
on Econometrics 
By:  Martin Bruns; Helmut Lütkepohl 
Abstract:  We propose a test for timevarying impulse responses in heteroskedastic structural vector autoregressions that can be used when the shocks are identified by external proxy variables as a group. The test can be used even if the shocks are not identified individually. The asymptotic analysis is supported by small sample simulations which show good properties of the test. An investigation of the impact of productivity shocks in a small macroeconomic model for the U.S. illustrates the importance of the issue for empirical work. 
Keywords:  Structural vector autoregression, proxy VAR, heteroskedasticity, productivity shocks 
JEL:  C32 
Date:  2022 
URL:  http://d.repec.org/n?u=RePEc:diw:diwwpp:dp2005&r= 
By:  Léopold Simar (Institut de Statistique, Biostatistique et Sciences Actuarielles, Université Catholique de Louvain, Voie du Roman Pays 20, B1348 LouvainlaNeuve, Belgium); Valentin Zelenyuk (School of Economics and Centre for Efficiency and Productivity Analysis (CEPA) at The University of Queensland, Australia); Shirong Zhao (School of Finance, Dongbei University of Finance and Economics, Dalian, Liaoning 116025) 
Abstract:  nA simple yet easy to implement method is proposed to further improve the finite sample approximation of the recently developed central limit theorems for aggregates of envelopment estimators. Focusing on the simple mean efficiency, we propose using the biascorrected individual efficiency estimate to improve the variance estimator. The extensive MonteCarlo experiments confirm that, for relatively small sample sizes (≤ 100), with both low dimensions and especially for high dimensions, our new method combined with the data sharpening method generally provides better ‘coverage’ (of the true values by the estimated confidence intervals) than the previously developed approaches. 
Keywords:  Efficiency, Nonparametric Efficiency Estimators, Data Envelopment Analysis, Free Disposal Hull 
JEL:  C1 C3 
Date:  2022–04 
URL:  http://d.repec.org/n?u=RePEc:qld:uqcepa:178&r= 
By:  Zhang, Siliang; Chen, Yunxiao 
Abstract:  Latent variable models have been playing a central role in psychometrics and related fields. In many modern applications, the inference based on latent variable models involves one or several of the following features: (1) the presence of many latent variables, (2) the observed and latent variables being continuous, discrete, or a combination of both, (3) constraints on parameters, and (4) penalties on parameters to impose model parsimony. The estimation often involves maximizing an objective function based on a marginal likelihood/pseudolikelihood, possibly with constraints and/or penalties on parameters. Solving this optimization problem is highly nontrivial, due to the complexities brought by the features mentioned above. Although several efficient algorithms have been proposed, there lacks a unified computational framework that takes all these features into account. In this paper, we fill the gap. Specifically, we provide a unified formulation for the optimization problem and then propose a quasiNewton stochastic proximal algorithm. Theoretical properties of the proposed algorithms are established. The computational efficiency and robustness are shown by simulation studies under various settings for latent variable model estimation. 
Keywords:  latent variable models; penalized estimator; stochastic approximation; proximal algorithm; quasiNewton methods; PolyakRuppert averaging; T&F deal 
JEL:  C1 
Date:  2022–05–07 
URL:  http://d.repec.org/n?u=RePEc:ehl:lserod:114489&r= 
By:  JeanJacques Forneron 
Abstract:  In nonlinear estimations, it is common to assess sampling uncertainty by bootstrap inference. For complex models, this can be computationally intensive. This paper combines optimization with resampling: turning stochastic optimization into a fast resampling device. Two methods are introduced: a resampled NewtonRaphson (rNR) and a resampled quasiNewton (rqN) algorithm. Both produce draws that can be used to compute consistent estimates, confidence intervals, and standard errors in a single run. The draws are generated by a gradient and Hessian (or an approximation) computed from batches of data that are resampled at each iteration. The proposed methods transition quickly from optimization to resampling when the objective is smooth and strictly convex. Simulated and empirical applications illustrate the properties of the methods on large scale and computationally intensive problems. Comparisons with frequentist and Bayesian methods highlight the features of the algorithms. 
