nep-ecm New Economics Papers
on Econometrics
Issue of 2023‒09‒18
fifteen papers chosen by
Sune Karlsson, Örebro universitet

  1. Causal Interpretation of Linear Social Interaction Models with Endogenous Networks By Tadao Hoshino
  2. Quantile Regression with an Andogenous Misclassified Binary Regressor By Carlos Lamarche
  3. A two-step estimator for multilevel latent class analysis with covariates By Di Mari, Roberto; Bakk, Zsuzsa; Oser, Jennifer; Kuha, Jouni
  4. Addressing Bias in Politician Characteristic Regression Discontinuity Designs By Torres, Santiago
  5. Testing Partial Instrument Monotonicity By Hongyi Jiang; Zhenting Sun
  6. A Comparison of Neural Networks and Bayesian MCMC for the Heston Model Estimation (Forget Statistics - Machine Learning is Sufficient!) By Jiří Witzany; Milan Fičura
  7. Quantile Tool Box Measures for Empirical Analysis and for Testing Distributional Comparisons in Direct Distribution-Free Fashion By Charles Beach
  8. Close Elections Regression Discontinuity Designs in Multi-seat Systems By Torres, Santiago
  9. Discussion of ‘Multi-scale Fisher’s independence test for multivariate dependence’ By Schrab, Antonin; Jitkrittum, Wittawat; Szabo, Zoltan; Sejdinovic, Dino; Gretton, Arthur
  10. On sparsity, power-law, and clustering properties of graphex processes By Caron, François; Panero, Francesca; Rousseau, Judith
  11. Closer to Finding Yeti By Tomas Micko; Alexander Karsay; Zuzana Mucka; Lucia Sramkova
  12. Estimating HANK for Central Banks By Sushant Acharya; William Chen; Marco Del Negro; Keshav Dogra; Aidan Gleich; Shlok Goyal; Donggyu Lee; Ethan Matlin; Reca Sarfati; Sikata Sengupta
  13. Stress Testing Structural Models of Unobserved Heterogeneity: Robust Inference on Optimal Nonlinear Pricing By Aaron Bodoh-Creed; Brent Hickman; John List; Ian Muir; Gregory Sun
  14. A New Approach to Overcoming Zero Trade in Gravity Models to Avoid Indefinite Values in Linear Logarithmic Equations and Parameter Verification Using Machine Learning By Mikrajuddin Abdullah
  15. Measuring systemic financial stress and its risks for growth By Chavleishvili, Sulkhan; Kremer, Manfred

  1. By: Tadao Hoshino
    Abstract: This study investigates the causal interpretation of linear social interaction models in the presence of endogeneity in network formation under a heterogeneous treatment effects framework. We consider an experimental setting in which individuals are randomly assigned to treatments while no interventions are made for the network structure. We show that running a linear regression ignoring network endogeneity is not problematic for estimating the average direct treatment effect. However, it leads to sample selection bias and negative-weights problem for the estimation of the average spillover effect. To overcome these problems, we propose using potential peer treatment as an instrumental variable (IV), which is automatically a valid IV for actual spillover exposure. Using this IV, we examine two IV-based estimands and demonstrate that they have a local average treatment-effect-type causal interpretation for the spillover effect.
    Date: 2023–08
  2. By: Carlos Lamarche (Department of Economics, University of Kentucky)
    Abstract: Recent work on the conditional mean model offers the possibility of addressing misreporting of participation in social programs, which is common and has increased in all major surveys. However, researchers who employ quantile regression continue to encounter challenges in terms of estimation and statistical inference. In this work, we propose a simple two-step estimator for a quantile regression model with endogenous misreporting. The identification of the model uses a parametric first stage and information related to participation and misreporting. We show that the estimator is consistent and asymptotically normal. We also establish that a bootstrap procedure is asymptotically valid for approximating the distribution of the estimator. Simulation studies show the small sample behavior of the estimator in comparison with other methods, including a new three-step estimator. Finally, we illustrate the novel approach using U.S. survey data to estimate the intergenerational effect of mother’s participation on welfare on daughter’s adult income.
