nep-ecm New Economics Papers
on Econometrics
Issue of 2020‒11‒30
thirteen papers chosen by
Sune Karlsson
Örebro universitet

  1. Rank-Based Testing for Semiparametric VAR Models: a measure transportation approach By Marc Hallin; Davide La Vecchia; Hang Liu
  2. Gaussian Transforms Modeling and the Estimation of Distributional Regression Functions By Richard Spady; Sami Stouli
  3. Sequentially Estimating Approximate Conditional Mean Using the Extreme Learning Machine By LIJUAN HUO; JIN SEO CHO
  4. Weak Identification in Discrete Choice Models By David T. Frazier; Eric Renault; Lina Zhang; Xueyan Zhao
  5. A Generalized Focused Information Criterion for GMM By Minsu Chang; Francis J. DiTraglia
  6. Finite sample forecast properties and window length under breaks in cointegrated systems By Luca Nocciola
  7. Mostly Harmless Machine Learning: Learning Optimal Instruments in Linear IV Models By Jiafeng Chen; Daniel L. Chen; Greg Lewis
  8. Covariates impacts in spatial autoregressive models for compositional data By Thomas-Agnan, Christine; Laurent, Thibault; Ruiz-Gazen, Anne
  9. Exploration of model performances in the presence of heterogeneous preferences and random effects utilities awareness By Gusarov, N.; Talebijmalabad, A.; Joly, I.
  10. Measuring and Hedging Geopolitical Risk By Robert F. Engle; Susana Campos-Martins
  11. "Dynamic Factor, Leverage and Realized Covariances in Multivariate Stochastic Volatility" By Yuta Yamauchi; Yasuhiro Omori
  12. Unified Extreme Value Estimation for Heterogeneous Data By Einmahl, John; He, Y.
  13. Identifying Causal Effects in Experiments with Social Interactions and Non-compliance By Francis J. DiTraglia; Camilo Garcia-Jimeno; Rossa O'Keeffe-O'Donovan; Alejandro Sanchez-Becerra

  1. By: Marc Hallin; Davide La Vecchia; Hang Liu
    Abstract: We develop a class of tests for semiparametric vector autoregressive (VAR) models with unspecified innovation densities, based on the recent measure-transportation-based concepts of multivariate center-outward ranks and signs. We show that these concepts, combined with Le Cam's asymptotic theory of statistical experiments, yield novel testing procedures, which (a) are valid under a broad class of innovation densities (possibly non-elliptical, skewed, and/or with infinite moments), (b) are optimal (locally asymptotically maximin or most stringent) at selected ones, and (c) are robust against additive outliers. In order to do so, we establish a Hajek asymptotic representation result, of independent interest, for a general class of center-outward rank-based serial statistics. As an illustration, we consider the problems of testing the absence of serial correlation in multiple-output and possibly non-linear regression (an extension of the classical Durbin-Watson problem) and the sequential identification of the order p of a vector autoregressive (VAR(p)) model. A Monte Carlo comparative study of our tests and their routinely-applied Gaussian competitors demonstrates the benefits (in terms of size, power, and robustness) of our methodology; these benefits are particularly significant in the presence of asymmetric and leptokurtic innovation densities. A real data application concludes the paper.
    Keywords: Multivariate ranks; Distribution-freeness; Hájek representation; Local asymptotic normality; Durbin-Watson; VAR order identification
  2. By: Richard Spady; Sami Stouli
    Abstract: Conditional distribution functions are important statistical objects for the analysis of a wide class of problems in econometrics and statistics. We propose flexible Gaussian representations for conditional distribution functions and give a concave likelihood formulation for their global estimation. We obtain solutions that satisfy the monotonicity property of conditional distribution functions, including under general misspecification and in finite samples. A Lasso-type penalized version of the corresponding maximum likelihood estimator is given that expands the scope of our estimation analysis to models with sparsity. Inference and estimation results for conditional distribution, quantile and density functions implied by our representations are provided and illustrated with an empirical example and simulations.
    Date: 2020–11
  3. By: LIJUAN HUO (Beijing Institute of Technology); JIN SEO CHO (Yonsei Univ)
    Abstract: This study applies the Wald test statistic assisted by the extreme learning machine (ELM) to test for model misspecification. When testing for model misspecification of conditional mean, the omnibus test statistics weakly converge to a Gaussian stochastic process under the null that makes their application inconvenient. We overcome this by applying the ELM to the Wald test statistic defined by the functional regression and also apply it to a sequential testing procedure to estimate an approximate conditional expectation. By conducting extensive Monte Carlo experiments, we evaluate its performance and verify that the sequential WELM testing procedure estimates the most parsimonious conditional mean equation consistently if the candidate polynomial models are correctly specified; and further it consistently rejects all candidate models if all of them are misspecified.
