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The policy relevant treatment effect (PRTE) measures the average effect of switching from a status-quo policy to a counterfactual policy under consideration. Estimation of the PRTE involves estimation of multiple preliminary parameters, including propensity scores, conditional expectation functions of the outcome and covariates given the propensity score, and marginal treatment effects. These preliminary estimators can affect the asymptotic distribution of the PRTE estimator in complicated and intractable manners. In this light, we propose an orthogonal score for double debiased estimation of the PRTE, whereby the asymptotic distribution of the PRTE estimator is obtained without any influence of preliminary parameter estimators as far as they satisfy mild requirements of convergence rates. To our knowledge, this paper is the first to develop limit distribution theories for inference about the PRTE.

Policy evaluation is central to economic data analysis, but economists mostly work with observational data in view of limited opportunities to carry out controlled experiments. In the potential outcome framework, the panel data approach (Hsiao, Ching and Wan, 2012) constructs the counterfactual by exploiting the correlation between cross-sectional units in panel data. The choice of cross-sectional control units, a key step in its implementation, is nevertheless unresolved in data-rich environment when many possible controls are at the researcher's disposal. We propose the forward selection method to choose control units, and establish validity of the post-selection inference. Our asymptotic framework allows the number of possible controls to grow much faster than the time dimension. The easy-to-implement algorithms and their theoretical guarantee extend the panel data approach to big data settings., Comment: accepted for publication at the Journal of Econometrics

This paper considers a treatment effect model in which individual treatment effect may be heterogeneous, even among observationally identical individuals. Specifically, by extending the classical instrumental-variables (IV) model with an endogenous binary treatment, the heteroskedasticity of the error disturbance is allowed to vary with the treatment variable so that the treatment generates both mean and variance effect on the outcome. In this endogenous heteroskedasticity IV (EHIV) model, the standard IV estimator can be inconsistent for the average treatment effect (ATE) and lead to incorrect inference. After nonparametric identification is established, closed-form estimators are provided under the linear EHIV specification for the mean and variance treatment effect, as well as the average treatment effect on the treated (ATT). Asymptotic properties of the estimators are derived. We use Monte Carlo experiments to investigate the performance of the proposed approach and then consider an empirical application regarding the effect of fertility on female labor supply. Our findings demonstrate the importance of accounting for endogenous heteroskedasticity.

Change point detection in time series has attracted substantial interest, but most of the existing results have been focused on detecting change points in the time domain. This paper considers the situation where nonlinear time series have potential change points in the state domain. We apply a density-weighted anti-symmetric kernel function to the state domain and therefore propose a nonparametric procedure to test the existence of change points. When the existence of change points is affirmative, we further introduce an algorithm to estimate the number of change points together with their locations. Theoretical results of the proposed detection and estimation procedures are given and a real dataset is used to illustrate our methods., to appear in Journal of Econometrics

We develop a model where persistent trade shocks create demand for a basket- backed stablecoin, such as Mark Carney's "synthetic hegemonic currency" or Facebook's recent proposal for Libra. In numerical simulations, we find four main results. First, because of general equilibrium effects of the basket currency on the volatility of currency values, overall demand for that currency is small. Second, despite scant holdings of the basket, its global reach may contribute to substantial increases in welfare if the basket is widely accepted, allowing it to complement holdings of sovereign currencies. Third, we calculate the welfare maximizing composition of the basket, finding that optimal weights depend on the pattern of international acceptance, but that basket composition does not significantly affect welfare. Fourth, despite potential welfare improvements, low demand for the basket currency from buyers limits sellers' incentives to invest in accepting it, suggesting that fears of a so-called global stablecoin replacing domestic sovereign currencies may be overstated.

We use longitudinal data from the income tax system to study the impacts of firms’ employment and wage-setting policies on the level and change in immigrant–native wage differences in Canada. We focus on immigrants who arrived in the early 2000s, distinguishing between those with and without a college degree from two broad groups of countries – the U.S., the U.K. and Northern Europe, and the rest of the world. Consistent with a growing literature based on the two-way fixed effects model of Abowd, Kramarz, and Margolis (1999), we find that firm-specific wage premiums explain a significant share of earnings inequality in Canada and contribute to the average earnings gap between immigrants and natives. In the decade after receiving permanent status, earnings of immigrants rise relative to those of natives. Compositional effects due to selective outmigration and changing participation play no role in this gain. About one-sixth is attributable to movements up the job ladder to employers that offer higher pay premiums for all groups, with particularly large gains for immigrants from the “rest of the world” countries.