    JEL: C21 C25 I32
    Date: 2023–09
  3. By: Di Mari, Roberto; Bakk, Zsuzsa; Oser, Jennifer; Kuha, Jouni
    Abstract: We propose a two-step estimator for multilevel latent class analysis (LCA) with covariates. The measurement model for observed items is estimated in its first step, and in the second step covariates are added in the model, keeping the measurement model parameters fixed. We discuss model identification, and derive an Expectation Maximization algorithm for efficient implementation of the estimator. By means of an extensive simulation study we show that (1) this approach performs similarly to existing stepwise estimators for multilevel LCA but with much reduced computing time, and (2) it yields approximately unbiased parameter estimates with a negligible loss of efficiency compared to the one-step estimator. The proposal is illustrated with a cross-national analysis of predictors of citizenship norms.
    Keywords: multilevel latent class analysis; covariates; stepwise estimators; pseudo ML; European Union grant (ERC; PRD; project number 101077659).
    JEL: C1
    Date: 2023–08–06
  4. By: Torres, Santiago (Universidad de los Andes, Facultad de Economía)
    Abstract: Politician characteristic regression discontinuity (PCRD) designs are a popular strategy when attempting to casually link a specific trait of an elected politician with a given outcome. However, recent research has revealed that this methodology often fails to retrieve the target causal effect¿a problem also known as the PCRD estimation bias. In this paper, I provide a new econometric framework to address this limitation in applied research. First, I propose a covariate-adjusted local polynomial estimator that corrects for the PCRD estimation bias provided all relevant confounders are observed. I then leverage the statistical properties of this estimator to propose several decompositions of the bias term and discuss their potential applications. Next, I devise a strategy to assess the robustness of the new estimator to omitted confounders that could potentially invalidate results. Finally, I illustrate these methods through an application: a PCRD aimed at evaluating the impact of female leadership during the COVID-19 pandemic.
    Keywords: Regression discontinuity designs; Close elections; Bias correction; Sensitivity analysis.
    JEL: C18 C51 P00
    Date: 2023–08–25
  5. By: Hongyi Jiang; Zhenting Sun
    Abstract: When multi-dimensional instruments are used to identify and estimate causal effects, the monotonicity condition may not hold due to heterogeneity in the population. Under a partial monotonicity condition, which only requires the monotonicity to hold for each instrument separately holding all the other instruments fixed, the 2SLS estimand can still be a positively weighted average of LATEs. In this paper, we provide a simple nonparametric test for partial instrument monotonicity. We demonstrate the good finite sample properties of the test through Monte Carlo simulations. We then apply the test to monetary incentives and distance from results centers as instruments for the knowledge of HIV status.
    Date: 2023–08
  6. By: Jiří Witzany; Milan Fičura
    Abstract: The main goal of this paper is to compare the classical MCMC estimation method with a universal Neural Network (NN) approach to estimate unknown parameters of the Heston stochastic volatility model given a series of observable asset returns. The main idea of the NN approach is to generate a large training synthetic dataset with sampled parameter vectors and the return series conditional on the Heston model. The NN can then be trained reverting the input and output, i.e. setting the return series, or rather a set of derived generalized moments as the input features and the parameters as the target. Once the NN has been trained, the estimation of parameters given observed return series becomes very efficient compared to the MCMC algorithm. Our empirical study implements the MCMC estimation algorithm and demonstrates that the trained NN provides more precise and substantially faster estimations of the Heston model parameters. We discuss some other advantages and disadvantages of the two methods, and hypothesize that the universal NN approach can in general give better results compared to the classical statistical estimation methods for a wide class of models.
    Keywords: Heston model, parameter estimation, neural networks, MCMC
    JEL: C45 C63 G13
    Date: 2023–07–11
  7. By: Charles Beach
    Abstract: This paper provides a set of tool box measures for flexibly describing distributional changes and empirically implementing several dominance criteria for social welfare comparisons and broad income inequality comparisons. Dominance criteria are expressed in terms of vectors of quantile statistics based on income shares and quantile means. Asymptotic variances and covariances of these sample ordinates are established from a Quantile Function Approach that provides a framework for direct statistical inference on these vectors. And practical empirical criteria are forwarded for using formal statistical inference tests to reach conclusions about ranking social welfare and inequality between distributions. Examples include rank dominance, Lorenz dominance, generalized Lorenz dominance, income polarization, and distributional distance dominance between income groups.
