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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... -
Do words hurt more than actions? The impact of trade tensions on financial ma...
We use machine learning techniques to quantify trade tensions between the United States and China. Our measure matches well-known events in the US-China trade dispute and is... -
Instrumental‐variable estimation of exponential‐regression models with two‐wa...
This paper introduces instrumental-variable estimators for exponential-regression models that feature two-way fixed effects. These techniques allow us to develop a... -
Trade openness and growth: A network‐based approach (replication data)
We propose a novel approach to the study of international trade based on a theory of country integration that embodies a broad systemic viewpoint on the relationship between... -
Identification of dynamic latent factor models of skill formation with transl...
In this paper, we highlight an important property of the translog production function for the identification of treatment effects in a model of latent skill formation. We show... -
Do rural banks matter that much? Burgess and Pande (2005) reconsidered (repli...
We replicate Burgess and Pande's (2005) analysis of the effect of India's state-led bank expansion on poverty. The authors instrument rural bank branch expansion by its trend... -
Macroeconomic forecasting in a multi‐country context (replication data)
In this paper, we propose a hierarchical shrinkage approach for multi-country VAR models. In implementation, we consider three different scale mixtures Normals priors and... -
Recurrent conditional heteroskedasticity (replication data)
We propose a new class of financial volatility models, called the REcurrent Conditional Heteroskedastic (RECH) models, to improve both in-sample analysis and out-of-sample... -
ARDL bounds test for cointegration: Replicating the Pesaran et al. (2001) res...
This paper replicates the UK earnings equation using the autoregressive distributed lag (ARDL) modeling approach and the bounds test for cointegration by Pesaran et al. (Journal... -
Extremal connectedness of hedge funds (replication data)
We propose a dynamic measure of extremal connectedness tailored to the short reporting period and unbalanced nature of hedge funds data. Using multivariate extreme value... -
Optimal forecast under structural breaks (replication data)
This paper develops an optimal combined estimator to forecast out-of-sample under structural breaks. When it comes to forecasting, using only the postbreak observations after... -
Oil prices, gasoline prices, and inflation expectations (replication data)
It has long been suspected, given the salience of gasoline prices, that fluctuations in gasoline prices shift households' 1-year inflation expectations. Assessing this view... -
Making text count: Economic forecasting using newspaper text (replication data)
This paper examines several ways to extract timely economic signals from newspaper text and shows that such information can materially improve forecasts of macroeconomic... -
How is machine learning useful for macroeconomic forecasting? (replication data)
We move beyond Is Machine Learning Useful for Macroeconomic Forecasting? by adding the how. The current forecasting literature has focused on matching specific variables and... -
Robust inference under time‐varying volatility: A real‐time evaluation of pro...
In many forecast evaluation applications, standard tests as well as tests allowing for time-variation in relative forecast ability build on... -
The role of precautionary and speculative demand in the global market for cru...
Contemporary structural models of the global market for crude oil jointly specify precautionary and speculative demand shocks as a composite shock, named a storage demand shock.... -
Generalized band spectrum estimation with an application to the New Keynesian...
This paper proposes a new method for estimating linear dynamic structural models. The proposed generalized band spectrum estimator (GBSE) generalizes band spectrum regression to...