Efficient computational algorithms for bootstrapping linear regression models with clustered data are discussed. For ordinary least squares (OLS) regression, a new algorithm is provided for the pairs cluster bootstrap, along with two algorithms for the wild cluster bootstrap. One of these is a new way to express an existing method. For instrumental variables (IV) regression, an efficient algorithm is provided for the wild restricted efficient cluster (WREC) bootstrap. All computations are based on matrices and vectors that contain sums of squares and cross-products for the observations within each cluster, which have to be computed just once before the bootstrap loop begins. Monte Carlo experiments are used to study the finite-sample properties of bootstrap Wald tests for OLS regression and of WREC bootstrap tests for IV regression.

Distributional regression models that overcome the traditional focus on relating the conditional mean of the response to explanatory variables and instead target either the complete conditional response distribution or more general features thereof have seen increasing interest in the past decade. The current state of distributional regression will be discussed, with a particular focus on the four most prominent model classes: (i) generalized additive models for location, scale and shape, (ii) conditional transformation models and distribution regression, (iii) density regression, and (iv) quantile and expectile regression. Characteristics of the different distributional regression approaches will be provided to establish a structured overview on the similarities and differences with respect to the required assumptions on the conditional response distribution, theoretical properties, and the availability of software implementations. In addition, challenges arising in the interpretability of distributional regression models will be discussed and all four approaches will be illustrated with an application analyzing determinants of income distributions from the German Socio-Economic Panel (GSOEP).

When predicting crop yield using both functional and multivariate predictors, the prediction performances benefit from the inclusion of the interactions between the two sets of predictors. We assume the interaction depends on a nonparametric, single-index structure of the multivariate predictor and reduce each functional predictor’s dimension using functional principal component analysis (FPCA). Allowing the number of FPCA scores to diverge to infinity, we consider a sequence of semiparametric working models with a diverging number of predictors, which are FPCA scores with estimation errors. We show that the parametric component of the model is root-n consistent and asymptotically normal, the overall prediction error is dominated by the estimation of the nonparametric interaction function, and justify a CV-based procedure to select the tuning parameters.

The Vector AutoRegressive Moving Average (VARMA) model is fundamental to the theory of multivariate time series; however, identifiability issues have led practitioners to abandon it in favor of the simpler but more restrictive Vector AutoRegressive (VAR) model. We narrow this gap with a new optimization-based approach to VARMA identification built upon the principle of parsimony. Among all equivalent data-generating models, we use convex optimization to seek the parameterization that is "simplest" in a certain sense. A user-specified strongly convex penalty is used to measure model simplicity, and that same penalty is then used to define an estimator that can be efficiently computed. We establish consistency of our estimators in a double-asymptotic regime. Our non-asymptotic error bound analysis accommodates both model specification and parameter estimation steps, a feature that is crucial for studying large-scale VARMA algorithms. Our analysis also provides new results on penalized estimation of infinite-order VAR, and elastic net regression under a singular covariance structure of regressors, which may be of independent interest. We illustrate the advantage of our method over VAR alternatives on three real data examples.

This paper explores the microfoundations of consumption models and quantifies the macro implications of consumption heterogeneity. We propose a new empirical method to estimate the response of consumption to permanent and transitory income shocks for different groups of households. We then apply this method to administrative data from Denmark. The large sample size, along with detailed household balance sheet information, allows us to finely divide the population along relevant dimensions. We find that households that stand to lose from an interest rate hike are significantly more responsive to income shocks than those that stand to gain. Following a 1-percentage-point interest rate increase, we estimate that consumption growth decreases by a 1/4 percentage point through this interest rate exposure channel alone, making this channel substantially larger than the intertemporal substitution channel that is at the core of representative agent New Keynesian models.

The COVID-19 pandemic is one of the most pressing issues at present. A question which is particularly important for governments and policy makers is the following: Does the virus spread in the same way in different countries? Or are there significant differences in the development of the epidemic? In this paper, we devise new inference methods that allow to detect differences in the development of the COVID-19 epidemic across countries in a statistically rigorous way. In our empirical study, we use the methods to compare the outbreak patterns of the epidemic in a number of European countries.

The ‘M4’ forecasting competition results were featured recently in a special issue of the International Journal of Forecasting and included projections for demographic time series. We sought to investigate whether the best M4 methods could improve the accuracy of small area population forecasts, which generally suffer from much higher forecast errors than regions with larger populations. The aim of this study was to apply the top ten M4 forecasting methods to produce 5- and 10-year forecasts of small area total populations using historical datasets from Australia and New Zealand. Forecasts were compared against the actual population numbers and forecasts from two simple benchmark models. The M4 methods were found to perform relatively well compared to our benchmarks. In the light of these findings, we discuss possible future directions for small area population forecasting research.