Date:  2022–05 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2205.03254&r= 
By:  Xavier D'Haultf{\oe}uille; Purevdorj Tuvaandorj 
Abstract:  We develop a new permutation test for inference on a subvector of coefficients in linear models. The test is exact when the regressors and the error terms are independent. Then, we show that the test is consistent and has power against local alternatives when the independence condition is relaxed, under two main conditions. The first is a slight reinforcement of the usual absence of correlation between the regressors and the error term. The second is that the number of strata, defined by values of the regressors not involved in the subvector test, is small compared to the sample size. Simulations and an empirical illustration suggest that the test has good power in practice. 
Date:  2022–05 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2205.06713&r= 
By:  Chen, Zezhun; Dassios, Angelos; Tzougas, George 
Abstract:  In this paper, we present a new family of bivariate mixed exponential regression models for taking into account the positive correlation between the cost of claims from motor third party liability bodily injury and property damage in a versatile manner. Furthermore, we demonstrate how maximum likelihood estimation of the model parameters can be achieved via a novel ExpectationMaximization algorithm. The implementation of two members of this family, namely the bivariate Pareto or, ExponentialInverse Gamma, and bivariate ExponentialInverse Gaussian regression models is illustrated by a real data application which involves fitting motor insurance data from a European motor insurance company. 
Keywords:  bivariate claim size modeling; regression models for the marginal means and dispersion parameters; motor third party liability insurance; expectationmaximization algorithm 
JEL:  C1 
Date:  2022–05–17 
URL:  http://d.repec.org/n?u=RePEc:ehl:lserod:115132&r= 
By:  James G. MacKinnon; Morten {\O}rregaard Nielsen; Matthew D. Webb 
Abstract:  Clusterrobust inference is widely used in modern empirical work in economics and many other disciplines. When data are clustered, the key unit of observation is the cluster. We propose measures of "highleverage" clusters and "influential" clusters for linear regression models. The measures of leverage and partial leverage, and functions of them, can be used as diagnostic tools to identify datasets and regression designs in which clusterrobust inference is likely to be challenging. The measures of influence can provide valuable information about how the results depend on the data in the various clusters. We also show how to calculate two jackknife variance matrix estimators, CV3 and CV3J, as a byproduct of our other computations. All these quantities, including the jackknife variance estimators, are computed in a new Stata package called summclust that summarizes the cluster structure of a dataset. 
Date:  2022–05 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2205.03288&r= 
By:  Fève, Frédérique; Florens, JeanPierre; Simar, Léopold (Université catholique de Louvain, LIDAM/ISBA, Belgium) 
Abstract:  The econometric analysis of cost functions is based on the analysis of the condi tional distribution of the cost Y given the level of the outputs X ∈ Rp+ and given a set of environment variables Z ∈ Rd. The model basically describes the conditional distribution of Y given X ≥ x and Z = z. In many applications, the dimension of Z is naturally large and a fully nonparametric specification of the model is limited by the curse of the dimensionality. Most of the approaches so far are based on twostage estimations when the frontier level does not depend on the value of Z. But even in the case of separability of the frontier, the estimation procedure suffers from several prob lems, mainly due to the inherent bias of the estimated efficiency scores and the poor rates of convergence of the frontier estimates. In this paper we suggest an alternative semiparametric model which avoids the drawbacks of the twostage methods. It is based on a class of model called the Proportional Incremental Cost Functions (PICF), adapted to our setup from the Cox proportional hazard models extensively used in survival analysis for durations models. We define the PICF model, then we examine its properties and propose a semiparametric estimation. By this way of modeling, we avoid the first stage nonparametric estimation of the frontier and avoid the curse of dimensionality keeping the parametric √n rates of convergence for the parameters of interest. We are also able to derive √nconsistent estimator of the conditional orderm robust frontiers (which, by contrast to the full frontier, may depend on Z) and we prove the Gaussian asymptotic properties of the resulting estimators. We illustrate the flexibility and the power of the procedure by some simulated examples and also with some real data sets. 
