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Controlling for ability using test scores (replication data)
This paper proposes a semiparametric method to control for ability using standardized test scores, or other item response assessments, in a regression model. The proposed method... -
Towards causal estimates of children's time allocation on skill development (...
In this paper we examine how children's time allocation affects their accumulation of cognitive skill. Children's time allocation is endogenous in a model of skill production... -
Catching up to girls: Understanding the gender imbalance in educational attai...
We estimate a sequential model of schooling to assess the major contributing factors to the large gender imbalance in educational attainment within racial groups. First, we find... -
Systemic risk and bank business models (replication data)
In this paper, we decompose banks' systemic risk into two dimensions: the risk of a bank (?bank tail risk?) and the link of the bank to the system in financial distress... -
Real‐time forecast combinations for the oil price (replication data)
Baumeister and Kilian (Journal of Business and Economic Statistics, 2015, 33(3), 338-351) combine forecasts from six empirical models to predict real oil prices. In this paper,... -
Modeling the effects of grade retention in high school (replication data)
A dynamic discrete-choice model is set up to estimate the effects of grade retention in high school, both in the short run (end-of-year evaluation) and in the long run (drop-out... -
NETS: Network estimation for time series (replication data)
We model a large panel of time series as a vector autoregression where the autoregressive matrices and the inverse covariance matrix of the system innovations are assumed to be... -
Uncertainty across volatility regimes (replication data)
We propose a nonrecursive identification scheme for uncertainty shocks that exploits breaks in the volatility of macroeconomic variables and is novel in the literature on... -
Bootstrap inference for impulse response functions in factor‐augmented vector...
In this study, we consider residual-based bootstrap methods to construct the confidence interval for structural impulse response functions in factor-augmented vector... -
The cyclicality of R&D investment revisited (replication data)
In Fabrizio and Tsolmon (Review of Economics and Statistics, 2014, 96(4), 662-675) and Barlevy (American Economic Review, 2007, 97(4), 1131-1164) it was concluded that R&D... -
An empirical investigation of direct and iterated multistep conditional forec...
When constructing unconditional point forecasts, both direct and iterated multistep (DMS and IMS) approaches are common. However, in the context of producing conditional... -
Steady‐state modeling and macroeconomic forecasting quality (replication data)
Vector autoregressions (VARs) with informative steady-state priors are standard forecasting tools in empirical macroeconomics. This study proposes (i) an adaptive hierarchical... -
Selecting structural innovations in DSGE models (replication data)
Dynamic stochastic general equilibrium (DSGE) models are typically estimated assuming the existence of certain structural shocks that drive macroeconomic fluctuations. We... -
Simultaneous confidence bands: Theory, implementation, and an application to ...
Simultaneous confidence bands are versatile tools for visualizing estimation uncertainty for parameter vectors, such as impulse response functions. In linear models, it is known... -
Private returns to R&D in the presence of spillovers, revisited (replicat...
This is both a replication of Eberhardt et al. (Review of Economics and Statistics, 2013, 95(2), 436-448) using different software, and a critical extension and diagnostic... -
The puzzling effects of monetary policy in VARs: Invalid identification or mi...
Standard vector autoregressions (VARs) often find puzzling effects of monetary policy shocks. Is this due to an invalid (recursive) identification scheme, or because the... -
The approximate solution of finite‐horizon discrete‐choice dynamic programmin...
The estimation of finite-horizon discrete-choice dynamic programming (DCDP) models is computationally expensive. This limits their realism and impedes verification and...
