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Jel Classification:C01 

Journal Article
The financial market effect of FOMC minutes

The influence of the Federal Reserve?s unanticipated target rate decisions on U.S. asset prices has been the subject of numerous studies. More recently, researchers have looked at the asset price response to statements issued by the Federal Open Market Committee (FOMC). Yet, despite a vast and growing body of evidence on the financial market effect of monetary news released on FOMC meeting days, little is known about the real-time response of U.S. asset prices to the information contained in central bank minutes. This article fills the gap by using a novel, high-frequency data set to examine ...
Economic Policy Review , Issue Dec , Pages 67-81

Working Paper
Sample Selection Models Without Exclusion Restrictions: Parameter Heterogeneity and Partial Identification

This paper studies semiparametric versions of the classical sample selection model (Heckman (1976, 1979)) without exclusion restrictions. We extend the analysis in Honoré and Hu (2020) by allowing for parameter heterogeneity and derive implications of this model. We also consider models that allow for heteroskedasticity and briefly discuss other extensions. The key ideas are illustrated in a simple wage regression for females. We find that the derived implications of a semiparametric version of Heckman's classical sample selection model are consistent with the data for women with no college ...
Working Paper Series , Paper WP 2022-33

Working Paper
A Local Projections Approach to Difference-in-Differences Event Studies

Many of the challenges in the estimation of dynamic heterogeneous treatment effects can be resolved with local projection (LP) estimators of the sort used in applied macroeconometrics. This approach provides a convenient alternative to the more complicated solutions proposed in the recent literature on Difference in-Differences (DiD). The key is to combine LPs with a flexible ‘clean control’ condition to define appropriate sets of treated and control units. Our proposed LP-DiD estimator is clear, simple, easy and fast to compute, and it is transparent and flexible in its handling of ...
Working Paper Series , Paper 2023-12

Report
Four Stylized Facts about COVID-19

We document four facts about the COVID-19 pandemic worldwide relevant for those studying the impact of non-pharmaceutical interventions (NPIs) on COVID-19 transmission. First: across all countries and U.S. states that we study, the growth rates of daily deaths from COVID-19 fell from a wide range of initially high levels to levels close to zero within 20-30 days after each region experienced 25 cumulative deaths. Second: after this initial period, growth rates of daily deaths have hovered around zero or below everywhere in the world. Third: the cross section standard deviation of growth rates ...
Staff Report , Paper 611

Working Paper
Simultaneity in Binary Outcome Models with an Application to Employment for Couples

Two of Peter Schmidt’s many contributions to econometrics have been to introduce a simultaneous logit model for bivariate binary outcomes and to study estimation of dynamic linear fixed effects panel data models using short panels. In this paper, we study a dynamic panel data version of the bivariate model introduced in Schmidt and Strauss (1975) that allows for lagged dependent variables and fixed effects as in Ahn and Schmidt (1995). We combine a conditional likelihood approach with a method of moments approach to obtain an estimation strategy for the resulting model. We apply this ...
Working Paper Series , Paper WP 2022-34

Working Paper
Four Stylized Facts about COVID-19

We document four facts about the worldwide COVID-19 pandemic that are relevant for those studying the impact of nonpharmaceutical interventions (NPIs) on COVID-19 transmission. First, across all countries and U.S. states that we study, the growth rates of daily deaths from COVID-19 fell from a wide range of initially high levels to levels close to zero within 20–30 days after each region experienced 25 cumulative deaths. Second, after this initial period, growth rates of daily deaths have hovered around zero or below everywhere in the world. Third, the cross section standard deviation of ...
FRB Atlanta Working Paper , Paper 2020-15

Working Paper
Dynamic Factor Models, Cointegration, and Error Correction Mechanisms

The paper studies Non-Stationary Dynamic Factor Models such that: (1) the factors Ft are I(1) and singular, i.e. Ft has dimension r and is driven by a q-dimensional white noise, the common shocks, with q < r, and (2) the idiosyncratic components are I(1). We show that Ft is driven by r-c permanent shocks, where c is the cointegration rank of Ft, and q - (r - c) < c transitory shocks, thus the same result as in the non-singular case for the permanent shocks but not for the transitory shocks. Our main result is obtained by combining the classic Granger Representation Theorem with recent ...
Finance and Economics Discussion Series , Paper 2016-018

Working Paper
Non-Stationary Dynamic Factor Models for Large Datasets

We study a Large-Dimensional Non-Stationary Dynamic Factor Model where (1) the factors Ft are I (1) and singular, that is Ft has dimension r and is driven by q dynamic shocks with q less than r, (2) the idiosyncratic components are either I (0) or I (1). Under these assumption the factors Ft are cointegrated and modeled by a singular Error Correction Model. We provide conditions for consistent estimation, as both the cross-sectional size n, and the time dimension T, go to infinity, of the factors, the loadings, the shocks, the ECM coefficients and therefore the Impulse Response Functions. ...
Finance and Economics Discussion Series , Paper 2016-024

Working Paper
A distinction between causal effects in structural and rubin causal models

Structural Causal Models define causal effects in terms of a single Data Generating Process (DGP), and the Rubin Causal Model defines causal effects in terms of a model that can represent counterfactuals from many DGPs. Under these different definitions, notationally similar causal effects make distinct claims about the results of interventions to the system under investigation: Structural equations imply conditional independencies in the data that potential outcomes do not. One implication is that the DAG of a Rubin Causal Model is different from the DAG of a Structural Causal Model. Another ...
Working Papers (Old Series) , Paper 1505

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