Keywords:  Cost efficiency ; Nonparametric robust frontier ; Proportional hazard model ; Environmental variables 
JEL:  C10 C14 C51 D22 
Date:  2022–05–01 
URL:  http://d.repec.org/n?u=RePEc:aiz:louvad:2022016&r= 
By:  Nathan Kallus; Miruna Oprescu 
Abstract:  The conditional average treatment effect (CATE) is the best point prediction of individual causal effects given individual baseline covariates and can help personalize treatments. However, as CATE only reflects the (conditional) average, it can wash out potential risks and tail events, which are crucially relevant to treatment choice. In aggregate analyses, this is usually addressed by measuring distributional treatment effect (DTE), such as differences in quantiles or tail expectations between treatment groups. Hypothetically, one can similarly fit covariateconditional quantile regressions in each treatment group and take their difference, but this would not be robust to misspecification or provide agnostic bestinclass predictions. We provide a new robust and modelagnostic methodology for learning the conditional DTE (CDTE) for a wide class of problems that includes conditional quantile treatment effects, conditional superquantile treatment effects, and conditional treatment effects on coherent risk measures given by $f$divergences. Our method is based on constructing a special pseudooutcome and regressing it on baseline covariates using any given regression learner. Our method is modelagnostic in the sense that it can provide the best projection of CDTE onto the regression model class. Our method is robust in the sense that even if we learn these nuisances nonparametrically at very slow rates, we can still learn CDTEs at rates that depend on the class complexity and even conduct inferences on linear projections of CDTEs. We investigate the performance of our proposal in simulation studies, and we demonstrate its use in a case study of 401(k) eligibility effects on wealth. 
Date:  2022–05 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2205.11486&r= 
By:  Paul Levine (University of Surrey); Joseph Pearlman (City University); Alessio Volpicella (University of Surrey); Bo Yang (Swansea University) 
Abstract:  This paper studies the potential ability of an SVAR to match impulse response functions of a wellestablished estimated DSGE model. We study the invertibility (fundamentalness) problem setting out conditions for the RE solution of a linearized Gaussian NKDSGE model to be invertible taking into account the information sets of agents. We then estimate an SVAR by generating artificial data from the theoretical model. A measure of approximate invertibility, where information can be imperfect, is constructed. Based on the VAR(1) representation of the DSGE model, we compare three forms of SVARidentification restrictions; zero, sign and bounds on the forecast error variance, for mapping the reduced form residuals of the empirical model to the structural shocks of interest. Separating out two reasons why SVARs may not recover the impulse responses to structural shocks of the DGP, namely noninvertibility and inappropriate identification restrictions, is then the main objective of the paper. 
JEL:  C11 C18 C32 E32 
Date:  2022–06 
URL:  http://d.repec.org/n?u=RePEc:sur:surrec:0522&r= 
By:  Bauwens, Luc (Université catholique de Louvain, LIDAM/CORE, Belgium); Chevillon, Guillaume; Laurent, Sébastien 
Abstract:  We build on two contributions that have found conditions for large dimensional networks or systems to generate long memory in their individual components, and provide a methodology for modeling and forecasting series displaying long range dependence. We model long memory properties within a vector autoregressive system of order 1 and consider Bayesian estimation or ridge regression. For these, we derive a theorydriven parametric setting that informs a prior distribution or a shrinkage target. Our proposal significantly outperforms univariate time series long memory models when forecasting a daily volatility measure for 250 US company stocks, as well as seasonally adjusted monthly streamflow series recorded at 97 locations of the Columbia river basin. 
Keywords:  Bayesian estimation ; Ridge regression ; Vector autoregressive model ; Forecasting 
Date:  2022–04–03 
URL:  http://d.repec.org/n?u=RePEc:cor:louvco:2022016&r= 
By:  Fabiana Gomez; David Pacini 
Abstract:  The existing literature indicates that spillovers lead to a complicated bias in the estimation of treatment effects in empirical corporate finance. We show that, under simple random treatment assignment, such a complicated bias is simplified if the proxy chosen for the grouplevel treatment intensity is the leaveoneout average treatment. This choice brings two advantages: first, it facilitates the diagnosis of the bias and, second, it facilitates the interpretation of the average spillover effect on the treated. These two advantages justify the use of the leaveoneout average treatment as the preferred proxy for the treatment intensity. We illustrate these advantages in the context of measuring the effect of credit supply contractions on firmsâ€™ employment decisions. 
