|
on Econometrics |
| By: | Seoyun Hong |
| Abstract: | This paper develops estimation and inference methods for censored quantile regression models with high-dimensional controls. The methods are based on the application of double/debiased machine learning (DML) framework to the censored quantile regression estimator of Buchinsky and Hahn (1998). I provide valid inference for low-dimensional parameters of interest in the presence of high-dimensional nuisance parameters when implementing machine learning estimators. The proposed estimator is shown to be consistent and asymptotically normal. The performance of the estimator with high-dimensional controls is illustrated with numerical simulation and an empirical application that examines the effect of 401(k) eligibility on savings. |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2303.02784&r=ecm |
| By: | Otsu, Taisuke; Takahata, Keisuke; Xu, Mengshan |
| Abstract: | This paper proposes an empirical likelihood inference method for monotone index models. We construct the empirical likelihood function based on a modified score function developed by Balabdaoui et al. (Scand J Stat 46:517–544, 2019), where the monotone link function is estimated by isotonic regression. It is shown that the empirical likelihood ratio statistic converges to a weighted chi-squared distribution. We suggest inference procedures based on an adjusted empirical likelihood statistic that is asymptotically pivotal, and a bootstrap calibration with recentering. A simulation study illustrates usefulness of the proposed inference methods. |
| Keywords: | Empirical likelihood; Isotonic regression; Monotone index model; Springer deal |
| JEL: | C1 |
| Date: | 2023–02–25 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:118123&r=ecm |
| By: | Abdul-Nasah Soale; Emmanuel Selorm Tsyawo |
| Abstract: | High covariate dimensionality is an increasingly occurrent phenomenon in model estimation. A common approach to handling high-dimensionality is regularisation, which requires sparsity of model parameters. However, sparsity may not always be supported by economic theory or easily verified in some empirical contexts; severe bias and misleading inference can occur. This paper introduces a grouped parameter estimator (GPE) that circumvents this problem by using a parameter clustering technique. The large sample properties of the GPE hold under fairly standard conditions including a compact parameter support that can be bounded away from zero. Monte Carlo simulations demonstrate the excellent performance of the GPE relative to competing estimators in terms of bias and size control. Lastly, an empirical application of the GPE to the estimation of price and income elasticities of demand for gasoline illustrates the practical utility of the GPE. |
| Date: | 2023–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2302.09255&r=ecm |
| By: | Gordon Burtch; Edward McFowland III; Mochen Yang; Gediminas Adomavicius |
| Abstract: | Despite increasing popularity in empirical studies, the integration of machine learning generated variables into regression models for statistical inference suffers from the measurement error problem, which can bias estimation and threaten the validity of inferences. In this paper, we develop a novel approach to alleviate associated estimation biases. Our proposed approach, EnsembleIV, creates valid and strong instrumental variables from weak learners in an ensemble model, and uses them to obtain consistent estimates that are robust against the measurement error problem. Our empirical evaluations, using both synthetic and real-world datasets, show that EnsembleIV can effectively reduce estimation biases across several common regression specifications, and can be combined with modern deep learning techniques when dealing with unstructured data. |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2303.02820&r=ecm |
| By: | JIN SEO CHO (Yonsei University); PETER C. B. PHILLIPS (Yale University); JUWON SEO (National University of Singapore) |
| Abstract: | A parametric quantile function estimation procedure is developed for functional data. The approach involves minimizing the sum of integrated functional distances that measure the functional gap between each functional observation and the quantile curve in terms of the check function. The procedure is validated under both correctly specified and misspecified models by allowing for the presence of nuisance parameter estimation effects. Testing methodology is developed using Wald, Lagrange multiplier, and quasi-likelihood ratio procedures in this functional data setting. Finite sample performance is assessed using simulations and the methodology is applied to study how lifetime income paths differ between genders and among different education levels using continuous work history samples. The methodology enables the analysis of full career income paths with temporal and possibly persistent dependence structures embodied in the observations.The results capture both gender and education effects but these empirical differences are shown to be mitigated upon rescaling to take account of lifetime experience and job mobility. |
| Keywords: | Functional data; quantile function; nuisance effects; quantile inference; lifetime income path; gender and education effects. |