This paper estimates a panel model with endogenously time-varying parameters for COVID-19 cases and deaths in U.S. states. The functional form for infections incorporates important features of epidemiological models but is flexibly parameterized to capture different trajectories of the pandemic. Daily deaths are modeled as a spike-and-slab regression on lagged cases. The paper's Bayesian estimation reveals that social distancing and testing have significant effects on the parameters. For example, a 10 percentage point increase in the positive test rate is associated with a 2 percentage point increase in the death rate among reported cases. The model forecasts perform well, even relative to models from epidemiology and statistics.

In this paper, we model the trajectory of the cumulative confirmed cases and deaths of COVID-19 (in log scale) via a piecewise linear trend model. The model naturally captures the phase transitions of the epidemic growth rate via change-points and further enjoys great interpretability due to its semiparametric nature. On the methodological front, we advance the nascent self-normalization (SN) technique (Shao, 2010) to testing and estimation of a single change-point in the linear trend of a nonstationary time series. We further combine the SN-based change-point test with the NOT algorithm (Baranowski et al., 2019) to achieve multiple change-point estimation. Using the proposed method, we analyze the trajectory of the cumulative COVID-19 cases and deaths for 30 major countries and discover interesting patterns with potentially relevant implications for effectiveness of the pandemic responses by different countries. Furthermore, based on the change-point detection algorithm and a flexible extrapolation function, we design a simple two-stage forecasting scheme for COVID-19 and demonstrate its promising performance in predicting cumulative deaths in the U.S.

We consider a new class of spatio-temporal models with sparse autoregressive coefficient matrices and exogenous variable. To estimate the model, we first profile the exogenous variable out of the response. This leads to a profiled model structure. Next, to overcome endogeneity issue, we propose a class of generalized methods of moment (GMM) estimators to estimate the autoregressive coefficient matrices. A novel bagging-based estimator is further developed to conquer the over-determined issue which also occurs in Chang et al. (2015) and Dou et al. (2016). An adaptive forward–backward greedy algorithm is proposed to learn the sparse structure of the autoregressive coefficient matrices. A new BIC-type selection criteria is further developed to conduct variable selection for GMM estimators. Asymptotic properties are further studied. The proposed methodology is illustrated with extensive simulation studies. A social network dataset is analyzed for illustration purpose.

International audience; Using an approach that embodies an explicit tradeoff between need and governance considerations, we propose an optimal aid allocation formula. We first assume exogenous, then endogenous governance. In the former case, a central concept is need-adjusted aid effectiveness while in the second case the donor has policing instruments under the form of monitoring and sanctioning capacities. We show that external disciplining has two advantages when the donor is sensitive enough to poverty intensity: (1) to cater to poor countries to a greater extent than is possible when local governance cannot be influenced by external forces, and (2) to respond (non-perversely) to improvements in the local governance of a country by raising its aid share. In institutionally weak countries, populations should welcome wisely applied donor's discipline as a way not only to get access to financial support but also to constrain their elites to refrain from abusing their position excessively. Imposing discipline when the release of externally-provided development funds is at stake seems more acceptable than aid directed to the explicit purpose of combatting corruption.

The output gap, the difference between potential and actual output, has a direct impact on policy decisions, e.g., monetary policy. Estimating this gap and its further analysis remain the subject of controversial debates due to methodological problems. We propose a local polynomial regression combined with a Self-Exciting Threshold AutoRegressive (SETAR) model and its forecasting extension for a systematic output gap estimation. Furthermore, local polynomial regression is proposed for the (multivariate) OECD production function approach and its reliability is demonstrated in forecasting output growth. A comparison of the proposed gap to the Hodrick–Prescott filter as well as to estimations by experts from the FED and OECD shows a higher correlation of our output gap with those from those economic institutions. Furthermore, sometimes gaps with different magnitude and different positions above or below the trend are observed between different methods. This may cause competing policy implications which can be improved with our results.

We estimate a structural model using data from a novel experiment investigating how investors’ preferred frequency of portfolio evaluations balance the opposing effects of ambiguity and loss aversion. Investors in the experiment face initial ambiguity concerning return distributions for an asset. They observe draws from the true return distribution of the asset, allowing them to reduce their ambiguity through time. We exploit portfolio choices and stated beliefs over possible return distributions to estimate preferences and ambiguity updating rules. We find that 70% of investors would opt for a high frequency of portfolio evaluations, reflecting the dominating effect of ambiguity aversion over loss aversion.