    Keywords: specification testing; conditional mean; omnibus test; Gaussian process; extreme learning machine; sequential testing procedure.
    Date: 2020–10
  4. By: David T. Frazier; Eric Renault; Lina Zhang; Xueyan Zhao
    Abstract: We study the impact of weak identification in discrete choice models, and provide insights into the determinants of identification strength in these models. Using these insights, we propose a novel test that can consistently detect weak identification in many commonly applied discrete choice models, such as probit, logit, and many of their extensions. Furthermore, we demonstrate that if the null hypothesis that identification is weak can be rejected, Wald-based inference can be carried out using standard formulas and critical values. A Monte Carlo analysis compares our proposed testing approach against commonly applied weak identification tests. The results simultaneously demonstrate the good performance of our approach and the fundamental failure of conventionally applied, i.e., linear, weak identification tests in this context. We compare our testing approach to those commonly applied in the literature within two empirical examples: married women labor force participation, and US food aid and civil conflicts.
    Date: 2020–11
  5. By: Minsu Chang (Department of Economics Georgetown University); Francis J. DiTraglia (Department of Economics University of Oxford)
    Abstract: This paper proposes a criterion for simultaneous GMM model and moment selection: the generalized focused information criterion (GFIC). Rather than attempting to identify the "true" specification, the GFIC chooses from a set of potentially mis-specified moment conditions and parameter restrictions to minimize the mean-squared error (MSE) of a user-specified target parameter. The intent of the GFIC is to formalize a situation common in applied practice. An applied researcher begins with a set of fairly weak "baseline" assumptions, assumed to be correct, and must decide whether to impose any of a number of stronger, more controversial "suspect" assumptions that yield parameter restrictions, additional moment conditions, or both. Provided that the baseline assumptions identify the model, we show how to construct an asymptotically unbiased estimator of the asymptotic MSE to select over these suspect assumptions: the GFIC. We go on to provide results for post-selection inference and model averaging that can be applied both to the GFIC and various alternative selection criteria. To illustrate how our criterion can be used in practice, we specialize the GFIC to the problem of selecting over exogeneity assumptions and lag lengths in a dynamic panel model, and show that it performs well in simulations. We conclude by applying the GFIC to a dynamic panel data model for the price elasticity of cigarette demand.
    Date: 2020–11
  6. By: Luca Nocciola
    Abstract: We show that extending the estimation window prior to structural breaks in cointegrated systems can be beneficial for forecasting performance and highlight under which conditions. In doing so, we generalize the Pesaran & Timmermann (2005)'s forecast error decomposition and show that it depends on four terms: 1) a period ahead risk; 2) a bias due to a conditional mean shift; 3) a bias due to a variance mismatch; 4) a gap term valid only conditionally. We also derive new expressions for the estimators of the adjustment matrix and a constant, which are auxiliary to the decomposition. Finally, we introduce new simulation based estimators for the finite sample forecast properties which are based on the derived decomposition. Our finding points out that, in some cases, we can neglect parameter instability by extending the window backward and be insured against higher forecast risk under this model class as well, generalizing Pesaran & Timmermann (2005)'s result. Our result gives renewed importance to break tests, in order to distinguish cases when break-neglection is (not) appropriate.
    Keywords: Finite sample forecast properties; MSE; structural breaks; cointegration; expanding window estimator
  7. By: Jiafeng Chen; Daniel L. Chen; Greg Lewis
    Abstract: We provide some simple theoretical results that justify incorporating machine learning in a standard linear instrumental variable setting, prevalent in empirical research in economics. Machine learning techniques, combined with sample-splitting, extract nonlinear variation in the instrument that may dramatically improve estimation precision and robustness by boosting instrument strength. The analysis is straightforward in the absence of covariates. The presence of linearly included exogenous covariates complicates identification, as the researcher would like to prevent nonlinearities in the covariates from providing the identifying variation. Our procedure can be effectively adapted to account for this complication, based on an argument by Chamberlain (1992). Our method preserves standard intuitions and interpretations of linear instrumental variable methods and provides a simple, user-friendly upgrade to the applied economics toolbox. We illustrate our method with an example in law and criminal justice, examining the causal effect of appellate court reversals on district court sentencing decisions.
