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Density Forecasting with BVAR Models under Macroeconomic Data Uncertainty (re...
This dataset has no description
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Should We Trust Cross Sectional Multiplier Estimates (replication data)
This dataset has no description
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New Evidence on the Importance of Instruction Time for Student Achievement on...
This dataset has no description
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Equity‐premium prediction: Attention is all you need (replication data)
Predictions of stock returns are greatly improved relative to low-dimensional forecasting regressions when the forecasts are based on the estimated factor of large data sets,... -
Did earnings mobility change after minimum wage introduction? Evidence from p...
We analyze the evolution of earnings mobility in Germany between 2011 and 2018. We use transition matrices and parametric and semi-nonparametric copula models to assess the... -
Long‐run predictability tests are even worse than you thought (replication data)
We derive asymptotic results for the long-horizon ordinary least squares (OLS) estimator and corresponding -statistic for stationary autoregressive predictors. The... -
Forecasting low‐frequency macroeconomic events with high‐frequency data (repl...
High-frequency financial and economic indicators are usually time-aggregated before computing forecasts of macroeconomic events, such as recessions. We propose a mixed-frequency... -
General Bayesian time‐varying parameter vector autoregressions for modeling g...
US yield curve dynamics are subject to time-variation, but there is ambiguity about its precise form. This paper develops a vector autoregressive (VAR) model with time-varying... -
Raiders of the Lost High‐Frequency Forecasts: New Data and Evidence on the Ef...
We introduce a new dataset of real gross domestic product (GDP) growth and core personal consumption expenditures (PCE) inflation forecasts produced by the staff of the Board of... -
Bayesian estimation of multivariate panel probits with higher‐order network i...
This paper proposes a Bayesian estimation framework for panel data sets with binary dependent variables where a large number of cross-sectional units are observed over a short... -
Reevaluating the evidence on seasonality in housing market match quality: Rep...
I revisit Ngai and Tenreyro (2014)'s empirical analysis of seasonal match quality in American Housing Survey (AHS) data. Using 1999 data only, Ngai and Tenreyro show that homes... -
Workplace Heterogeneity and Wage Inequality in Denmark (replication data)
Wage inequality is on the rise in most developed economies, and this phenomenon has fostered a growing body of research on its potential drivers. Using German data over the... -
Regression with an imputed dependent variable (replication data)
Researchers are often interested in the relationship between two variables, with no single data set containing both. A common strategy is to use proxies for the dependent... -
Reassessing the dependence between economic growth and financial conditions s...
Adrian, Boyarchenko and Giannone ((2019), ABG) adapt quantile regression (QR) methods to examine the relationship between US economic growth and financial conditions. We confirm... -
Normal but skewed? (replication data)
We propose a multivariate normality test against skew normal distributions using higher-order log-likelihood derivatives, which is asymptotically equivalent to the likelihood... -
The role of observed and unobserved heterogeneity in the duration of unemploy...
This paper studies the degree to which observable and unobservable worker characteristics account for the variation in the aggregate duration of unemployment. I model the... -
Covariate distribution balance via propensity scores (replication data)
This paper proposes new estimators for the propensity score that aim to maximize the covariate distribution balance among different treatment groups. Heuristically, our proposed... -
Matching theory and evidence on Covid‐19 using a stochastic network SIR model...
This paper develops an individual-based stochastic network SIR model for the empirical analysis of the Covid-19 pandemic. It derives moment conditions for the number of infected... -
Count Roy model with finite mixtures (replication data)
This paper develops the Finite Mixture Roy model for count variables and uses this semiparametric model to analyze the effect of supplemental Medigap private insurance on the...