This paper estimates a discrete choice model of time allocation decisions made by university students. We consider investments in academic and non-academic activities, such as job placements or volunteering. Identification is achieved using data collected through a recent survey of UK university students on subjective expectations about the returns to these activities, and the enjoyment students derive from them. Unobserved heterogeneity in the choice set is addressed using a sufficient set logit method. The analysis reveals significant ethnic differences in the level of investments, expected academic and labour market returns, and enjoyment of academic and non-academic activities. Simulations suggest that existing constraints play an important role in explaining ethnic gaps in investments.

This paper investigates how migration and location choice decisions depend on a large set of location characteristics, with particular focus on measuring the importance and nature of non-monetary costs of moving. We employ a stated-preference approach to elicit respondents’ choice probabilities for a set of hypothetical choice scenarios, using two waves from the NY Fed’s Survey of Consumer Expectations. Our stated probabilistic choice approach allows us to recover the distribution of individual-level preferences for location and mobility attributes without concerns about omitted variables and selection biases that hamper analyses based on observed mobility choices alone. We estimate substantial heterogeneity in the willingness-to-pay (WTP) for location characteristics and in moving costs, both across and within demographic groups. We find moving costs to be strongly associated with attachment to the current neighborhood and dwelling and to social networks. Our results indicate evidence of sorting into current locations based on preferences for location attributes as well as a strong negative association between respondents’ non-monetary moving costs and their moving expectations and actual mobility decisions.

While a large literature is interested in the relationship between family and labor supply outcomes, little is known about the expectations of these objects at earlier stages. We examine these expectations, taking advantage of unique data from the Berea Panel Study. In addition to characterizing expectations, starting during college, the data details outcomes for ten years after graduation. Methodological contributions come from approaches to validate quality of survey expectations data and the recognition that expectations data, along with longitudinal data, can potentially help address endogeneity issues arising in the estimation of the causal effect of family on labor supply.

While economic theories indicate that market power by downstream firms can potentially counteract market power upstream, antitrust policy is opaque as to whether to incorporate countervailing market power in merger analyses. We use detailed national claims data from the health care sector to evaluate whether countervailing insurer power does indeed limit hospitals' exercise of market power. We estimate willingness-to-pay models to evaluate hospital market power across analysis areas. We find that countervailing market power is important: a typical hospital merger would raise hospital prices 4.3% at the 25th percentile of insurer concentration but only 0.97% at the 75th percentile of insurer concentration.

We examine price negotiation in the payment card industry by exploiting a unique merchant-, industry-, and city-level data set. Motivated by the substantial variation in acquirer fees and heterogeneous merchant card transactions, we use Nash bargaining to model the negotiation over the acquirer fee between an acquirer and a merchant. We find that the merchants secure a larger incremental surplus than the acquirer on average. Moreover, merchants might face upward pressure on acquirer fees as the card penetration rate rises over time, and policies that weaken the acquirer's bargaining power could relieve the upward fee pressure.

We analyse a long panel of households’ stock market beliefs to gain insights into the nature of the levels, dynamics, and informativeness of these expectations. In a first step, we classify respondents into one of five groups based on their beliefs data alone. In a second step, we estimate models of expectations at the group level so that belief levels, volatility, and response to information can vary freely across groups. At opposite extremes in terms of optimism we identify pessimists who expect substantially negative returns and financially sophisticated individuals whose expectations are close to the historical average. Two groups expect average returns around zero and differ only in how they respond to information: Extrapolators who become more optimistic following positive information and mean-reverters for whom the opposite is the case. The final group is characterised by its members being unable or unwilling to quantify their beliefs about future returns.

In imperfectly competitive settings, a firm's price depends on its own costs as well as those of its competitors. We demonstrate that this has important implications for the estimation and interpretation of pass-through. Leveraging a large input cost shock resulting from the fracking boom, we isolate price responses to firm-specific, regional, and industry-wide input cost shocks in the U.S. oil refining industry. The pass-through of these components varies from near zero to full pass-through, reconciling seemingly disparate results from the literature. We illustrate the policy implications of rival cost pass-through in the context of a tax on refinery carbon emissions.