Date:  2022–05–23 
URL:  http://d.repec.org/n?u=RePEc:bri:uobdis:22/766&r= 
By:  Simon M. S. Lo; Ralf A. Wilke 
Abstract:  A typical situation in competing risks analysis is that the researcher is only interested in a subset of risks. This paper considers a depending competing risks model with the distribution of one risk being a parametric or semiparametric model, while the model for the other risks being unknown. Identifiability is shown for popular classes of parametric models and the semiparametric proportional hazards model. The identifiability of the parametric models does not require a covariate, while the semiparametric model requires at least one. Estimation approaches are suggested which are shown to be $\sqrt{n}$consistent. Applicability and attractive finite sample performance are demonstrated with the help of simulations and data examples. 
Date:  2022–05 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2205.06087&r= 
By:  Erhao Xie 
Abstract:  In the literature that estimates discrete games with incomplete information, researchers usually impose two assumptions. First, either the payoff function or the distribution of private information or both are restricted to follow some parametric functional forms. Second, players’ behaviors are assumed to be consistent with the Bayesian Nash equilibrium. This paper jointly relaxes both assumptions. The framework nonparametrically specifies both the payoff function and the distribution of private information. In addition, each player’s belief about other players’ behaviors is also modeled as a nonparametric function. I allow this belief function to be any probability distribution over other players’ action sets. This specification nests the equilibrium assumption when each player’s belief corresponds to other players’ actual choice probabilities. It also allows nonequilibrium behaviors when some players’ beliefs are biased or incorrect. Under the above framework, this paper first derives a testable implication of the equilibrium condition. It then obtains the identification results for the payoff function, the belief function and the distribution of private information. 
Keywords:  Econometric and statistical methods 
JEL:  C57 
Date:  2022–05 
URL:  http://d.repec.org/n?u=RePEc:bca:bocawp:2222&r= 
By:  Yasushi Ota; Yu Jiang; Daiki Maki 
Abstract:  Noarbitrage property provides a simple method for pricing financial derivatives. However, arbitrage opportunities exist among different markets in various fields, even for a very short time. By knowing that an arbitrage property exists, we can adopt a financial trading strategy. This paper investigates the inverse option problems (IOP) in the extended BlackScholes model. We identify the model coefficients from the measured data and attempt to find arbitrage opportunities in different financial markets using a Bayesian inference approach, which is presented as an IOP solution. The posterior probability density function of the parameters is computed from the measured data.The statistics of the unknown parameters are estimated by a Markov Chain Monte Carlo (MCMC) algorithm, which exploits the posterior state space. The efficient sampling strategy of the MCMC algorithm enables us to solve inverse problems by the Bayesian inference technique. Our numerical results indicate that the Bayesian inference approach can simultaneously estimate the unknown trend and volatility coefficients from the measured data. 
Date:  2022–05 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2205.11012&r= 
By:  Bhattacharjee, Arnab; Kohns, David 
Abstract:  This paper investigates the benefits of internet search data in the form of Google Trends for nowcasting real U.S. GDP growth in real time through the lens of mixed frequency Bayesian Structural Time Series (BSTS) models. We augment and enhance both model and methodology to make these better amenable to nowcasting with large number of potential covariates. Specifically, we allow shrinking state variances towards zero to avoid overfitting, extend the SSVS (spike and slab variable selection) prior to the more flexible normalinversegamma prior which stays agnostic about the underlying model size, as well as adapt the horseshoe prior to the BSTS. The application to nowcasting GDP growth as well as a simulation study demonstrate that the horseshoe prior BSTS improves markedly upon the SSVS and the original BSTS model with the largest gains in dense datageneratingprocesses. Our application also shows that a large dimensional set of search terms is able to improve nowcasts early in a specific quarter before other macroeconomic data become available. Search terms with high inclusion probability have good economic interpretation, reflecting leading signals of economic anxiety and wealth effects. 