| JEL: | C12 C21 C31 C80 |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:yon:wpaper:2023rwp-211&r=ecm |
| By: | KANO, Takashi |
| Abstract: | The minimal econometric interpretation (MEI) of DSGE models provides a formal model evaluation and comparison of misspecified nonlinear dynamic stochastic general equilibrium (DSGE) models based on atheoretical reference models. The MEI approach recognizes DSGE models as incomplete econometric tools that provide only prior distributions of targeted population moments but have no implications for actual data and sample moments. This study, based on the MEI approach, develops a Bayesian posterior inference method. Prior distributions of targeted population moments simulated by the DSGE model restrict the hyperparameters of Dirichlet distributions. These are natural conjugate priors for multinomial distributions followed by corresponding posterior distributions estimated by the reference model. The Polya marginal likelihood of the resulting restricted Dirichlet-multinomial model has a tractive approximated log-linear representation of the Jensen-Shannon divergence, which the proposed distribution-matching posterior inference uses as the limited information likelihood function. Monte Carlo experiments indicate that the MEI posterior sampler correctly infers calibrated structural parameters of an equilibrium asset pricing model and detects the true model with posterior odds ratios. |
| Keywords: | Bayesian posterior inference, Minimum econometric interpretation, Nonlinear DSGE model, Model misspecification, Equilibrium asset pricing model |
| JEL: | C11 C52 E37 |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:hit:hiasdp:hias-e-128&r=ecm |
| By: | Quinn Lanners; Harsh Parikh; Alexander Volfovsky; Cynthia Rudin; David Page |
| Abstract: | Our goal is to produce methods for observational causal inference that are auditable, easy to troubleshoot, yield accurate treatment effect estimates, and scalable to high-dimensional data. We describe an almost-exact matching approach that achieves these goals by (i) learning a distance metric via outcome modeling, (ii) creating matched groups using the distance metric, and (iii) using the matched groups to estimate treatment effects. Our proposed method uses variable importance measurements to construct a distance metric, making it a flexible method that can be adapted to various applications. Concentrating on the scalability of the problem in the number of potential confounders, we operationalize our approach with LASSO. We derive performance guarantees for settings where LASSO outcome modeling consistently identifies all confounders (importantly without requiring the linear model to be correctly specified). We also provide experimental results demonstrating the auditability of matches, as well as extensions to more general nonparametric outcome modeling. |
| Date: | 2023–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2302.11715&r=ecm |
| By: | Stommes, Drew; Aronow, P. M.; Sävje, Fredrik |
| Abstract: | The regression discontinuity (RD) design offers identification of causal effects under weak assumptions, earning it a position as a standard method in modern political science research. But identification does not necessarily imply that causal effects can be estimated accurately with limited data. In this paper, we highlight that estimation under the RD design involves serious statistical challenges and investigate how these challenges manifest themselves in the empirical literature in political science. We collect all RD-based findings published in top political science journals in the period 2009-2018. The distribution of published results exhibits pathological features; estimates tend to bunch just above the conventional level of statistical significance. A reanalysis of all studies with available data suggests that researcher discretion is not a major driver of these features. However, researchers tend to use inappropriate methods for inference, rendering standard errors artificially small. A retrospective power analysis reveals that most of these studies were underpowered to detect all but large effects. The issues we uncover, combined with well-documented selection pressures in academic publishing, cause concern that many published findings using the RD design may be exaggerated. |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:i4rdps:22&r=ecm |
| By: | Chen Liu; Chao Wang; Minh-Ngoc Tran; Robert Kohn |
| Abstract: | We propose a new approach to volatility modelling by combining deep learning (LSTM) and realized volatility measures. This LSTM-enhanced realized GARCH framework incorporates and distills modeling advances from financial econometrics, high frequency trading data and deep learning. Bayesian inference via the Sequential Monte Carlo method is employed for statistical inference and forecasting. The new framework can jointly model the returns and realized volatility measures, has an excellent in-sample fit and superior predictive performance compared to several benchmark models, while being able to adapt well to the stylized facts in volatility. The performance of the new framework is tested using a wide range of metrics, from marginal likelihood, volatility forecasting, to tail risk forecasting and option pricing. We report on a comprehensive empirical study using 31 widely traded stock indices over a time period that includes COVID-19 pandemic. |