“Rational inattention” is becoming increasingly prominent in economic modeling, but there is little empirical evidence for its central premise-that the choice of attention results from a cost-benefit optimization. Observational data typically do not allow researchers to infer attention choices from observables. We fill this gap in the literature by exploiting a unique dataset of professional forecasters who update their inflation forecasts at days of their choice. In the data we observe how many forecasters update (extensive margin of updating), the magnitude of the update (intensive margin), and the objective of optimization (forecast accuracy). There are also “shifters” in incentives: A contest that increases the benefit of accurate forecasting, and the release of official data that reduces the cost of processing information. These features allow us to link observables to attention and incentive parameters. We structurally estimate a model where the decision to update and the magnitude of the update are endogenous and the latter is the outcome of a rational inattention optimization. The empirical findings provide support for the key implication of rational inattention that information-processing efforts react to changing incentives. Counterfactuals reveal that accuracy is maximized if the contest date coincides with the release of information, aligning higher benefits with lower costs of attention.

This paper considers estimation and inference about tail features when the observations beyond some threshold are censored. We first show that ignoring such tail censoring could lead to substantial bias and size distortion, even if the censored probability is tiny. Second, we propose a new maximum likelihood estimator (MLE) based on the Pareto tail approximation and derive its asymptotic properties. Third, we provide a small sample modification to the MLE by resorting to Extreme Value theory. The MLE with this modification delivers excellent small sample performance, as shown by Monte Carlo simulations. We illustrate its empirical relevance by estimating (i) the tail index and the extreme quantiles of the US individual earnings with the Current Population Survey dataset and (ii) the tail index of the distribution of macroeconomic disasters and the coefficient of risk aversion using the dataset collected by Barro and Urs{\'u}a (2008). Our new empirical findings are substantially different from the existing literature.

An approach to modelling volatile financial return series using stationary d-vine copula processes combined with Lebesgue-measure-preserving transformations known as v-transforms is proposed. By developing a method of stochastically inverting v-transforms, models are constructed that can describe both stochastic volatility in the magnitude of price movements and serial correlation in their directions. In combination with parametric marginal distributions it is shown that these models can rival and sometimes outperform well-known models in the extended GARCH family. 1

In this article, we introduce a covariance-mean regression model with heterogeneous similarity matrices. It not only links the covariance of responses to heterogeneous similarity matrices induced by auxiliary information, but also establishes the relationship between the mean of responses and covariates. Under this new model setting, however, two statistical inference challenges are encountered. The first challenge is that the consistency of the covariance estimator based on the standard profile likelihood approach breaks down. Hence, we propose an adjustment and develop the Z -estimation and unconstrained/constrained ordinary least squares estimation methods. We demonstrate that the resulting estimators are consistent and asymptotically normal. The second challenge is testing the adequacy of the covariance-mean regression model comprising both the multivariate mean regression and the heterogeneous covariance matrices. Correspondingly, we introduce two diagnostic test statistics and then obtain their theoretical properties. The proposed estimators and tests are illustrated via extensive simulations and an empirical example study of the stock return comovement in the US stock market.

Many statistical and econometric learning methods rely on Bayesian ideas, often applied or reinterpreted in a frequentist setting. Two leading examples are shrinkage estimators and model averaging estimators, such as weighted-average least squares (WALS). In many instances, the accuracy of these learning methods in repeated samples is assessed using the variance of the posterior distribution of the parameters of interest given the data. This may be permissible when the sample size is large because, under the conditions of the Bernstein--von Mises theorem, the posterior variance agrees asymptotically with the frequentist variance. In finite samples, however, things are less clear. In this paper we explore this issue by first considering the frequentist properties (bias and variance) of the posterior mean in the important case of the normal location model, which consists of a single observation on a univariate Gaussian distribution with unknown mean and known variance. Based on these results, we derive new estimators of the frequentist bias and variance of the WALS estimator in finite samples. We then study the finite-sample performance of the proposed estimators by a Monte Carlo experiment with design derived from a real data application about the effect of abortion on crime rates.

We propose a semiparametric estimator for varying coefficient models when the regressors in the nonparametric components are measured with error. Varying coefficient models are an extension of other popular semiparametric models, including partially linear and nonparametric additive models, and deliver an attractive solution to the curse-of-dimensionality. We use deconvolution kernel estimation in a two-step procedure and show that the estimator is consistent and asymptotically normally distributed. We do not assume that we know the distribution of the measurement error a priori. Instead, we suppose we have access to a repeated measurement of the noisy regressor and present results using the approach of Delaigle, Hall and Meister (2008) and, for cases when the measurement error may be asymmetric, the approach of Li and Vuong (1998) based on Kotlarski’s (1967) identity. We show that the convergence rate of the estimator is significantly reduced when the distribution of the measurement error is assumed unknown and possibly asymmetric. We study the small sample behaviour of our estimator in a simulation study and apply it to a real dataset. In particular, we consider the role of cognitive ability in augmenting the effect of risk preferences on earnings.