Keywords:  globallocal priors, Google trends, noncentred state space, shrinkage 
JEL:  C11 C22 C55 E37 E66 
Date:  2022–05 
URL:  http://d.repec.org/n?u=RePEc:nsr:niesrd:538&r= 
By:  Manudeep Bhuller; Henrik Sigstad 
Abstract:  We study what twostage least squares (2SLS) identifies in models with multiple treatments and multiple instruments under treatment effect heterogeneity. Two testable conditions are shown to be necessary and sufficient for 2SLS to identify a positively weighted sum of individual treatment effects: monotonicity and no cross effects. For justidentified models, these conditions imply that (i) each instrument affects exactly one treatment choice and (ii) choice behavior can be described by singlepeaked preferences (for ordered treatments) or by preferences where the excluded treatment is always either the best or the nextbest alternative (for unordered treatments). For overidentified models, these conditions need to hold only on average across realizations of the instruments. The conditions are satisfied in a singleindex thresholdcrossing model under an easily testable linearity condition. We illustrate how our results can be used to assess the validity of 2SLS with multiple treatments in applications on the returns to educational choices and feedback effects in judicial decisionmaking. 
Date:  2022–05 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2205.07836&r= 
By:  Chaojun Li; Shi Qiu 
Abstract:  This study proposes an efficient algorithm for score computation for regimeswitching models, and derived from which, an efficient expectationmaximization (EM) algorithm. Different from existing algorithms, this algorithm does not rely on the forwardbackward filtering for smoothed regime probabilities, and only involves forward computation. Moreover, the algorithm to compute score is readily extended to compute the Hessian matrix. 
Date:  2022–05 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2205.01565&r= 
By:  Robin C. Sickles (Department of Economics, Rice University, Houston, TX 772511892, USA); Zhichao Wang (School of Economics, University of Queensland, Brisbane, Qld 4072, Australia); Valentin Zelenyuk (School of Economics and Centre for Efficiency and Productivity Analysis (CEPA) at The University of Queensland, Australia) 
Abstract:  In this chapter, we provide a brief overview of the stochastic frontier analysis (SFA) in the context of analysing healthcare, with a focus on hospitals, where it has received most attention. We start with the classical SFA model of Aigner, Lovell and Schmidt (1977) and then consider many of its popular extensions and generalizations in both crosssectional and panel data (mainly published in Journal of Econometrics, Journal of Business & Economic Statistics and Journal of Productivity Analysis). We also briefly discuss semiparametric and nonparametric generalizations, spatial frontiers, and Bayesian SFA. Whenever possible, we refer the readers to various applications of these general methods to healthcare, and for hospitals in particular. Finally, we also illustrate some of these methods for real data on public hospitals in Queensland, Australia, as well as provide practical guidance and references for their computational implementations via R. 
Keywords:  Stochastic frontier analysis, R, healthcare, hospital, Queensland 
Date:  2022–05 
URL:  http://d.repec.org/n?u=RePEc:qld:uqcepa:177&r= 
By:  Kirstin Hubrich; Daniel F. Waggoner 
Abstract:  We conduct a novel empirical analysis of the role of leverage of financial institutions for the transmission of financial shocks to the macroeconomy. For that purpose we develop an endogenous regimeswitching structural vector autoregressive model with timevarying transition probabilities that depend on the state of the economy. We propose new identification techniques for regime switching models. Recently developed theoretical models emphasize the role of bank balance sheets for the buildup of financial instabilities and the amplification of financial shocks. We build a marketbased measure of leverage of financial institutions employing institutionlevel data and find empirical evidence that real effects of financial shocks are amplified by the leverage of financial institutions in the financialconstraint regime. We also find evidence of heterogeneity in how financial institutions, including depository financial institutions, global systemically important banks and selected nonbank financial institutions, affect the transmission of shocks to the macroeconomy. Our results confirm the leverage ratio as a useful indicator from a policy perspective. 
