
on Econometrics 
By:  Jungbin Hwang; Byunghoon Kang; Seojeong Lee 
Abstract:  We propose a new finite sample corrected variance estimator for the linear generalized method of moments (GMM) including the onestep, twostep, and iterated estimators. Our formula additionally corrects for the overidentification bias in variance estimation on top of the commonly used finite sample correction of Windmeijer (2005) which corrects for the bias from estimating the efficient weight matrix, so is doubly corrected. Formal stochastic expansions are derived to show the proposed double correction estimates the variance of some higherorder terms in the expansion. In addition, the proposed double correction provides robustness to misspecification of the moment condition. In contrast, the conventional variance estimator and the Windmeijer correction are inconsistent under misspecification. That is, the proposed double correction formula provides a convenient way to obtain improved inference under correct specification and robustness against misspecification at the same time. 
Date:  2019–08 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:1908.07821&r=all 
By:  Stéphane Lhuissier 
Abstract:  We examine Markovswitching autoregressive models where the commonly used Gaussian assumption for disturbances is replaced with a skewnormal distribution. This allows us to detect regime changes not only in the mean and the variance of a specified time series, but also in its skewness. A Bayesian framework is developed based on Markov chain Monte Carlo sampling. Our informative prior distributions lead to closedform full conditional posterior distributions, whose sampling can be efficiently conducted within a Gibbs sampling scheme. The usefulness of the methodology is illustrated with a realdata example from U.S. stock markets. 
Keywords:  Regime switching, Skewness, Gibbssampler, time series analysis, upside and downside risks. 
JEL:  C01 C11 C2 G11 
Date:  2019 
URL:  http://d.repec.org/n?u=RePEc:bfr:banfra:726&r=all 
By:  Tetsuya Kaji 
Abstract:  We provide general formulation of weak identification in semiparametric models and an efficiency concept. Weak identification occurs when a parameter is weakly regular, i.e., when it is locally homogeneous of degree zero. When this happens, consistent or equivariant estimation is shown to be impossible. We then show that there exists an underlying regular parameter that fully characterizes the weakly regular parameter. While this parameter is not unique, concepts of sufficiency and minimality help pin down a desirable one. If estimation of minimal sufficient underlying parameters is inefficient, it introduces noise in the corresponding estimation of weakly regular parameters, whence we can improve the estimators by local asymptotic RaoBlackwellization. We call an estimator weakly efficient if it does not admit such improvement. New weakly efficient estimators are presented in linear IV and nonlinear regression models. Simulation of a linear IV model demonstrates how 2SLS and optimal IV estimators are improved. 
Date:  2019–08 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:1908.10478&r=all 
By:  Huber, Martin; Solovyeva, Anna 
Abstract:  This paper considers the evaluation of direct and indirect treatment effects, also known as mediation analysis, when outcomes are only observed for a subpopulation due to sample selection or outcome attrition. For identification, we combine sequential conditional independence assumptions on the assignment of the treatment and the mediator, i.e. the variable through which the indirect effect operates, with either selection on observables/missing at random or instrumental variable assumptions on the outcome attrition process. We derive expressions for the effects of interest that are based on inverse probability weighting by specific treatment, mediator, and/or selection propensity scores. We also provide a brief simulation study and an empirical illustration based on U.S. Project STAR data that assesses the direct effect and indirect effect (via absenteeism) of smaller kindergarten classes on math test scores. 
Keywords:  Causal mechanisms; direct effects; indirect effects; causal channels; mediation analysis; causal pathways; sample selection; attrition; outcome nonresponse; inverse probability weighting; propensity score 
JEL:  C21 I21 
Date:  2018–10–22 
URL:  http://d.repec.org/n?u=RePEc:fri:fribow:fribow00496&r=all 
By:  Christophe Muller (AMSE  AixMarseille Sciences Economiques  EHESS  École des hautes études en sciences sociales  AMU  Aix Marseille Université  ECM  Ecole Centrale de Marseille  CNRS  Centre National de la Recherche Scientifique) 
Abstract:  The main two methods of endogeneity correction for linear quantile regressions with their advantages and drawbacks are reviewed and compared. Then, we discuss opportunities of alleviating the constant effect restriction of the fittedvalue approach by relaxing identification conditions. 