This paper proposes a novel nonparametric method for estimating tail jump variation measures from short-dated options, which can achieve rate-efficiency and works in a general infinite jump activity setting, avoiding parametric or semiparametric assumptions for the jump measure. The method is based on expressing the measures of interest as integrals of the Laplace transforms of the jump compensator and developing methods for recovering nonparametrically the latter from the available option data. The separation of volatility from jumps is done in a novel way by making use of the second derivative of the Laplace transform of the returns, de-biased using either the value of the Laplace transform or of its second derivative evaluated at high frequencies. A Monte Carlo study shows the superiority of the newly-developed method over existing ones in empirically realistic settings. In an empirical application to S&P 500 index options, we find risk-neutral negative market tail jump variation that is on average smaller than previous estimates of it, is generated by smaller-sized jumps, and has less dependence on the level of diffusive volatility.

Asymptotic properties of classical kernel estimators for the spectral density are studied in the locally stationary framework. In particular, it is shown that for a locally stationary process standard spectral density estimators consistently estimate the time-averaged spectral density. This result is complemented by some illustrative examples and applications including HAC-inference in the multiple linear regression model, a simple visual tool for the detection of unconditional heteroskedasticity and a test for covariance stationarity.

This paper considers the inference problems in nonlinear quantile regressions with both stationary and nonstationary covariates. The nonparametric local constant quantile estimator is proposed to estimate the unknown quantile regression function, whose asymptotic properties are established under quite general conditions. Specification testing of the quantile regression function is further considered through a statistic constructed based on the integrated squared distance between the parametric and the nonparametric estimators for the regression function. The test statistic is shown to converge to a random variable related to the local time of an Ornstein–Uhlenbeck process under the parametric null. The power of the test against local alternatives is also investigated. Additional asymptotic results on the null parametric quantile estimators and a bootstrap test are developed as well. Numerical results demonstrate that the proposed nonparametric estimator and the specification test enjoy attractive finite sample performance.

In this paper, we use Bayesian nonparametric learning to estimate the skill of actively managed mutual funds and also to estimate the population distribution of skill. A nonparametric hierarchical prior, where the hyperprior distribution is unknown and modeled with a Dirichlet Process prior, is used to model the skill parameter, with its posterior predictive distribution being an estimate of the population distribution. Our nonparametric approach is equivalent to an infinitely ordered mixture of normals where we resolve the uncertainty in the number of mixture components by learning how to partition the funds into groups according to the average ability and the variability in the skill of a group. By resolving the mixture’s uncertainty, our nonparametric prior avoids having to sequentially estimate and test an array of pre-specified, finite ordered, mixture priors. Applying our Bayesian nonparametric learning approach to a panel of actively managed, domestic equity funds, we find the population distribution of skill to be fat-tailed, skewed towards higher levels of performance, with two distinct modes – a primary mode where the average ability covers the average fees charged by funds, and a secondary mode at a performance level where a fund loses money for its investors.

This paper analyzes crime as a function of the interaction between offenders and victims. I study robbery of bus drivers, a crime that remains common in cities throughout the world. Exploiting the timing of a Chilean public transportation reform and detailed administrative data, I show how victims' propensity to resist an attack can alter the level and nature of criminal activity. I also find a large decline in crime after the implementation of a technological innovation that eliminated cash transactions on buses. My results suggest a strong relationship between victim incentives, cash, and crime.

The principle that people should be held personally responsible for the consequences of their choices is a fundamental moral ideal in Western societies. We report from a large-scale experimental study of how far-reaching this principle is for inequality acceptance. We show that third-party spectators violate minimal conditions for a morally relevant choice when making redistributive decisions for two workers. They accept more inequality when the workers have made nominal and forced choices than when brute luck is the source of inequality. We argue that our findings shed light on important current political debates about personal responsibility and redistributive policies.

