nep-ecm New Economics Papers
on Econometrics
Issue of 2020‒11‒23
sixteen papers chosen by
Sune Karlsson
Örebro universitet

  1. An Alternative Bootstrap for Proxy Vector Autoregressions By Martin Bruns; Helmut Luetkepohl
  2. Conditional quantile estimators: A small sample theory By Grigory Franguridi; Bulat Gafarov; Kaspar Wuthrich
  3. Robust Inference in Time-Varying Structural VAR Models: The DC-Cholesky Multivariate Stochastic Volatility Model By Hartwig, Benny
  4. Sparse time-varying parameter VECMs with an application to modeling electricity prices By Niko Hauzenberger; Michael Pfarrhofer; Luca Rossini
  5. Instrumental Variable Identification of Dynamic Variance Decompositions By Mikkel Plagborg-M{\o}ller; Christian K. Wolf
  6. Relaxing Conditional Independence in an Endogenous Binary Response Model By Alyssa Carlson
  7. Estimation, Inference, and Interpretation in the Regression Discontinuity Design By Blaise Melly; Rafael Lalive
  8. Do We Exploit all Information for Counterfactual Analysis? Benefits of Factor Models and Idiosyncratic Correction By Jianqing Fan; Ricardo P. Masini; Marcelo C. Medeiros
  9. Two-Stage Least Squares Random Forests with a Replication of Angrist and Evans (1998) By Kugler, Philipp; Biewen, Martin
  10. Machine Learning for Experimental Design: Methods for Improved Blocking By Brian Quistorff; Gentry Johnson
  11. Nonparametric Identification of Production Function, Total Factor Productivity, and Markup from Revenue Data By Hiroyuki Kasahara; Yoichi Sugita
  12. Adaptive Bernstein Copulas and Risk Management By Dietmar Pfeifer; Olena Ragulina
  13. Causal Inference for Spatial Treatments By Michael Pollmann
  14. Coresets for Regressions with Panel Data By Lingxiao Huang; K. Sudhir; Nisheeth K. Vishnoi
  15. Parameterizing structural equation models as Bayesian multilevel regression models: An example with the Global Multidimensional Poverty Index By Uanhoro, James Ohisei
  16. Using mixed-frequency and realized measures in quantile regression By Vincenzo Candila; Giampiero M. Gallo; Lea Petrella

  1. By: Martin Bruns (University of East Anglia); Helmut Luetkepohl (DIW Berlin and Freie Universitaet Berlin)
    Abstract: We propose a new bootstrap for inference for impulse responses in structural vector autoregressive models identi ed with an external proxy variable. Simulations show that the new bootstrap provides confidence intervals for impulse responses which often have more precise coverage than and similar length as the competing moving-block bootstrap intervals. An empirical example shows how the new bootstrap can be applied in the context of identifying monetary policy shocks.
    Keywords: Bootstrap inference, structural vector autoregression, impulse responses, instrumental variable
    JEL: C32
    Date: 2020–11–11
  2. By: Grigory Franguridi; Bulat Gafarov; Kaspar Wuthrich
    Abstract: This paper studies small sample properties and bias of just-identified instrumental variable quantile regression (IVQR) estimators, nesting order statistics and classical quantile regression. We propose a theoretical framework for analyzing small sample properties based on a novel approximation of the discontinuous sample moments with a H\"older continuous process. Using this approximation, we derive remainder bounds for the asymptotic linear expansions of exact and k-step estimators of IVQR models. Furthermore, we derive a bias formula for exact IVQR estimators up to order $o\left(\frac{1}{n}\right)$. The bias contains components that cannot be consistently estimated and depend on the particular numerical estimation algorithm. To circumvent this problem, we propose a novel 1-step adjustment of the estimator, which admits a feasible bias correction. Monte Carlo evidence suggests that our formula removes a substantial portion of the bias for sample sizes as small as $n=50$. We suggest using exact estimators, when possible, to achieve the smallest bias. Otherwise, applying 1-step corrections may improve the higher-order bias and MSE of any consistent estimator.
    Date: 2020–11
  3. By: Hartwig, Benny
    Abstract: This paper investigates how the ordering of variables affects properties of the time-varying covariance matrix in the Cholesky multivariate stochastic volatility model. It establishes that systematically different dynamic restrictions are imposed when the ratio of volatilities is time-varying. Simulations demonstrate that estimated covariance matrices become more divergent when volatility clusters idiosyncratically. It is illustrated that this property is important for empirical applications. Specifically, alternative estimates on the evolution of U.S. systematic monetary policy and in ation-gap persistence indicate that conclusions may critically hinge on a selected ordering of variables. The dynamic correlation Cholesky multivariate stochastic volatility model is proposed as a robust alternative.