    Keywords: welfare testing, inequality dominance, dominance testing
    JEL: C12 C46 D31 D63
    Date: 2023–09
  8. By: Torres, Santiago (Universidad de los Andes)
    Abstract: This article presents a general framework for using continuity-based regression discontinuity designs as an identification strategy in multi-seat electoral contests. First, I extend singlewinner- close-race designs by developing precise definitions of electoral tightness in elections where multiple winners are possible. These narrowness measures can be used to formulate forcing variables for conducting regression discontinuity designs. Moreover, I show that it is possible to construct different running variables to identify different (local) causal effects. I further specialize my method to proportional election systems, the most prominent family of multi-seat assignment methods, covering its most common variations: the highest average methods and largest remainder algorithms. The proposed approach improves existing methodologies for causal inference on multi-seat systems in four dimensions: it relies on weaker identifying assumptions, estimated quantities have a clear interpretation as causal effects, it does not hinge on discretionary choices, and it is easier to scale into problems with many political entities and seats.
    Keywords: Regression Discontinuity Designs; Multi-seat Electoral Systems; Close Elections; Causal inference.
    JEL: C01 C21 P48
    Date: 2023–08–18
  9. By: Schrab, Antonin; Jitkrittum, Wittawat; Szabo, Zoltan; Sejdinovic, Dino; Gretton, Arthur
    Abstract: We discuss how MultiFIT, the Multiscale Fisher’s Independence Test for Multivariate Dependence proposed by Gorsky and Ma (2022), compares to existing linear-time kernel tests based on the Hilbert-Schmidt independence criterion (HSIC). We highlight the fact that the levels of the kernel tests at any finite sample size can be controlled exactly, as it is the case with the level of MultiFIT. In our experiments, we observe some of the performance limitations of MultiFIT in terms of test power.
    Keywords: EP/S021566/1
    JEL: C1
    Date: 2022–09–01
  10. By: Caron, François; Panero, Francesca; Rousseau, Judith
    Abstract: This paper investigates properties of the class of graphs based on exchangeable point processes. We provide asymptotic expressions for the number of edges, number of nodes, and degree distributions, identifying four regimes: (i) a dense regime, (ii) a sparse, almost dense regime, (iii) a sparse regime with power-law behaviour, and (iv) an almost extremely sparse regime. We show that, under mild assumptions, both the global and local clustering coefficients converge to constants which may or may not be the same. We also derive a central limit theorem for subgraph counts and for the number of nodes. Finally, we propose a class of models within this framework where one can separately control the latent structure and the global sparsity/power-law properties of the graph.
    Keywords: community structure; generalised graphon; Networks; Poisson processes; power law; sparsity; subgraph counts; transitivity; EPSRC and MRC Centre for Doctoral Training in Statistical Science (grant code EP/L016710/1; European Union’s Horizon 2020 research and innovation programme (grant agreement no. 834175
    JEL: C1
    Date: 2023–06–16
  11. By: Tomas Micko (Council for Budget Responsibility); Alexander Karsay (Council for Budget Responsibility); Zuzana Mucka (Council for Budget Responsibility); Lucia Sramkova (Council for Budget Responsibility)
    Abstract: This paper offers a synthesis of several approaches to measuring output gap in Slovakia and serves as an update of the original CBR work Finding Yeti after almost a decade. A “suite of models” approach is estimated and assessed to provide advantages over single models. Following the recommendation of the EU IFIs guide suggesting no one-size-fits-all approach for measuring output gap, our family of methods consist of two unobserved component models, principal component model, semi-structural model and Modified Hamilton filter. We propose a novelty approach to weighting the individual models capturing recent structural innovations in the economy to construct one central estimate of the output gap. Such a robust estimate is maximising its overall plausibility and applicability to prudent fiscal policy assessment.