Keywords:  TwoStage Estimation,Quantile Regression,FittedValue Approach,Endogeneity 
Date:  2019–07 
URL:  http://d.repec.org/n?u=RePEc:hal:wpaper:halshs02272874&r=all 
By:  Gunawan, David (School of Economics, UNSW Business School, University of New South Wales, ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS).); Dang, KhueDung (School of Economics, UNSW Business School, University of New South Wales, ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS).); Quiroz, Matias (School of Economics, UNSW Business School, University of New South Wales, ARC Centre of Excellence for Mathematical, Statistical Frontiers (ACEMS) and Research Division.); Kohn, Robert (School of Economics, UNSW Business School, University of New South Wales, ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS).); Tran, MinhNgoc (ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) and Discipline of Business Analytics, University) 
Abstract:  We show how to speed up Sequential Monte Carlo (SMC) for Bayesian inference in large data problems by data subsampling. SMC sequentially updates a cloud of particles through a sequence of distributions, beginning with a distribution that is easy to sample from such as the prior and ending with the posterior distribution. Each update of the particle cloud consists of three steps: reweighting, resampling, and moving. In the move step, each particle is moved using a Markov kernel and this is typically the most computation ally expensive part, particularly when the dataset is large. It is crucial to have an efficient move step to ensure particle diversity. Our article makes two important contributions. First, in order to speed up the SMC computation, we use an approximately unbiased and efficient annealed likelihood estimator based on data subsampling. The subsampling approach is more memory effi cient than the corresponding full data SMC, which is an advantage for parallel computation. Second, we use a Metropolis within Gibbs kernel with two con ditional updates. A Hamiltonian Monte Carlo update makes distant moves for the model parameters, and a block pseudomarginal proposal is used for the particles corresponding to the auxiliary variables for the data subsampling. We demonstrate the usefulness of the methodology for estimating three gen eralized linear models and a generalized additive model with large datasets. 
Keywords:  Hamiltonian Monte Carlo; Large datasets; Likelihood annealing 
JEL:  C11 C15 
Date:  2019–04–01 
URL:  http://d.repec.org/n?u=RePEc:hhs:rbnkwp:0371&r=all 
By:  Martin Weidner; Thomas Zylkin 
Abstract:  We study the incidental parameter problem in "threeway" Poisson PseudoMaximum Likelihood ("PPML") gravity models recently recommended for identifying the effects of trade policies. Despite the number and variety of fixed effects this model entails, we confirm it is consistent for small $T$ and we show it is in fact the only estimator among a wide range of PML gravity estimators that is generally consistent in this context when $T$ is small. At the same time, asymptotic confidence intervals in fixed$T$ panels are not correctly centered at the true point estimates, and clusterrobust variance estimates used to construct standard errors are generally biased as well. We characterize each of these biases analytically and show both numerically and empirically that they are salient even for realdata settings with a large number of countries. We also offer practical remedies that can be used to obtain more reliable inferences of the effects of trade policies and other timevarying gravity variables. 
Date:  2019–09 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:1909.01327&r=all 
By:  Tore Selland Kleppe; Roman Liesenfeld; Guilherme Valle Moura; Atle Oglend 
Abstract:  We propose a factor statespace approach with stochastic volatility to model and forecast the term structure of future contracts on commodities. Our approach builds upon the dynamic 3factor NelsonSiegel model and its 4factor Svensson extension and assumes for the latent level, slope and curvature factors a Gaussian vector autoregression with a multivariate Wishart stochastic volatility process. Exploiting the conjugacy of the Wishart and the Gaussian distribution, we develop a computationally fast and easy to implement MCMC algorithm for the Bayesian posterior analysis. An empirical application to daily prices for contracts on crude oil with stipulated delivery dates ranging from one to 24 months ahead show that the estimated 4factor Svensson model with two curvature factors provides a good parsimonious representation of the serial correlation in the individual prices and their volatility. It also shows that this model has a good outofsample forecast performance. 