    Keywords: Model uncertainty,Multivariate stochastic volatility,Dynamic correlations,Monetary policy,Structural VAR
    JEL: C11 C32 E32 E52
    Date: 2020
  4. By: Niko Hauzenberger; Michael Pfarrhofer; Luca Rossini
    Abstract: In this paper we propose a time-varying parameter (TVP) vector error correction model (VECM) with heteroscedastic disturbances. We combine a set of econometric techniques for dynamic model specification in an automatic fashion. We employ continuous global-local shrinkage priors for pushing the parameter space towards sparsity. In a second step, we post-process the cointegration relationships, the autoregressive coefficients and the covariance matrix via minimizing Lasso-type loss functions to obtain truly sparse estimates. This two-step approach alleviates overfitting concerns and reduces parameter estimation uncertainty, while providing estimates for the number of cointegrating relationships that varies over time. Our proposed econometric framework is applied to modeling European electricity prices and shows gains in forecast performance against a set of established benchmark models.
    Date: 2020–11
  5. By: Mikkel Plagborg-M{\o}ller; Christian K. Wolf
    Abstract: Macroeconomists increasingly use external sources of exogenous variation for causal inference. However, unless such external instruments (proxies) capture the underlying shock without measurement error, existing methods are silent on the importance of that shock for macroeconomic fluctuations. We show that, in a general moving average model with external instruments, variance decompositions for the instrumented shock are interval-identified, with informative bounds. Various additional restrictions guarantee point identification of both variance and historical decompositions. Unlike SVAR analysis, our methods do not require invertibility. Applied to U.S. data, they give a tight upper bound on the importance of monetary shocks for inflation dynamics.
    Date: 2020–11
  6. By: Alyssa Carlson (Department of Economics, University of Missouri-Columbia)
    Abstract: For binary response models, the literature primarily addresses endogeneity by a control function approach assuming conditional independence (CF-CI). However, as the literature also notes, CF-CI implies conditions like homoskedasticity (of the latent error with respect to the instruments) that fail in many empirical settings. I propose an alternative approach that allows for heteroskedasticity, achieving identification with a conditional mean restriction. These identification results apply to a latent Gaussian error term with flexibly parametrized heteroskedasticity. I propose a two-step conditional maximum likelihood estimator and derive its asymptotic distribution. In simulations, the new estimator outperforms others when CF-CI fails and is fairly robust to distributional misspecification. An empirical illustration studies married women's labor force participation.
    Keywords: Binary choice model, Endogenous regressors, Control function, Heteroskedasticity
    JEL: C31 C35
    Date: 2020–09
  7. By: Blaise Melly; Rafael Lalive
    Abstract: The Regression Discontinuity Design (RDD) has proven to be a compelling and transparent research design to estimate treatment effects. We provide a review of the main assumptions and key challenges faced when adopting an RDD. We cover the most recent developments and advanced methods, and provide the key intuitions that underlie the statistical arguments. Among others, we summarize new insights that we consider to be highly relevant about the choice of bandwidth, optimal inference, discrete running variables, distributional effects, estimation in the presence of covariates, and the regression kink design. We also show how structural parameters can be estimated by combining an RDD identification strategy with theoretical models. We illustrate the procedures by applying them to data and we provide codes to replicate the results.
    Date: 2020–11
  8. By: Jianqing Fan; Ricardo P. Masini; Marcelo C. Medeiros
    Abstract: The measurement of treatment (intervention) effects on a single (or just a few) treated unit(s) based on counterfactuals constructed from artificial controls has become a popular practice in applied statistics and economics since the proposal of the synthetic control method. In high-dimensional setting, we often use principal component or (weakly) sparse regression to estimate counterfactuals. Do we use enough data information? To better estimate the effects of price changes on the sales in our case study, we propose a general framework on counterfactual analysis for high dimensional dependent data. The framework includes both principal component regression and sparse linear regression as specific cases. It uses both factor and idiosyncratic components as predictors for improved counterfactual analysis, resulting a method called Factor-Adjusted Regularized Method for Treatment (FarmTreat) evaluation. We demonstrate convincingly that using either factors or sparse regression is inadequate for counterfactual analysis in many applications and the case for information gain can be made through the use of idiosyncratic components. We also develop theory and methods to formally answer the question if common factors are adequate for estimating counterfactuals. Furthermore, we consider a simple resampling approach to conduct inference on the treatment effect as well as bootstrap test to access the relevance of the idiosyncratic components. We apply the proposed method to evaluate the effects of price changes on the sales of a set of products based on a novel large panel of sale data from a major retail chain in Brazil and demonstrate the benefits of using additional idiosyncratic components in the treatment effect evaluations.
    Date: 2020–11
  9. By: Kugler, Philipp; Biewen, Martin
    Abstract: We develop the case of two-stage least squares estimation (2SLS) in the general framework of Athey et al. (Generalized Random Forests, Annals of Statistics, Vol. 47, 2019) and provide a software implementation for R and C++. We use the method to revisit the classic application of instrumental variables in Angrist and Evans (Children and Their Parents' Labor Supply: Evidence from Exogenous Variation in Family Size, American Economic Review, Vol. 88, 1998). The two-stage least squares random forest allows one to investigate local heterogenous effects that cannot be investigated using ordinary 2SLS.