    Keywords: output gap, unobserved component, trend, cycle, plausibility, Bayesian analysis, estimation
    JEL: C11 C13 C32 E32 E62
    Date: 2023–08
  12. By: Sushant Acharya; William Chen; Marco Del Negro; Keshav Dogra; Aidan Gleich; Shlok Goyal; Donggyu Lee; Ethan Matlin; Reca Sarfati; Sikata Sengupta
    Abstract: We provide a toolkit for efficient online estimation of heterogeneous agent (HA) New Keynesian (NK) models based on Sequential Monte Carlo methods. We use this toolkit to compare the out-of-sample forecasting accuracy of a prominent HANK model, Bayer et al. (2022), to that of the representative agent (RA) NK model of Smets and Wouters (2007, SW). We find that HANK’s accuracy for real activity variables is notably inferior to that of SW. The results for consumption in particular are disappointing since the main difference between RANK and HANK is the replacement of the RA Euler equation with the aggregation of individual households’ consumption policy functions, which reflects inequality.
    Keywords: HANK model; Heterogeneous-agent New Keynesian (HANK) model; Bayesian inference; sequential Monte Carlo methods
    JEL: C11 C32 D31 E32 E37 E52
    Date: 2023–08–01
  13. By: Aaron Bodoh-Creed; Brent Hickman; John List; Ian Muir; Gregory Sun
    Abstract: In this paper, we provide a suite of tools for empirical market design, including optimal nonlinear pricing in intensive-margin consumer demand, as well as a broad class of related adverse selection models. Despite significant data limitations, we are able to derive informative bounds on demand under counterfactual price changes. These bounds arise because empirically plausible DGPs must respect the Law of Demand and the observed shift(s) in aggregate demand resulting from a known exogenous price change(s). These bounds facilitate robust policy prescriptions using rich, internal data sources similar to those available in many real-world applications. Our partial identification approach enables viable nonlinear pricing design while achieving robustness against worst-case deviations from baseline model assumptions. As a side benefit, our identification results also provide useful, novel insights into optimal experimental design for pricing RCTs.
    Date: 2023
  14. By: Mikrajuddin Abdullah
    Abstract: The presence of a high number of zero flow trades continues to provide a challenge in identifying gravity parameters to explain international trade using the gravity model. Linear regression with a logarithmic linear equation encounters an indefinite value on the logarithmic trade. Although several approaches to solving this problem have been proposed, the majority of them are no longer based on linear regression, making the process of finding solutions more complex. In this work, we suggest a two-step technique for determining the gravity parameters: first, perform linear regression locally to establish a dummy value to substitute trade flow zero, and then estimating the gravity parameters. Iterative techniques are used to determine the optimum parameters. Machine learning is used to test the estimated parameters by analyzing their position in the cluster. We calculated international trade figures for 2004, 2009, 2014, and 2019. We just examine the classic gravity equation and discover that the powers of GDP and distance are in the same cluster and are both worth roughly one. The strategy presented here can be used to solve other problems involving log-linear regression.
    Date: 2023–08
  15. By: Chavleishvili, Sulkhan; Kremer, Manfred
    Abstract: This paper proposes a general statistical framework for systemic financial stress indices which measure the severity of financial crises on a continuous scale. Several index designs from the financial stress and systemic risk literature can be represented as special cases. We introduce an enhanced daily variant of the CISS (composite indicator of systemic stress) for the euro area and the US. The CISS aggregates a representative set of stress indicators using their time-varying cross-correlations as systemic risk weights, computationally similar to how portfolio risk is computed from the risk characteristics of individual assets. A boot-strap algorithm provides test statistics. Single-equation and system quantile growth-at-risk regressions show that the CISS has stronger effects in the lower tails of the growth distribu-tion. Simulations based on a quantile VAR suggest that systemic stress is a major driver of the Great Recession, while its contribution to the COVID-19 crisis appears to be small. JEL Classification: C14, C31, C43, C53, E44, G01
    Keywords: Financial crisis, Financial stress index, Macro-financial linkages, Quantile VAR, Systemic risk
    Date: 2023–08

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