Date:  2019–08 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:1908.07798&r=all 
By:  Denis Belomestny; Leonid Iosipoi 
Abstract:  Markov Chain Monte Carlo methods become increasingly popular in applied mathematics as a tool for numerical integration with respect to complex and highdimensional distributions. However, application of MCMC methods to heavy tailed distributions and distributions with analytically intractable densities turns out to be rather problematic. In this paper, we propose a novel approach towards the use of MCMC algorithms for distributions with analytically known Fourier transforms and, in particular, heavy tailed distributions. The main idea of the proposed approach is to use MCMC methods in Fourier domain to sample from a density proportional to the absolute value of the underlying characteristic function. A subsequent application of the Parseval's formula leads to an efficient algorithm for the computation of integrals with respect to the underlying density. We show that the resulting Markov chain in Fourier domain may be geometrically ergodic even in the case of heavy tailed original distributions. We illustrate our approach by several numerical examples including multivariate elliptically contoured stable distributions. 
Date:  2019–09 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:1909.00698&r=all 
By:  Bram De Rock; Laurens Cherchye; Bart Smeulders 
Abstract:  Kitamura and Stoye (2018) recently proposed a nonparametric statistical test for random utility models of consumer behavior. The test is formulated in terms of linear inequality constraints and a quadratic objective function. While the nonparametric test is conceptually appealing, its practical implementation is computationally challenging. In this note, we develop a column generation approach to operationalize the test. We show that these novel computational tools generate considerable computational gains in practice, which substantially increases the empirical usefulness of Kitamura and Stoye’s statistical test. 
Keywords:  computational tools; statistical testing 
Date:  2019–08 
URL:  http://d.repec.org/n?u=RePEc:eca:wpaper:2013/292215&r=all 
By:  Huber, Martin 
Abstract:  This chapter covers different approaches to policy evaluation for assessing the causal effect of a treatment or intervention on an outcome of interest. As an introduction to causal inference, the discussion starts with the experimental evaluation of a randomized treatment. It then reviews evaluation methods based on selection on observables (assuming a quasirandom treatment given observed covariates), instrumental variables (inducing a quasirandom shift in the treatment), differenceindifferences and changesinchanges (exploiting changes in outcomes over time), as well as regression discontinuities and kinks (using changes in the treatment assignment at some threshold of a running variable). The chapter discusses methods particularly suited for data with many observations for a flexible (i.e. semi or nonparametric) modeling of treatment effects, and/or many (i.e. high dimensional) observed covariates by applying machine learning to select and control for covariates in a datadriven way. This is not only useful for tackling confounding by controlling for instance for factors jointly affecting the treatment and the outcome, but also for learning effect heterogeneities across subgroups defined upon observable covariates and optimally targeting those groups for which the treatment is most effective. 
Keywords:  Policy evaluation; treatment effects; machine learning; experiment; selection on observables; instrument; differenceindifferences; changesinchanges; regression discontinuity design; regression kink design 
JEL:  C21 C26 C29 
Date:  2019–08–12 
URL:  http://d.repec.org/n?u=RePEc:fri:fribow:fribow00504&r=all 
By:  Kosiorowski Daniel; Jerzy P. Rydlewski 
Abstract:  Results of a convincing causal statistical inference related to socioeconomic phenomena are treated as especially desired background for conducting various socioeconomic programs or government interventions. Unfortunately, quite often real socioeconomic issues do not fulfill restrictive assumptions of procedures of causal analysis proposed in the literature. This paper indicates certain empirical challenges and conceptual opportunities related to applications of procedures of data depth concept into a process of causal inference as to socioeconomic phenomena. We show, how to apply a statistical functional depths in order to indicate factual and counterfactual distributions commonly used within procedures of causal inference. The presented framework is especially useful in a context of conducting causal inference basing on official statistics, i.e., basing on already existing databases. Methodological considerations related to extremal depth, modified band depth, FraimanMuniz depth, and multivariate Wilcoxon sum rank statistic are illustrated by means of example related to a study of an impact of EU direct agricultural subsidies on a digital development in Poland in a period of 20122019. 
