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Inference in Difference-in-Differences: How Much Should we Trust in Independe...
Replication material for 'Inference in Difference-in-Differences: How Much Should we Trust in Independent Clusters?' by Bruno Ferman, published in Journal of Applied Econometrics. -
Taxation in a Globalized World
Due to technological change, the opening of borders, and increased economic integration, the financial costs of relocating businesses and factors of production, moving... -
The role of sex segregation in the gender wage gap among university graduates...
In this paper we examine the gender wage gap among university graduates in Germany from 1997 to 2013 based on the DZHW (the German Centre for Higher Education Research and... -
Export boosting policies and firm performance: Review of empirical evidence a...
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Hours Worked and the U.S. Distribution of Real Annual Earnings 1976--2019 (re...
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Bayesian Collapsed Gibbs Sampling for a Stochastic Volatility Model with a Di...
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Real-time Macroeconomic Projection Using Narrative Central Bank Communication...
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Deep Distributional Time Series Models and the Probabilistic Forecasting of I...
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Identifying and Interpreting the Factors in Factor models via Sparsity: Diffe...
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Density Forecasting with BVAR Models under Macroeconomic Data Uncertainty (re...
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Should We Trust Cross Sectional Multiplier Estimates (replication data)
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New Evidence on the Importance of Instruction Time for Student Achievement on...
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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...