| Date: | 2023–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2302.08002&r=ecm |
| By: | Marzio Di Vece; Diego Garlaschelli; Tiziano Squartini |
| Abstract: | Analysing weighted networks requires modelling the binary and weighted properties simultaneously. We highlight three approaches for estimating the parameters responsible for them: econometric techniques treating topology as deterministic and statistical techniques either ensemble-averaging parameters or maximising an averaged likelihood over the topological randomness. In homogeneous networks, equivalence holds; in heterogeneous networks, the local disorder breaks it, in a way reminiscent of the difference between quenched and annealed averages in the physics of disordered systems. |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2303.02716&r=ecm |
| By: | Riani, Marco; Atkinson, Anthony C.; Corbellini, Aldo; Farcomeni, Alessio; Laurini, Fabrizio |
| Abstract: | Information criteria for model choice are extended to the detection of outliers in regression models. For deletion of observations (hard trimming) the family of models is generated by monitoring properties of the fitted models as the trimming level is varied. For soft trimming (downweighting of observations), some properties are monitored as the efficiency or breakdown point of the robust regression is varied. Least Trimmed Squares and the Forward Search are used to monitor hard trimming, with MM- and S-estimation the methods for soft trimming. Bayesian Information Criteria (BIC) for both scenarios are developed and results about their asymptotic properties provided. In agreement with the theory, simulations and data analyses show good performance for the hard trimming methods for outlier detection. Importantly, this is achieved very simply, without the need to specify either significance levels or decision rules for multiple outliers. |
| Keywords: | automatic data analysis; Bayesian Information Criterion (BIC); forward search; least trimmed squares; MM-estimation; S-estimation |
| JEL: | C1 |
| Date: | 2022–02–25 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:113647&r=ecm |
| By: | Sobin Joseph; Shashi Jain |
| Abstract: | This paper introduces the Neural Network for Nonlinear Hawkes processes (NNNH), a non-parametric method based on neural networks to fit nonlinear Hawkes processes. Our method is suitable for analyzing large datasets in which events exhibit both mutually-exciting and inhibitive patterns. The NNNH approach models the individual kernels and the base intensity of the nonlinear Hawkes process using feed forward neural networks and jointly calibrates the parameters of the networks by maximizing the log-likelihood function. We utilize Stochastic Gradient Descent to search for the optimal parameters and propose an unbiased estimator for the gradient, as well as an efficient computation method. We demonstrate the flexibility and accuracy of our method through numerical experiments on both simulated and real-world data, and compare it with state-of-the-art methods. Our results highlight the effectiveness of the NNNH method in accurately capturing the complexities of nonlinear Hawkes processes. |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2303.03073&r=ecm |
| By: | Cai, Hanqing; Wang, Tengyao |
| Abstract: | Change-points are a routine feature of `big data' observed in the form of high-dimensional data streams. In many such data streams, the component series possess group structures and it is natural to assume that changes only occur in a small number of all groups. We propose a new change point procedure, called groupInspect, that exploits the group sparsity structure to estimate a projection direction so as to aggregate information across the component series to successfully estimate the change-point in the mean structure of the series. We prove that the estimated projection direction is minimax optimal, up to logarithmic factors, when all group sizes are of comparable order. Moreover, our theory provide strong guarantees on the rate of convergence of the change-point location estimator. Numerical studies demonstrates the competitive performance of groupInspect in a wide range of settings and a real data example conrms the practical usefulness of our procedure. |
| Keywords: | change-point analysis; high-dimensional data; group sparsity; EP/T02772X/1 |
| JEL: | C1 |
| Date: | 2023–03–03 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:118366&r=ecm |
| By: | Bonev, Petyo |
| Abstract: | What is a behavioral spillover? How can a spillover be uncovered from the data? What is the precise link between the underlying psychological theory of a spillover and the econometric assumptions which are necessary to estimate it? This paper draws on recent advancements in causal inference, behavioral economics, psychology, and neuroscience to develop a framework for the causal evaluation and interpretation of behavioral spillovers. A novel research design is suggested. The paper challenges existing empirical strategies and reevaluates existing empirical results. |
