Economics

This paper studies the latent index representation of the conditional LATE model, making explicit the role of covariates in treatment selection. We find that if the directions of the monotonicity condition are the same across all values of the conditioning covariate, which is often assumed in the literature, then the treatment choice equation has to satisfy a separability condition between the instrument and the covariate. This global representation result establishes testable restrictions imposed on the way covariates enter the treatment choice equation. We later extend the representation theorem to incorporate multiple ordered levels of treatment.

This paper proposes several tests of restricted specification in nonparametric instrumental regression. Based on series estimators, test statistics are established that allow for tests of the general model against a parametric or nonparametric specification as well as a test of exogeneity of the vector of regressors. The tests' asymptotic distributions under correct specification are derived and their consistency against any alternative model is shown. Under a sequence of local alternative hypotheses, the asymptotic distributions of the tests is derived. Moreover, uniform consistency is established over a class of alternatives whose distance to the null hypothesis shrinks appropriately as the sample size increases. A Monte Carlo study examines finite sample performance of the test statistics.

I devise a novel approach to evaluate the effectiveness of fiscal policy in the short run with multi-category treatment effects and inverse probability weighting based on the potential outcome framework. This study's main contribution to the literature is the proposed modified conditional independence assumption to improve the evaluation of fiscal policy. Using this approach, I analyze the effects of government spending on the US economy from 1992 to 2019. The empirical study indicates that large fiscal contraction generates a negative effect on the economic growth rate, and small and large fiscal expansions realize a positive effect. However, these effects are not significant in the traditional multiple regression approach. I conclude that this new approach significantly improves the evaluation of fiscal policy.

Multidimensional heterogeneity and endogeneity are important features of models with multiple treatments. We consider a heterogeneous coefficients model where the outcome is a linear combination of dummy treatment variables, with each variable representing a different kind of treatment. We use control variables to give necessary and sufficient conditions for identification of average treatment effects. With mutually exclusive treatments we find that, provided the generalized propensity scores (Imbens, 2000) are bounded away from zero with probability one, a simple identification condition is that their sum be bounded away from one with probability one. These results generalize the classical identification result of Rosenbaum and Rubin (1983) for binary treatments.

Several studies of the Job Corps tend to nd more positive earnings effects for males than for females. This effect heterogeneity favouring males contrasts with the results of the majority of other training programmes' evaluations. Applying the translated quantile approach of Bitler, Hoynes, and Domina (2014), I investigate a potential mechanism behind the surprising findings for the Job Corps. My results provide suggestive evidence that the effect of heterogeneity by gender operates through existing gender earnings inequality rather than Job Corps trainability differences.

This paper considers how the effect of job displacement varies across different individuals. In particular, our interest centers on features of the distribution of the individual-level effect of job displacement. Identifying features of this distribution is particularly challenging -- e.g., even if we could randomly assign workers to be displaced or not, many of the parameters that we consider would not be point identified. We exploit our access to panel data, and our approach relies on comparing outcomes of displaced workers to outcomes the same workers would have experienced if they had not been displaced and if they maintained the same rank in the distribution of earnings as they had before they were displaced. Using data from the Displaced Workers Survey, we find that displaced workers earn about $157 per week less, on average, than they would have earned if they had not been displaced. We also find that there is substantial heterogeneity. We estimate that 42% of workers have higher earnings than they would have had if they had not been displaced and that a large fraction of workers have experienced substantially more negative effects than the average effect of displacement. Finally, we also document major differences in the distribution of the effect of job displacement across education levels, sex, age, and counterfactual earnings levels. Throughout the paper, we rely heavily on quantile regression. First, we use quantile regression as a flexible (yet feasible) first step estimator of conditional distributions and quantile functions that our main results build on. We also use quantile regression to study how covariates affect the distribution of the individual-level effect of job displacement.

This paper proposes a new method to identify leaders and followers in a network. Prior works use spatial autoregression models (SARs) which implicitly assume that each individual in the network has the same peer effects on others. Mechanically, they conclude the key player in the network to be the one with the highest centrality. However, when some individuals are more influential than others, centrality may fail to be a good measure. I develop a model that allows for individual-specific endogenous effects and propose a two-stage LASSO procedure to identify influential individuals in a network. Under an assumption of sparsity: only a subset of individuals (which can increase with sample size n) is influential, I show that my 2SLSS estimator for individual-specific endogenous effects is consistent and achieves asymptotic normality. I also develop robust inference including uniformly valid confidence intervals. These results also carry through to scenarios where the influential individuals are not sparse. I extend the analysis to allow for multiple types of connections (multiple networks), and I show how to use the sparse group LASSO to detect which of the multiple connection types is more influential. Simulation evidence shows that my estimator has good finite sample performance. I further apply my method to the data in Banerjee et al. (2013) and my proposed procedure is able to identify leaders and effective networks.

In this paper, we are interested in testing if the volatility process is constant or not during a given time span by using high-frequency data with the presence of jumps and microstructure noise. Based on estimators of integrated volatility and spot volatility, we propose a nonparametric way to depict the discrepancy between local variation and global variation. We show that our proposed test estimator converges to a standard normal distribution if the volatility is constant, otherwise it diverges to infinity. Simulation studies verify the theoretical results and show a good finite sample performance of the test procedure. We also apply our test procedure to do the heteroscedasticity test for some real high-frequency financial data. We observe that in almost half of the days tested, the assumption of constant volatility within a day is violated. And this is due to that the stock prices during opening and closing periods are highly volatile and account for a relative large proportion of intraday variation.

This article investigates retirement decumulation behaviours using the Grouped Fixed-Effects (GFE) estimator applied to Australian panel data on drawdowns from phased withdrawal retirement income products. Behaviours exhibited by the distinct latent groups identified suggest that retirees may adopt simple heuristics determining how they draw down their accumulated wealth. Two extensions to the original GFE methodology are proposed: a latent group label-matching procedure which broadens bootstrap inference to include the time profile estimates, and a modified estimation procedure for models with time-invariant additive fixed effects estimated using unbalanced data.

This paper presents a novel high dimensional forecast combination estimator in the presence of many forecasts and potential latent group structures. The new algorithm, which we call ℓ 2 -relaxation, minimizes the squared ℓ 2 -norm of the weight vector subject to a relaxed version of the first-order conditions, instead of minimizing the mean squared forecast error as those standard optimal forecast combination procedures. A proper choice of the tuning parameter achieves bias and variance trade-off, and incorporates as special cases the simple average (equal-weight) strategy and the conventional optimal weighting scheme. When the variance-covariance (VC) matrix of the individual forecast errors exhibits latent group structures -- a block equicorrelation matrix plus a VC for idiosyncratic noises, ℓ 2 -relaxation delivers combined forecasts with roughly equal within-group weights. Asymptotic optimality of the new method is established by exploiting the duality between the sup-norm restriction and the high-dimensional sparse ℓ 1 -norm penalization. Excellent finite sample performance of our method is demonstrated in Monte Carlo simulations. Its wide applicability is highlighted in three real data examples concerning empirical applications of microeconomics, macroeconomics and finance.