    Keywords: machine learning,generalized random forests,fertility,instrumental variable estimation
    JEL: C26 C55 J22 J13 C14
    Date: 2020
  10. By: Brian Quistorff; Gentry Johnson
    Abstract: Restricting randomization in the design of experiments (e.g., using blocking/stratification, pair-wise matching, or rerandomization) can improve the treatment-control balance on important covariates and therefore improve the estimation of the treatment effect, particularly for small- and medium-sized experiments. Existing guidance on how to identify these variables and implement the restrictions is incomplete and conflicting. We identify that differences are mainly due to the fact that what is important in the pre-treatment data may not translate to the post-treatment data. We highlight settings where there is sufficient data to provide clear guidance and outline improved methods to mostly automate the process using modern machine learning (ML) techniques. We show in simulations using real-world data, that these methods reduce both the mean squared error of the estimate (14%-34%) and the size of the standard error (6%-16%).
    Date: 2020–10
  11. By: Hiroyuki Kasahara; Yoichi Sugita
    Abstract: Commonly used methods of production function estimation assume that a firm’s output quantity can be observed as data, but typical datasets contain only revenue, not output quantity. We examine the nonparametric identification of production function from revenue data when a firm faces a general nonparametric demand function under imperfect competition. Under standard assumptions, we provide the constructive nonparametric identification of various firm-level objects: gross production function, total factor productivity, price markups over marginal costs, output prices, output quantities, a demand system, and a representative consumer’s utility function.
    Keywords: nonparametric identification, production function, markup, total factor productivity, revenue
    JEL: C14 L11 L25
    Date: 2020
  12. By: Dietmar Pfeifer; Olena Ragulina
    Abstract: We present a constructive approach to Bernstein copulas with an admissible discrete skeleton in arbitrary dimensions when the underlying marginal grid sizes are smaller than the number of observations. This prevents an overfitting of the estimated dependence model and reduces the simulation effort for Bernstein copulas a lot. In a case study, we compare different approaches of Bernstein and Gaussian copulas w.r.t. the estimation of risk measures in risk management.
    Date: 2020–11
  13. By: Michael Pollmann
    Abstract: I propose a framework, estimators, and inference procedures for the analysis of causal effects in a setting with spatial treatments. Many events and policies (treatments), such as opening of businesses, building of hospitals, and sources of pollution, occur at specific spatial locations, with researchers interested in their effects on nearby individuals or businesses (outcome units). However, the existing treatment effects literature primarily considers treatments that could be assigned directly at the level of the outcome units, potentially with spillover effects. I approach the spatial treatment setting from a similar experimental perspective: What ideal experiment would we design to estimate the causal effects of spatial treatments? This perspective motivates a comparison between individuals near realized treatment locations and individuals near unrealized candidate locations, which is distinct from current empirical practice. Furthermore, I show how to find such candidate locations and apply the proposed methods with observational data. I apply the proposed methods to study the causal effects of grocery stores on foot traffic to nearby businesses during COVID-19 lockdowns.
    Date: 2020–10
  14. By: Lingxiao Huang; K. Sudhir; Nisheeth K. Vishnoi
    Abstract: This paper introduces the problem of coresets for regression problems to panel data settings. We first define coresets for several variants of regression problems with panel data and then present efficient algorithms to construct coresets of size that depend polynomially on 1/$\varepsilon$ (where $\varepsilon$ is the error parameter) and the number of regression parameters - independent of the number of individuals in the panel data or the time units each individual is observed for. Our approach is based on the Feldman-Langberg framework in which a key step is to upper bound the "total sensitivity" that is roughly the sum of maximum influences of all individual-time pairs taken over all possible choices of regression parameters. Empirically, we assess our approach with a synthetic and a real-world datasets; the coreset sizes constructed using our approach are much smaller than the full dataset and coresets indeed accelerate the running time of computing the regression objective.
    Date: 2020–11
  15. By: Uanhoro, James Ohisei
    Abstract: The goal of this paper is to frame structural equation models (SEMs) as Bayesian multilevel regression models. Framing SEMs as Bayesian regression models provides an alternative approach to understanding SEMs that can improve model transparency and enhance innovation during modeling. For demonstration, we analyze six indicators of living standards data from 101 countries. The data are proportions and we develop confirmatory factor analysis as regression while accommodating the fact that the data are proportions. We also provide extensive guidance on prior specification, which is relevant for estimating complex regression models such as these. Finally, we run through regression equations for SEMs beyond the scope of the demonstration.
    Date: 2020–11–04
  16. By: Vincenzo Candila; Giampiero M. Gallo; Lea Petrella
    Abstract: Quantile regression is an efficient tool when it comes to estimate popular measures of tail risk such as the conditional quantile Value at Risk. In this paper we exploit the availability of data at mixed frequency to build a volatility model for daily returns with low-- (for macro--variables) and high--frequency (which may include an \virg{--X} term related to realized volatility measures) components. The quality of the suggested quantile regression model, labeled MF--Q--ARCH--X, is assessed in a number of directions: we derive weak stationarity properties, we investigate its finite sample properties by means of a Monte Carlo exercise and we apply it on financial real data. VaR forecast performances are evaluated by backtesting and Model Confidence Set inclusion among competitors, showing that the MF--Q--ARCH--X has a consistently accurate forecasting capability.
    Date: 2020–11

This nep-ecm issue is ©2020 by Sune Karlsson. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
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