Date:  2019–08 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:1908.11099&r=all 
By:  G\'abor Petneh\'azi 
Abstract:  A dilated causal onedimensional convolutional neural network architecture is proposed for quantile regression. The model can forecast any arbitrary quantile, and it can be trained jointly on multiple similar time series. An application to Value at Risk forecasting shows that QCNN outperforms linear quantile regression and constant quantile estimates. 
Date:  2019–08 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:1908.07978&r=all 
By:  Francisco C. Pereira 
Abstract:  This paper introduces the concept of travel behavior embeddings, a method for rerepresenting discrete variables that are typically used in travel demand modeling, such as mode, trip purpose, education level, family type or occupation. This rerepresentation process essentially maps those variables into a latent space called the \emph{embedding space}. The benefit of this is that such spaces allow for richer nuances than the typical transformations used in categorical variables (e.g. dummy encoding, contrasted encoding, principal components analysis). While the usage of latent variable representations is not new per se in travel demand modeling, the idea presented here brings several innovations: it is an entirely data driven algorithm; it is informative and consistent, since the latent space can be visualized and interpreted based on distances between different categories; it preserves interpretability of coefficients, despite being based on Neural Network principles; and it is transferrable, in that embeddings learned from one dataset can be reused for other ones, as long as travel behavior keeps consistent between the datasets. The idea is strongly inspired on natural language processing techniques, namely the word2vec algorithm. Such algorithm is behind recent developments such as in automatic translation or next word prediction. Our method is demonstrated using a model choice model, and shows improvements of up to 60\% with respect to initial likelihood, and up to 20% with respect to likelihood of the corresponding traditional model (i.e. using dummy variables) in outofsample evaluation. We provide a new Python package, called PyTre (PYthon TRavel Embeddings), that others can straightforwardly use to replicate our results or improve their own models. Our experiments are themselves based on an open dataset (swissmetro). 
Date:  2019–08 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:1909.00154&r=all 
By:  Zheng Tracy Ke; Bryan T. Kelly; Dacheng Xiu 
Abstract:  We introduce a new textmining methodology that extracts sentiment information from news articles to predict asset returns. Unlike more common sentiment scores used for stock return prediction (e.g., those sold by commercial vendors or built with dictionarybased methods), our supervised learning framework constructs a sentiment score that is specifically adapted to the problem of return prediction. Our method proceeds in three steps: 1) isolating a list of sentiment terms via predictive screening, 2) assigning sentiment weights to these words via topic modeling, and 3) aggregating terms into an articlelevel sentiment score via penalized likelihood. We derive theoretical guarantees on the accuracy of estimates from our model with minimal assumptions. In our empirical analysis, we textmine one of the most actively monitored streams of news articles in the financial system—the Dow Jones Newswires—and show that our supervised sentiment model excels at extracting returnpredictive signals in this context. 
JEL:  C53 C58 G10 G11 G12 G14 G17 
Date:  2019–08 
URL:  http://d.repec.org/n?u=RePEc:nbr:nberwo:26186&r=all 
By:  Gutierrez, Federico H. 
Abstract:  This paper proposes a simple solution to the independence of irrelevant alternatives (IIA) problem in Choo and Siow (2006) model, overcoming what is probably the main limitation of this approach. The solution consists of assuming matchspecific rather than choicespecific random preferences. The original marriage matching function gets modified by an adjustment factor that improves its empirical properties. Using the American Community Survey, I show that the new approach yields significantly different results affecting the qualitative conclusions of the analysis. The proposed solution to the IIA problem applies to other settings in which the relative "supply" of choices is observable. 