| Keywords: | Behavioral spillovers, environmental policy evaluation, moral licensing, self-perception theory, cognitive dissonance theory, foot-in-the-door effect |
| JEL: | C21 C26 C9 D04 D9 |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:usg:econwp:2023:03&r=ecm |
| By: | Sam Dannels |
| Abstract: | A common problem when forecasting rare events, such as recessions, is limited data availability. Recent advancements in deep learning and generative adversarial networks (GANs) make it possible to produce high-fidelity synthetic data in large quantities. This paper uses a model called DoppelGANger, a GAN tailored to producing synthetic time series data, to generate synthetic Treasury yield time series and associated recession indicators. It is then shown that short-range forecasting performance for Treasury yields is improved for models trained on synthetic data relative to models trained only on real data. Finally, synthetic recession conditions are produced and used to train classification models to predict the probability of a future recession. It is shown that training models on synthetic recessions can improve a model's ability to predict future recessions over a model trained only on real data. |
| Date: | 2023–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2302.10490&r=ecm |
| By: | Serena Ng; Susannah Scanlan |
| Abstract: | Monthly and weekly economic indicators are often taken to be the largest common factor estimated from high and low frequency data, either separately or jointly. To incorporate mixed frequency information without directly modeling them, we target a low frequency diffusion index that is already available, and treat high frequency values as missing. We impute these values using multiple factors estimated from the high frequency data. In the empirical examples considered, static matrix completion that does not account for serial correlation in the idiosyncratic errors yields imprecise estimates of the missing values irrespective of how the factors are estimated. Single equation and systems-based dynamic procedures yield imputed values that are closer to the observed ones. This is the case in the counterfactual exercise that imputes the monthly values of consumer sentiment series before 1978 when the data was released only on a quarterly basis. This is also the case for a weekly version of the CFNAI index of economic activity that is imputed using seasonally unadjusted data. The imputed series reveals episodes of increased variability of weekly economic information that are masked by the monthly data, notably around the 2014-15 collapse in oil prices. |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2303.01863&r=ecm |
| By: | Lin Liu; Chang Li |
| Abstract: | Higher-Order Influence Functions (HOIFs) provide a unified theory for constructing rate-optimal estimators for a large class of low-dimensional (smooth) statistical functionals/parameters (and sometimes even infinite-dimensional functions) that arise in substantive fields including epidemiology, economics, and the social sciences. Since the introduction of HOIFs by Robins et al. (2008), they have been viewed mostly as a theoretical benchmark rather than a useful tool for statistical practice. Works aimed to flip the script are scant, but a few recent papers Liu et al. (2017, 2021b) make some partial progress. In this paper, we take a fresh attempt at achieving this goal by constructing new, numerically stable HOIF estimators (or sHOIF estimators for short with ``s'' standing for ``stable'') with provable statistical, numerical, and computational guarantees. This new class of sHOIF estimators (up to the 2nd order) was foreshadowed in synthetic experiments conducted by Liu et al. (2020a). |
| Date: | 2023–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2302.08097&r=ecm |
| By: | Yoosoon Chang (Indiana University, Department of Economics); Hilde C. Bjornland (BI Norwegian Business School); Jamie L. Cross (BI Norwegian Business School) |
| Abstract: | This paper proposes a new mixed vector autoregression (MVAR) model to examine the relationship between aggregate time series and functional variables in a multivariate setting. The model facilitates a re-examination of the oil-stock price nexus by estimating the effects of demand and supply shocks from the global market for crude oil on the entire distribution of U.S. stock returns since the late 1980s. We show that the MVAR effectively extracts information from the returns distribution that is more relevant for understanding the oil-stock price nexus beyond simply looking at the first few moments. Using novel functional impulse response functions (FIRFs), we find that oil market demand and supply shocks tend to increase returns, reduce volatility, and have an asymmetric effect on the returns distribution as a whole. In a value-at-risk (VaR) analysis we also find that the oil market contains important information that reduces expected loss, and that the response of VaR to the oil market demand and supply shocks has changed over time. |
| Keywords: | Oil market, stock market, oil-stock price nexus, functional VAR. |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:inu:caeprp:2023005&r=ecm |