Inaccurately imposing Gaussian distributional assumptions in standard multivariate time series models does not affect inference on the autoregressive coefficients but distorts both classical and Bayesian inference on the volatility matrix whenever the true error distribution has excess kurtosis relative to the multivariate normal density. Inference on the intercept is also affected whenever the innovations are generated from a non-symmetric distribution. As a result of distributional misspecification, Bayesian methods lead to asymptotically invalid posterior inference for the intercept and the volatility matrix and, consequently, invalid posterior credible sets for quantities such as impulse responses, variance decompositions and density forecasts. We propose a robust and computationally fast Bayesian procedure which delivers asymptotically correct posterior credible sets without the need for distributional assumptions. The proposed corrected Bayesian posteriors for the volatility matrix and the intercept vector are based on the asymptotic covariance of the QML estimators and admit a closed form. Implementation of the procedure requires consistent estimation of the multivariate skewness and kurtosis of the innovations, and we propose novel shrinkage estimators designed to shrink these large dimensional objects towards the skewness and kurtosis of a Gaussian vector. We extend our robust Bayesian analysis to accommodate non-Gaussian disturbances in the presence of parameter instability, by combining the estimators of the current paper with semi-parametric kernel-type methods. We demonstrate that our estimators deliver correct posterior coverage rates in an extensive Monte Carlo exercise under a variety of distributional specifications. Finally, we present empirical evidence that imposing Gaussianity or homoskedasticity assumptions on financial and uncertainty shocks is not justified and may lead to misleading empirical conclusions.

Este trabajo estudia diferencias de genero en la carrera profesional y en las promociones en la banca central, una ocupacion predominantemente masculina. Empleamos datos confidenciales anonimos del personal del Banco Central Europeo (BCE) durante el periodo 2003-2017. Documentamos que surge una brecha salarial entre hombres y mujeres a los pocos anos tras su contratacion, a pesar de condiciones de ingreso similares en terminos de niveles salariales y en otros observables. Tambien encontramos que la probabilidad de promocion a una banda salarial superior era menor para las mujeres hasta 2010, cuando el BCE emitio una declaracion publica en apoyo a la diversidad y tomo varias medidas para apoyar la igualdad de genero. Tras este cambio, la brecha de promocion desaparece. La brecha de promocion de genero previa al cambio de politica se debia, en parte, al efecto de la maternidad sobre la carrera profesional de las madres. Con datos de aplicaciones y decisiones en procesos de ascensos de 2012 a 2017, exploramos el proceso de promocion en profundidad y confirmamos que, durante este periodo mas reciente, las mujeres tienen la misma probabilidad de promocion que los hombres. Sin embargo, esta ausencia de brecha enmascara dos efectos opuestos: una menor probabilidad de que las mujeres soliciten participar en los procesos de promocion y una mayor probabilidad de que las mujeres sean seleccionadas condicionado a ser candidatas. Despues de obtener la promocion, las mujeres muestran una mejor progresion salarial que los hombres ascendidos, lo que sugiere que la mayor probabilidad de ser seleccionado se debe a merito y no a discriminacion positiva.

Costs from switching suppliers can affect prices by discouraging buyer movements from high- to low-cost sellers. This paper uses confidential data on U.S. importers and their Chinese exporters to investigate these costs. I find barriers to supplier adjustments: nearly half of importers keep their partner over time. Importers switch less if their supplier offers higher quality or provides lower prices. I propose and structurally estimate a dynamic discrete choice model to compute switching costs. Cost estimates are large, heterogeneous across products, and matter for trade prices: halving switching costs reduces the U.S.-China Import Price Index by 7.6%.

This paper considers the estimation and inference procedures for the case of a logistic panel regression model with interactive fixed effects, where multiple individual effects are allowed and the model is capable of capturing high-dimensional cross-section dependence. The proposed model also allows for heterogeneous regression coefficients. New Bayesian and non-Bayesian approaches are introduced to estimate the model parameters. We investigate the asymptotic behaviors of the estimated parameters. We show the consistency and asymptotic normality of the estimated regression coefficients and the estimated interactive fixed effects when both the cross-section and time-series dimensions of the panel go to infinity. We prove that the dimensionality of the interactive effects can be consistently estimated by the proposed information criterion. Monte Carlo simulations demonstrate the satisfactory performance of the proposed method. Finally, the method is applied to study the performance of New York City medallion drivers in terms of efficiency.

In this study, we consider cross-sectional interaction models including spatial autoregressive models and peer effects models as special cases. Our model allows the endogenous effect – the effect of others’ outcomes on one’s own outcome – to be nonlinear and nonparametric. For the model estimation, we propose a sieve instrumental variable estimator and establish both its consistency and asymptotic normality. Furthermore, we propose a nonparametric specification test for the linearity of the endogenous effect. Under the null hypothesis of linearity, we show that the test statistic is asymptotically distributed as normal. As an empirical illustration, we focus on the data on regional economic performance investigated by Gennaioli et al. (2013). This empirical analysis highlights the usefulness of the proposed model and method.