Keywords:  Independence of irrelevant alternatives,marriage market,transferable utility 
JEL:  J12 J16 J10 
Date:  2019 
URL:  http://d.repec.org/n?u=RePEc:zbw:glodps:387&r=all 
By:  Du Nguyen 
Abstract:  We show Vector Autoregressive Moving Average models with scalar Moving Average components could be estimated by generalized least square (GLS) for each fixed moving average polynomial. The conditional variance of the GLS model is the concentrated covariant matrix of the moving average process. Under GLS the likelihood function of these models has similar format to their VAR counterparts. Maximum likelihood estimate can be done by optimizing with gradient over the moving average parameters. These models are inexpensive generalizations of Vector Autoregressive models. We discuss a relationship between this result and the BorodinOkounkov formula in operator theory. 
Date:  2019–09 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:1909.00386&r=all 
By:  Yuya Sasaki; Yulong Wang 
Abstract:  We develop a new extreme value theory for panel data and use it to construct asymptotically valid confidence intervals (CIs) for conditional tail features such as conditional extreme quantile and conditional tail index. As a byproduct, we also construct CIs for tail features of the coefficients in the random coefficient regression model. The new CIs are robustly valid without parametric assumptions and have excellent small sample coverage and length properties. Applying the proposed method, we study the tail risk of the monthly U.S. stock returns and find that (i) the left tail features of stock returns and those of the FamaFrench regression residuals heavily depend on other stock characteristics such as stock size; and (ii) the alpha's and beta's are strongly heterogeneous across stocks in the FamaFrench regression. These findings suggest that the FamaFrench model is insufficient to characterize the tail behavior of stock returns. 
Date:  2019–08 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:1909.00294&r=all 
By:  Goller, Daniel (University of St. Gallen); Lechner, Michael (University of St. Gallen); Moczall, Andreas (Institute for Employment Research (IAB), Nuremberg); Wolff, Joachim (Institute for Employment Research (IAB), Nuremberg) 
Abstract:  Matchingtype estimators using the propensity score are the major workhorse in active labour market policy evaluation. This work investigates if machine learning algorithms for estimating the propensity score lead to more credible estimation of average treatment effects on the treated using a radius matching framework. Considering two popular methods, the results are ambiguous: We find that using LASSO based logit models to estimate the propensity score delivers more credible results than conventional methods in small and medium sized high dimensional datasets. However, the usage of Random Forests to estimate the propensity score may lead to a deterioration of the performance in situations with a low treatment share. The application reveals a positive effect of the training programme on days in employment for longterm unemployed. While the choice of the "first stage" is highly relevant for settings with low number of observations and few treated, machine learning and conventional estimation becomes more similar in larger samples and higher treatment shares. 
Keywords:  programme evaluation, active labour market policy, causal machine learning, treatment effects, radius matching, propensity score 
JEL:  J68 C21 
Date:  2019–08 
URL:  http://d.repec.org/n?u=RePEc:iza:izadps:dp12526&r=all 
By:  Huber, Martin 
Abstract:  Mediation analysis aims at evaluating the causal mechanisms through which a treatment or intervention affects an outcome of interest. The goal is to disentangle the total treatment effect into an indirect effect operating through one or several observed intermediate variables, the socalled mediators, as well as a direct effect reflecting any impact not captured by the observed mediator(s). This paper reviews methodological advancements with a particular focus on applications in economics. It defines the parameters of interest, covers various identification strategies, e.g. based on control variables or instruments, and presents sensitivity checks. Furthermore, it discusses several extensions of the standard mediation framework, such as multivalued treatments, mismeasured mediators, and outcome attrition. 
Keywords:  Mediation; direct effect; indirect effect; sequential conditional independence; instrument 
JEL:  C21 
Date:  2019–01–01 
URL:  http://d.repec.org/n?u=RePEc:fri:fribow:fribow00500&r=all 
By:  Yixiao Li; Gloria Lin; Thomas Lau; Ruochen Zeng 
Abstract:  The objective of the changepoint detection is to discover the abrupt property changes lying behind the timeseries data. In this paper, we firstly summarize the definition and indepth implication of the changepoint detection. The next stage is to elaborate traditional and some alternative modelbased changepoint detection algorithms. Finally, we try to go a bit further in the theory and look into future research directions. 
Date:  2019–08 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:1908.07136&r=all 