nep-ecm New Economics Papers
on Econometrics
Issue of 2020‒01‒27
eight papers chosen by
Sune Karlsson
Örebro universitet

  1. A Higher-Order Correct Fast Moving-Average Bootstrap for Dependent Data By Davide La Vecchia; Alban Moor; Olivier Scaillet
  2. Temporal-Difference estimation of dynamic discrete choice models By Karun Adusumilli; Dita Eckardt
  3. Sparse Covariance Estimation in Logit Mixture Models By Youssef M Aboutaleb; Mazen Danaf; Yifei Xie; Moshe Ben-Akiva
  4. ResLogit: A residual neural network logit model By Melvin Wong; Bilal Farooq
  5. Causality in econometric modeling. From theory to structural causal modeling By MOUCHART Michel,; ORSI Renzo,; WUNSCH Guillaume,
  6. Forward Variable Selection for Sparse Ultra-High Dimensional Generalized Varying Coefficient Models By Honda, Toshio; Lin, Chien-Tong
  7. Understanding Program Complementarities: Estimating the Dynamic Effects of a Training Program with Multiple Alternatives By Dalla-Zuanna, Antonio; Liu, Kai
  8. Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions By Andrea Carriero; Todd E. Clark; Massimiliano Marcellino

  1. By: Davide La Vecchia; Alban Moor; Olivier Scaillet
    Abstract: We develop and implement a novel fast bootstrap for dependent data. Our scheme is based on the i.i.d. resampling of the smoothed moment indicators. We characterize the class of parametric and semi-parametric estimation problems for which the method is valid. We show the asymptotic refinements of the proposed procedure, proving that it is higher-order correct under mild assumptions on the time series, the estimating functions, and the smoothing kernel. We illustrate the applicability and the advantages of our procedure for Generalized Empirical Likelihood estimation. As a by-product, our fast bootstrap provides higher-order correct asymptotic confidence distributions. Monte Carlo simulations on an autoregressive conditional duration model provide numerical evidence that the novel bootstrap yields higher-order accurate confidence intervals. A real-data application on dynamics of trading volume of stocks illustrates the advantage of our method over the routinely-applied first-order asymptotic theory, when the underlying distribution of the test statistic is skewed or fat-tailed.
    Date: 2020–01
  2. By: Karun Adusumilli; Dita Eckardt
    Abstract: We propose a new algorithm to estimate the structural parameters in dynamic discrete choice models. The algorithm is based on the conditional choice probability approach, but uses the idea of Temporal-Difference learning from the Reinforcement Learning literature to estimate the different terms in the value functions. In estimating these terms with functional approximations using basis functions, our approach has the advantage of naturally allowing for continuous state spaces. Furthermore, it does not require specification of transition probabilities, and even estimation of choice probabilities can be avoided using a recursive procedure. Computationally, our algorithm only requires solving a low dimensional linear equation. We find that it is substantially faster than existing approaches when the finite dependence property does not hold, and comparable in speed to approaches that exploit this property. For the estimation of dynamic games, our procedure does not require integrating over the actions of other players, which further heightens the computational advantage. We show that our estimator is consistent, and efficient under discrete state spaces. In settings with continuous states, we propose easy to implement locally robust corrections in order to achieve parametric rates of convergence. Preliminary Monte Carlo simulations confirm the workings of our algorithm.
    Date: 2019–12
  3. By: Youssef M Aboutaleb; Mazen Danaf; Yifei Xie; Moshe Ben-Akiva
    Abstract: This paper introduces a new data-driven methodology for estimating sparse covariance matrices of the random coefficients in logit mixture models. Researchers typically specify covariance matrices in logit mixture models under one of two extreme assumptions: either an unrestricted full covariance matrix (allowing correlations between all random coefficients), or a restricted diagonal matrix (allowing no correlations at all). Our objective is to find optimal subsets of correlated coefficients for which we estimate covariances. We propose a new estimator, called MISC, that uses a mixed-integer optimization (MIO) program to find an optimal block diagonal structure specification for the covariance matrix, corresponding to subsets of correlated coefficients, for any desired sparsity level using Markov Chain Monte Carlo (MCMC) posterior draws from the unrestricted full covariance matrix. The optimal sparsity level of the covariance matrix is determined using out-of-sample validation. We demonstrate the ability of MISC to correctly recover the true covariance structure from synthetic data. In an empirical illustration using a stated preference survey on modes of transportation, we use MISC to obtain a sparse covariance matrix indicating how preferences for attributes are related to one another.
    Date: 2020–01
  4. By: Melvin Wong; Bilal Farooq
    Abstract: We present a Residual Logit (ResLogit) model for seamlessly integrating a data-driven Deep Neural Network (DNN) architecture in the random utility maximization paradigm. DNN models such as the Multi-layer Perceptron (MLP) have shown remarkable success in modelling complex data accurately, but recent studies have consistently demonstrated that their black-box properties are incompatible with discrete choice analysis for the purpose of interpreting decision making behaviour. Our proposed machine learning choice model is a departure from the conventional feed-forward MLP framework by using a dynamic residual neural network learning based approach. Our proposed method can be formulated as a Generalized Extreme Value (GEV) random utility maximization model for greater flexibility in capturing unobserved heterogeneity. It can generate choice model structures where the covariance between random utilities is estimated and incorporated into the random error terms, allowing for a richer set of higher-order substitution patterns than a standard logit might be able to achieve. We describe the process of our model estimation and examine the relative empirical performance and econometric implications on two mode choice experiments. We analyzed the behavioural and theoretical properties of our methodology. We showed how model interpretability is possible, while also capturing the underlying complex and unobserved behavioural heterogeneity effects in the residual covariance matrices.
