Journal of Applied Econometrics最新文献

This paper studies identification and estimation of the average treatment effect of a latent treated subpopulation in difference-in-difference designs when the observed treatment is differentially (or endogenously) mismeasured for the truth. Common examples include misreporting and mistargeting. We propose a two-step estimator that corrects for the empirically common phenomenon of one-sided misclassification in the treatment status. The solution uses a single exclusion restriction embedded in a partial observability probit to point identify the latent parameter. We demonstrate the method by revisiting two large-scale national programs in India: one where pension benefits are underreported and second where the program is mistargeted.

Many forecast surveys ask their participants for fixed-event forecasts. As fixed-event forecasts have seasonal properties, users often employ an ad hoc approach to approximate fixed-horizon forecasts based on these fixed-event forecasts. We derive an optimal approximation for fixed-horizon forecasts by minimizing the mean-squared approximation error. Like the ad hoc approach, our approximation employs a weighted average of the fixed-event forecasts. The optimal weights tend to differ substantially from those of the ad hoc approach. In empirical applications, the gains from using optimal instead of ad hoc weights turn out to be sizeable. The approximation approach proposed can also be useful in other applications.

This paper revisits the question of how shocks to expectations of market participants can cause business cycle fluctuations. We use a vector autoregression to estimate dynamic causal effects of belief shocks which are extracted from nowcast errors about output growth. In a first step, we replicate and corroborate the findings of Enders, Kleemann, and Müller (2021). The second step computes nowcast errors about growth-at-risk at various quantiles. This involves both recovering the quantiles of the nowcast distribution of output growth from the Survey of Professional Forecasters, and, since the true quantiles of output growth are unobserved, estimating them with quantile regressions. We document a lack of distinct patterns in response to shocks arising from nowcasts misjudging macroeconomic risk. Although the differences are statistically insignificant, belief shocks about downside risk seem to produce somewhat sharper business cycle fluctuations.

Quantile regression provides a powerful tool for investigating the effects of covariates on key quantiles of a conditional distribution. However, we often lack a general picture of how covariates affect the overall shape of the conditional distribution. Using quantile regression estimation and quantile-based measures of spread, skewness, and kurtosis, we propose spread regression, skewness regression, and kurtosis regression as empirical tools to quantify the effects of covariates on the spread, skewness, and kurtosis of the conditional distribution. This methodology is applied to US wage data during 1980–2019 with substantive findings, and a comparison is made with a moment-based robust approach. In addition, we decompose changes in the spread into composition effects and structural effects to clarify rising inequality. We also provide the Stata commands spreadreg, skewreg, and kurtosisreg, which are available from the Statistical Software Components (SSC) archive, for easy implementation of spread, skewness, and kurtotis regressions.

This paper makes a case for the use of jackknife methods for standard error, $��p�$ value, and confidence interval construction for difference-in-difference (DiD) regression. We review cluster-robust, bootstrap, and jackknife standard error methods and show that standard methods can substantially underperform in conventional settings. In contrast, our proposed jackknife inference methods work well in broad contexts. We illustrate the relevance by replicating several influential DiD applications and showing how inferential results can change if jackknife standard error and inference methods are used.

Monthly macroeconomic series captured the sharp fluctuations during the COVID-19 pandemic only with a lag. The use of alternative high-frequency data is promising for crisis periods, but it is difficult to extract relevant business cycle information from them. We present a Bayesian mixed-frequency dynamic factor model with stochastic volatility for measuring GDP growth at high-frequency intervals. Its novelty is an additional state-space block, in which the sparse observations in the mixed-frequency data are augmented to a balanced panel with observed and estimated latent information. The dynamic factors are then estimated conditional on the augmented data. Our model exploits the information in rich datasets of weekly, monthly, and quarterly series, including alternative high-frequency data. GDP is nowcasted timely and accurately during volatile periods.

This paper discusses pairing double/debiased machine learning (DDML) with stacking, a model averaging method for combining multiple candidate learners, to estimate structural parameters. In addition to conventional stacking, we consider two stacking variants available for DDML: Short-stacking exploits the cross-fitting step of DDML to substantially reduce the computational burden, and pooled stacking enforces common stacking weights over cross-fitting folds. Using calibrated simulation studies and two applications estimating gender gaps in citations and wages, we show that DDML with stacking is more robust to partially unknown functional forms than common alternative approaches based on single pre-selected learners. We provide Stata and R software implementing our proposals.