Experimental work in monetary economics is usually based on theory that incorporates an infinite horizon. Yet, hard constraints on laboratory sessions lead to finite times when the game must (with probability 1) end, and then simple backward induction implies monetary equilibria cannot exist. Hence, these experiments cannot evaluate subjects’ ability to settle on the use of money as a medium of exchange, that ameliorates trading frictions, as an equilibrium outcome. To address this, we present some finite-horizon games where monetary exchange is an equilibrium outcome, and report some experimental results using these games.

Experiments that investigate the spontaneous emergence of money in laboratory societies rely on indefinitely repeated helping games with random matching (Camera et al., 2013; Camera and Casari, 2014). An important open issue is the lack of a general proof of existence of an equilibrium capable of supporting the efficient allocation under private monitoring, without money. Here, we fill this gap by offering a general proof, as well as by characterizing the efficient non-monetary equilibrium. This technique can be extended to study games with simultaneous actions.

Preferences over wealth are a channel to explain why households in the aggregate are less responsive when real interest rates fall. Following such preferences, they save more than would be optimal in a standard model. However, little is known about preferences over wealth empirically. We run an intentionally simple lab experiment on intertemporal spending and saving decisions with 180 students. Under a positive discount factor, zero interest and linear utility, maximizing behaviour would imply spending any funds instantaneously. While half of the participants behave optimally, we find a robust pattern where participants on average form and maintain a stock of wealth, consistent with wealth entering the utility function directly.

We design a laboratory experiment to test the importance of wealth as a channel for financial contagion across markets with unrelated fundamentals. In a sequential global game, we analyze the decisions of a group of investors that hold assets in two markets. We consider two treatments that vary the level of diversification of these assets across markets. In both treatments, we find evidence of financial contagion. When investors have completely diversified portfolios, we provide evidence of contagion due to a wealth effect: for certain ranges of fundamentals, we show that a decrease in wealth from the investment in the first market makes withdrawals more likely in the second, thereby increasing the probability of a crisis. When portfolio diversification is small, then social imitation is relevant in explaining contagion.

We establish a feasible central limit theorem with convergence rate n 1 ∕ 8 for the estimation of the integrated volatility of volatility (VoV) based on noisy high-frequency data with jumps. This is the first inference theory ever built for VoV estimation under such a general setup. The central limit theorem is applied to provide interval estimates of the VoV and conduct hypothesis tests. Furthermore, when one is interested in the null hypothesis that the VoV is zero, we show that a more powerful test can be established based on a VoV estimator with a convergence rate n 1 ∕ 5 under the null. Empirical results on the S&P 500 and individual stocks show strong evidence of non-zero VoV.

Model averaging has attracted abundant attentions in the past decades as it emerges as an impressive forecasting device in econometrics, social sciences and medicine. So far most developed model averaging methods focus only on either parametric models or nonparametric models with a continuous response. In this paper, we propose a semiparametric model averaging prediction (SMAP) method for a dichotomous response. The idea is to approximate the unknown score function by a linear combination of one-dimensional marginal score functions. The weight parameters involved in the approximation are obtained by first smoothing the nonparametric marginal scores and then applying the parametric model averaging via a maximum likelihood estimation. The proposed SMAP provides greater flexibility than parametric models while being more stable than a fully nonparametric approach. Theoretical properties are investigated in two practical scenarios: (i) covariates are conditionally independent given the response; and (ii) the conditional independence assumption does not hold. In the first scenario, we show that SMAP puts weight one to the true model and hence the model averaging estimators are consistent. In the second scenario in which a “true” model may not exist, SMAP is shown to be asymptotically optimal in the sense that its Kullback–Leibler loss is asymptotically identical to that of the best – but infeasible – model averaging estimator. Empirical evidences from simulation studies and a real data analysis are presented to support and illustrate our methods.

International audience; Using an online randomized experiment in the context of the 2019 European elections campaign in France, we study how fact-checking affects sharing of false news on social media. We exposed a random sample of French voting-age Facebook users to statements on the role of the European Union made by the far-right populist party Rassemblement National.A randomly selected subgroup of participants was also presented with fact-checking of these statements; another subgroup was offered achoice whether to view the fact-checking information. Then, all participants could choose whether to share the false statements on their Facebookpages. We show that (i) both imposed and voluntary fact-checking reduce sharing of false statements by about 45\%; (ii) the size of the effectis similar between imposed and voluntary fact-checking; and (iii) each additional click required to share false statements substantiallyreduces sharing. These results inform the debate about policy proposals aimed at limiting propagation of false news on social media.