    Date: 2019–12
  5. By: MOUCHART Michel, (Université catholique de Louvain); ORSI Renzo, (University of Bologna); WUNSCH Guillaume, (Université catholique de Louvain)
    Abstract: This paper examines different approaches for assessing causality as typically followed in econometrics and proposes a constructive perspective for improving statistical models elaborated in view of causal analysis. Without attempting to be exhaustive, this paper examines some of these approaches. Traditional structural modeling is first discussed. A distinction is then drawn between model-based and design-based approaches. Some more recent developments are examined next, namely history-friendly simulation and information-theory based approaches. Finally, in a constructive perspective, structural causal modeling (SCM) is presented, based on the concepts of mechanism and sub-mechanisms, and of recursive decomposition of the joint distribution of variables. This modeling strategy endeavors at representing the structure of the underlying data generating process. It operationalizes the concept of causation through the ordering and role-function of the variables in each of the intelligible sub-mechanisms.
    Keywords: structural modeling, exogeneity, causality, model-based and design-based approaches, recursive decomposition, history-friendly simulation, transfer entropy
    JEL: C15 C18 C51 C54
    Date: 2020–01–01
  6. By: Honda, Toshio; Lin, Chien-Tong
    Abstract: In this paper we propose a forward variable selection procedure for feature screening in ultra-high dimensional generalized varying coefficient models. We employ regression spline to approximate coefficient functions and then maximize the log-likelihood to select an additional relevant covariate sequentially. If we decide we do not significantly improve the log-likelihood any more by selecting any new covariates from our stopping rule, we terminate the forward procedure and give our estimate of relevant covariates. The effect of the size of the current model has been overlooked in stopping rules for sequential procedures for high-dimensional models. Our stopping rule takes into account the size of the current model suitably. Our forward procedure has screening consistency and some other desirable properties under regularity conditions. We also present the results of numerical studies to show its good finite sample performances.
    Keywords: B-spline basis, forward procedure, MLE, screening consistency, stopping rule, varying coefficient mode
    Date: 2020–01
  7. By: Dalla-Zuanna, Antonio (Institute for Fiscal Study and Norwegian School of Economics); Liu, Kai (University of Cambridge)
    Abstract: In this paper we estimate the causal effect of a training program for disadvantaged youths on their long-run labor market outcomes. Individuals receive lottery offers to participate in the program, but are allowed to choose when to leave the program and to participate in alternative programs. We consider a multistage decision setting, where individuals sequentially select which program to participate in at every stage. The standard IV estimator using initial random assignment as instrumental variable identifies a weighted average of the effects of the treatment for subgroups of individuals differing in terms of potential duration of participation and choice of alternative programs. We estimate a sequential choice model that allows us to estimate the effect of the treatment for these different subgroups separately. We use the estimated model to investigate the dynamic complementarity between different training programs and explore program targeting to improve the cost-effectiveness relative to the existing program.
    Keywords: training, program evaluation, dynamic treatment effects, experiment
    JEL: J0 H4
    Date: 2019–12
  8. By: Andrea Carriero; Todd E. Clark; Massimiliano Marcellino (European University Institute; Universität Commerciale Luigi Bocconi; National Bureau of Economic Research; Centre for Economic Policy Research (CEPR); Universität degli Studi di Firenze; Bocconi University)
    Abstract: A rapidly growing body of research has examined tail risks in macroeconomic outcomes. Most of this work has focused on the risks of significant declines in GDP, and has relied on quantile regression methods to estimate tail risks. In this paper we examine the ability of Bayesian VARs with stochastic volatility to capture tail risks in macroeconomic forecast distributions and outcomes. We consider both a conventional stochastic volatility specification and a specification featuring a common volatility factor that is a function of past financial conditions. Even though the conditional predictive distributions from the VAR models are symmetric, our estimated models featuring time-varying volatility yield more time variation in downside risk as compared to upside risk—a feature highlighted in other work that has advocated for quantile regression methods or focused on asymmetric conditional distributions. Overall, the BVAR models perform comparably to quantile regression for estimating tail risks, with, in addition, some gains in standard point and density forecasts.
    Keywords: forecasting; downside risk; asymmetries
    JEL: C53 E17 E37 F47
    Date: 2020–01–16

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