    Date: 2020–11
  8. By: Thomas-Agnan, Christine; Laurent, Thibault; Ruiz-Gazen, Anne
    Abstract: Spatial simultaneous autoregressive models have been adapted to model data with both a geographic and a compositional nature. Interpretation of parameters in such a model is intricate. Indeed, due to their spatial dimension, this interpretation must focus on impacts rather than parameters when they involve a lag of the dependent variable and because of their compositional nature, this interpretation should be based on elasticities (or semi-elasticities). We provide here exact formulas for the evaluation of these elasticity-based impact measures which have been only approximated so far in some applications. We also discuss their decomposition into direct and indirect impacts
    Keywords: compositional regression model, marginal effects, simplicial derivative, elasticity, semi-elasticity
    JEL: C10 C39 C65 M31 Q15
    Date: 2020–11–17
  9. By: Gusarov, N.; Talebijmalabad, A.; Joly, I.
    Abstract: This work is a cross-disciplinary study of econometrics and machine learning (ML) models applied to consumer choice preference modelling. To bridge the interdisciplinary gap, a simulation and theorytesting framework is proposed. It incorporates all essential steps from hypothetical setting generation to the comparison of various performance metrics. The flexibility of the framework in theory-testing and models comparison over economics and statistical indicators is illustrated based on the work of Michaud, Llerena and Joly (2012). Two datasets are generated using the predefined utility functions simulating the presence of homogeneous and heterogeneous individual preferences for alternatives’ attributes. Then, three models issued from econometrics and ML disciplines are estimated and compared. The study demonstrates the proposed methodological approach’s efficiency, successfully capturing the differences between the models issued from different fields given the homogeneous or heterogeneous consumer preferences.
    JEL: C25 C45 C52 C80 C90
    Date: 2020
  10. By: Robert F. Engle (New York Stern School of Business); Susana Campos-Martins (Nuffield College, University of Oxford and NIPE)
    Abstract: Geopolitical events can impact volatilities of all assets, asset classes, sectors and countries. It is shown that innovations to volatilities are correlated across assets and therefore can be used to measure and hedge geopolitical risk. We introduce a definition of geopolitical risk which is based on volatility shocks to a wide range of financial market prices. To measure geopolitical risk, we propose a statistical model for the magnitude of the common volatility shocks. Accordingly, a test and estimation methods are developed and studied using both empirical and simulated data. We provide a novel explanation for why idiosyncratic volatilities comove based on a new way to formulate multiplicative factors. Finally, we propose a new criterion for portfolio optimality which is intended to reduce the exposure to geopolitical risk.
    Date: 2020
  11. By: Yuta Yamauchi (Graduate School of Economics, The University of Tokyo); Yasuhiro Omori (Faculty of Economics, The University of Tokyo)
    Abstract: In the stochastic volatility models for multivariate daily stock returns, it has been found that the estimates of parameters become unstable as the dimension of returns increases. To solve this problem, we focus on the factor structure of multiple returns and consider two additional sources of information: first, the realized stock index associated with the market factor, and second, the realized covariance matrix calculated from high frequency data. The proposed dynamic factor model with the leverage effect and realized measures is applied to ten of the top stocks composing the exchange traded fund linked with the investment return of the S&P500 index and the model is shown to have a stable advantage in portfolio performance.
    Date: 2020–11
  12. By: Einmahl, John (Tilburg University, Center For Economic Research); He, Y. (Tilburg University, Center For Economic Research)
    Keywords: Power law;; Extreme values; Heterogeneous data; COVID-19; Inequality
    Date: 2020
  13. By: Francis J. DiTraglia (Department of Economics University of Oxford); Camilo Garcia-Jimeno (Federal Reserve Bank of Chicago); Rossa O'Keeffe-O'Donovan (Department of Economics University of Oxford); Alejandro Sanchez-Becerra (University of Pennsylvania)
    Abstract: This paper shows how to use a randomized saturation experimental design to identify and estimate causal effects in the presence of social interactions--one person's treatment may affect another's outcome--and one-sided non-compliance--subjects can only be offered treatment, not compelled to take it up. Two distinct causal effects are of interest in this setting: direct effects quantify how a person's own treatment changes her outcome, while indirect effects quantify how her peers' treatments change her outcome. We consider the case in which social interactions occur only within known groups, and take-up decisions do not depend on peers' offers. In this setting we point identify local average treatment effects, both direct and indirect, in a flexible random coefficients model that allows for both heterogenous treatment effects and endogeneous selection into treatment. We go on to propose a feasible estimator that is consistent and asymptotically normal as the number and size of groups increases.
    Date: 2020–11

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