Keywords:  Regime switching models; Timevarying transition probabilities; Financial shocks; Leverage; Bank and nonbank financial institutions; Heterogeneity 
JEL:  C11 C32 C53 C55 E44 G21 
Date:  2022–06–01 
URL:  http://d.repec.org/n?u=RePEc:fip:fedgfe:202234&r= 
By:  Kiran Tomlinson; Austin R. Benson 
Abstract:  Choices made by individuals have widespread impactsfor instance, people choose between political candidates to vote for, between social media posts to share, and between brands to purchasemoreover, data on these choices are increasingly abundant. Discrete choice models are a key tool for learning individual preferences from such data. Additionally, social factors like conformity and contagion influence individual choice. Existing methods for incorporating these factors into choice models do not account for the entire social network and require handcrafted features. To overcome these limitations, we use graph learning to study choice in networked contexts. We identify three ways in which graph learning techniques can be used for discrete choice: learning chooser representations, regularizing choice model parameters, and directly constructing predictions from a network. We design methods in each category and test them on realworld choice datasets, including countylevel 2016 US election results and Android app installation and usage data. We show that incorporating social network structure can improve the predictions of the standard econometric choice model, the multinomial logit. We provide evidence that app installations are influenced by social context, but we find no such effect on app usage among the same participants, which instead is habitdriven. In the election data, we highlight the additional insights a discrete choice framework provides over classification or regression, the typical approaches. On synthetic data, we demonstrate the sample complexity benefit of using social information in choice models. 
Date:  2022–05 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2205.11365&r= 
By:  Johannes König (Australian National University); David I. Stern (University of Kassel); Richard S.J. Tol (Vrije Universiteit Amsterdam) 
Abstract:  We compute confidence intervals for recursive impact factors, that take into account that some citations are more prestigious than others, as well as for the associated ranks of journals, applying the methods to the population of economics journals. The Quarterly Journal of Economics is clearly the journal with greatest impact, the confidence interval for its rank only includes one. Based on the simple bootstrap, the remainder of the â€œTop 5â€ journals are in the top 6 together with the Journal of Finance, while the Xie et al. (2009), and Mogstad et al. (2022) methods generally broaden estimated confidence intervals, particularly for midranking journals. All methods agree that most apparent differences in journal quality are, in fact, mostly insignificant. 
Keywords:  Bibliometrics, citation analysis, publishing, bootstrapping 
JEL:  C71 
Date:  2022–04–30 
URL:  http://d.repec.org/n?u=RePEc:tin:wpaper:20220038&r= 
By:  Rafael Reisenhofer; Xandro Bayer; Nikolaus Hautsch 
Abstract:  Despite the impressive success of deep neural networks in many application areas, neural network models have so far not been widely adopted in the context of volatility forecasting. In this work, we aim to bridge the conceptual gap between established time series approaches, such as the Heterogeneous Autoregressive (HAR) model, and stateoftheart deep neural network models. The newly introduced HARNet is based on a hierarchy of dilated convolutional layers, which facilitates an exponential growth of the receptive field of the model in the number of model parameters. HARNets allow for an explicit initialization scheme such that before optimization, a HARNet yields identical predictions as the respective baseline HAR model. Particularly when considering the QLIKE error as a loss function, we find that this approach significantly stabilizes the optimization of HARNets. We evaluate the performance of HARNets with respect to three different stock market indexes. Based on this evaluation, we formulate clear guidelines for the optimization of HARNets and show that HARNets can substantially improve upon the forecasting accuracy of their respective HAR baseline models. In a qualitative analysis of the filter weights learnt by a HARNet, we report clear patterns regarding the predictive power of past information. Among information from the previous week, yesterday and the day before, yesterday's volatility makes by far the most contribution to today's realized volatility forecast. Moroever, within the previous month, the importance of single weeks diminishes almost linearly when moving further into the past. 
Date:  2022–05 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2205.07719&r= 