Economics

We study the role and drivers of persistence in the extensive margin of bilateral trade. Motivated by a stylized heterogeneous firms model of international trade with market entry costs, we propose new bias-corrected dynamic binary choice estimators with three sets of high-dimensional fixed effects. Monte Carlo simulations confirm their desirable statistical properties. A reassessment of the most commonly studied determinants of the extensive margin of trade demonstrates that both true state dependence and unobserved heterogeneity contribute strongly to trade persistence and that taking this persistence into account matters significantly in identifying the effects of trade policies on the extensive margin.

As the COVID-19 pandemic progresses, researchers are reporting findings of randomized trials comparing standard care with care augmented by experimental drugs. The trials have small sample sizes, so estimates of treatment effects are imprecise. Seeing imprecision, clinicians reading research articles may find it difficult to decide when to treat patients with experimental drugs. Whatever decision criterion one uses, there is always some probability that random variation in trial outcomes will lead to prescribing sub-optimal treatments. A conventional practice when comparing standard care and an innovation is to choose the innovation only if the estimated treatment effect is positive and statistically significant. This practice defers to standard care as the status quo. To evaluate decision criteria, we use the concept of near-optimality, which jointly considers the probability and magnitude of decision errors. An appealing decision criterion from this perspective is the empirical success rule, which chooses the treatment with the highest observed average patient outcome in the trial. Considering the design of recent and ongoing COVID-19 trials, we show that the empirical success rule yields treatment results that are much closer to optimal than those generated by prevailing decision criteria based on hypothesis tests.

Contribution of this paper lies in the formulation and estimation of a generalized model for stochastic frontier analysis (SFA) that nests virtually all forms used and includes some that have not been considered so far. The model is based on the generalized t distribution for the observation error and the generalized beta distribution of the second kind for the inefficiency-related term. We use this general error structure framework for formal testing, to compare alternative specifications and to conduct model averaging. This allows us to deal with model specification uncertainty, which is one of the main unresolved issues in SFA, and to relax a number of potentially restrictive assumptions embedded within existing SF models. We also develop Bayesian inference methods that are less restrictive compared to the ones used so far and demonstrate feasible approximate alternatives based on maximum likelihood.

Assessing the trend of the COVID-19 pandemic and policy effectiveness is essential for both policymakers and stock investors, but challenging because the crisis has unfolded with extreme speed and the previous index was not suitable for measuring policy effectiveness for COVID-19. This paper builds an index of policy effectiveness on fighting COVID-19 pandemic, whose building method is similar to the index of Policy Uncertainty, based on province-level paper documents released in China from Jan.1st to Apr.16th of 2020. This paper also studies the relationships among COVID-19 daily confirmed cases, stock market volatility, and document-based policy effectiveness in China. This paper uses the DCC-GARCH model to fit conditional covariance's change rule of multi-series. This paper finally tests four hypotheses, about the time-space difference of policy effectiveness and its overflow effect both on the COVID-19 pandemic and stock market. Through the inner interaction of this triad structure, we can bring forward more specific and scientific suggestions to maintain stability in the stock market at such exceptional times.

A structural version of the Gaussian mixture vector autoregressive model is introduced. The shocks are identified by combining simultaneous diagonalization of the error term covariance matrices with zero and sign constraints. It turns out that this often leads to less restrictive identification conditions than in conventional SVAR models, while some of the constraints are also testable. The accompanying R-package gmvarkit provides easy-to-use tools for estimating the models and applying the introduced methods.

We propose a novel method for modeling data by using structural models based on economic theory as regularizers for statistical models. We show that even if a structural model is misspecified, as long as it is informative about the data-generating mechanism, our method can outperform both the (misspecified) structural model and un-structural-regularized statistical models. Our method permits a Bayesian interpretation of theory as prior knowledge and can be used both for statistical prediction and causal inference. It contributes to transfer learning by showing how incorporating theory into statistical modeling can significantly improve out-of-domain predictions and offers a way to synthesize reduced-form and structural approaches for causal effect estimation. Simulation experiments demonstrate the potential of our method in various settings, including first-price auctions, dynamic models of entry and exit, and demand estimation with instrumental variables. Our method has potential applications not only in economics, but in other scientific disciplines whose theoretical models offer important insight but are subject to significant misspecification concerns.

In this paper, a statistical model for panel data with unobservable grouped factor structures which are correlated with the regressors and the group membership can be unknown. The factor loadings are assumed to be in different subspaces and the subspace clustering for factor loadings are considered. A method called least squares subspace clustering estimate (LSSC) is proposed to estimate the model parameters by minimizing the least-square criterion and to perform the subspace clustering simultaneously. The consistency of the proposed subspace clustering is proved and the asymptotic properties of the estimation procedure are studied under certain conditions. A Monte Carlo simulation study is used to illustrate the advantages of the proposed method. Further considerations for the situations that the number of subspaces for factors, the dimension of factors and the dimension of subspaces are unknown are also discussed. For illustrative purposes, the proposed method is applied to study the linkage between income and democracy across countries while subspace patterns of unobserved factors and factor loadings are allowed.

Synchronization is a phenomenon when a pair of fluctuations adjust their rhythms when they interact with each other. We measure the degree of synchronization between the exchange rates of the U.S. dollar (USD) and the euro, and between those of the USD and the Japanese yen based on purchasing power parity (PPP) over time. We employ a method of synchronization analysis using the Hilbert transform which is common in the field of nonlinear science. We find that the synchronization degree is high most of the time, suggesting a PPP establishment. The synchronization degree does not remain high across periods containing economic events with asymmetric effects, such as the U.S. real estate bubble.

Consider a panel data setting with observations of N units over T time periods. Each unit undergoes one of D interventions at time period T 0 , with 1≤ T 0 <T , prior to which all units are under control. We present synthetic interventions (SI), a framework to estimate counterfactual outcomes of each unit under each of the D interventions, averaged over the post-intervention time periods. We prove identification of this causal parameter under a latent factor model across time, units, and interventions. We furnish an estimator for this causal parameter and establish its consistency and asymptotic normality. In doing so, we establish novel identification and inference results for the synthetic controls (SC) literature. Further, we introduce a hypothesis test to validate when to use SI (and thereby SC). Through simulations and an empirical case-study, we demonstrate efficacy of the SI framework. Lastly, we discuss connections between SI and tensor estimation.

In this paper, we test the contribution of foreign management on firms' competitiveness. We use a novel dataset on the careers of 165,084 managers employed by 13,106 companies in the United Kingdom in the period 2009-2017. We find that domestic manufacturing firms become, on average, between 7% and 12% more productive after hiring the first foreign managers, whereas foreign-owned firms register no significant improvement. In particular, we test that previous industry-specific experience is the primary driver of productivity gains in domestic firms (15.6%), in a way that allows the latter to catch up with foreign-owned firms. Managers from the European Union are highly valuable, as they represent about half of the recruits in our data. Our identification strategy combines matching techniques, difference-in-difference, and pre-recruitment trends to challenge reverse causality. Results are robust to placebo tests and to different estimators of Total Factor Productivity. Eventually, we argue that upcoming limits to the mobility of foreign talents after the Brexit event can hamper the allocation of productive managerial resources.