...

We propose a new method for estimating latent asset pricing factors that fit the time-series and cross-section of expected returns. Our estimator generalizes Principal Component Analysis (PCA) by including a penalty on the pricing error in expected returns. We show that our estimator strongly...

Lou and Shu decompose Amihud's illiquidity measure (ILLIQ) proposing that its component, the average of inverse dollar trading volume (IDVOL), is sufficient to explain the pricing of illiquidity. Their decomposition misses a component of ILLIQ that is related to illiquidity. We find that this...

We identify a novel economic mechanism through which passive ownership positively affects informational efficiency in the cross-section of firms. Passive ownership lowers the cost of capital, encouraging firms to invest more aggressively in risky growth opportunities. The resultant higher cash...

We provide the first systematic evidence on the link between long-short anomaly portfolio returns—a cornerstone of the cross-sectional literature—and the time-series predictability of the aggregate market excess return. Using 100 representative anomalies from the literature, we employ a...

This paper introduces a unified framework of counterfactual estimation for time-series cross-sectional data, which estimates the average treatment effect on the treated by directly imputing treated counterfactuals. Its special cases include several newly developed methods, such as the fixed...

Fixed effects estimation of nonlinear dynamic panel models is subject to the incidental parameter issue, leading to a biased asymptotic distribution. While this problem has been studied extensively in the literature, a general analysis allowing for both serial and cross-sectional dependence is...

This paper jointly examines the link between competition and expected returns in the time series and in the cross section. To this end, we build a general equilibrium model where markups vary because of firm entry with oligopolistic competition. When concentration is high, markups are more...

In this paper we focus on estimating the degree of cross-sectional dependence in the error terms of a classical panel data regression model. For this purpose we propose an estimator of the exponent of cross-sectional dependence denoted by α; which is based on the number of non-zero pair-wise...

We use the cross-section regression approach of Fama and MacBeth (FM 1973) to construct cross-section factors corresponding to the time-series factors of Fama and French (FF 2015). Time-series models that use only cross-section factors provide better descriptions of average returns than...