Article
Economics
Daniel McFadden
Summary: This study identifies the issue of numerical and statistical instability in estimating market-clearing prices using a mixed logit demand model, particularly caused by the high influence of the left tail of the random price coefficient. The author provides conditions for the existence of market equilibrium prices in mixed logit models, which are satisfied in lognormal mixing and some cases of truncated normal mixing. Nonetheless, even with market equilibria, these models can still produce unstable and unreliable conclusions.
JOURNAL OF CHOICE MODELLING
(2022)
Article
Computer Science, Interdisciplinary Applications
John Paul Helveston
Summary: This paper introduces the logitr R package, which allows for fast maximum likelihood estimation of multinomial logit and mixed logit models with unobserved heterogeneity across individuals by modeling parameters that vary randomly over individuals according to a chosen distribution. It is faster than other similar packages and supports utility models specified with preference space or willingness-to-pay (WTP) space parameterizations, allowing for direct estimation of marginal WTP. The paper discusses the implications of each utility parameterization for WTP estimates and highlights design features that enable logitr's performant estimation speed, including benchmarking with similar packages and additional features designed specifically for WTP space models.
JOURNAL OF STATISTICAL SOFTWARE
(2023)
Article
Economics
Ali Behnood, Milad Haghani, Emadaldin Mohammadi Golafshani
Summary: Determining the likelihood of consumers purchasing automated vehicles is crucial for policymakers, researchers, and automobile manufacturers. This study analyzes various factors that affect the purchase likelihood of AVs and finds significant differences between partially and fully automated vehicles.
TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE
(2022)
Article
Mathematics, Applied
Qinjiao Gao, Xingping Sun, Shenggang Zhang
Summary: The research introduces crosslet grids for high-dimensional numerical integration and develops symmetric quadrature rules based on these grids, which offer the same accuracy as full grids but with fewer nodes and less computational complexity. Theoretical analysis and numerical simulations demonstrate the effectiveness of quadrature rules based on crosslet grids for integrands with localized nonsmoothness, revealing a close connection between quadrature rules and quasi-interpolation.
NUMERICAL ALGORITHMS
(2022)
Article
Health Care Sciences & Services
Xiaolei Lin, Robin Mermelstein, Donald Hedeker
Summary: The study utilized a multivariate mixed cumulative logit model to analyze data on cigarette, alcohol, and marijuana use, revealing gender differences in time trends and age effects, as well as confirming the violation of the proportional odds assumption.
BMC MEDICAL RESEARCH METHODOLOGY
(2021)
Article
Computer Science, Interdisciplinary Applications
Jan Pablo Burgard, Joscha Krause, Simon Schmaus
Summary: Spatial dynamic microsimulations allow for multivariate analysis of complex systems with accurate simulation outcomes. However, the reliance on survey data for transition probabilities can be challenging due to lack of regional detail and coverage, potentially leading to simulation failure.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2021)
Article
Mathematics
Opeyo Peter Otieno, Weihu Cheng
Summary: Convergence of the maximization algorithm in logistic regression models is critical, but may fail. Bias correction methods have been conducted for maximum likelihood estimates of parameters for complete data sets and longitudinal models. Balanced data sets yield consistent estimates from conditional Logit estimators for binary response panel data models.
Article
Neurosciences
Mohsen Bahrami, Paul J. Laurienti, Heather M. Shappell, Dale Dagenbach, Sean L. Simpson
Summary: In recent years, there has been an increasing focus on analyzing the brain as a complex dynamic system using neuroimaging data. This study introduces a promising regression-based model to analyze the relationship between dynamic brain networks and desired phenotypes. Additionally, the model can simulate dynamic brain networks at both group and individual levels. This approach addresses the need for a statistical framework that can examine the associations between phenotypic traits and dynamic patterns of brain properties. The model-based method presented in this study allows researchers to align their hypotheses with the analytic approach.
NETWORK NEUROSCIENCE
(2022)
Article
Computer Science, Theory & Methods
Dinh Dung
Summary: This paper investigates the approximation of weighted integrals over Rd for integrands from weighted Sobolev spaces of mixed smoothness. Upper and lower bounds of the convergence rate of optimal quadratures with respect to n integration nodes for functions from these spaces are proved. In the one-dimensional case (d = 1), the right convergence rate of optimal quadratures is obtained. For d >= 2, the upper bound is performed by sparse-grid quadratures with integration nodes on step hyperbolic crosses in the function domain Rd.
JOURNAL OF COMPLEXITY
(2023)
Article
Political Science
Philip Paolino
Summary: Multinomial logit (MNL) estimates the effects of variables on nominal outcomes, leading to variable coefficients based on the choice of reference outcomes. Researchers should focus on the substantive and statistical significance of predicted probabilities matching their research questions for accurate analysis.
POLITICAL ANALYSIS
(2021)
Article
Multidisciplinary Sciences
Purhadi Purhadi, M. Fathurahman
Summary: This article presents a bivariate binary logit model and statistical inference procedures for parameter estimation and hypothesis testing. The BBL model is an extension of the binary logit model with two correlated binary responses, obtained using maximum likelihood and BHHH methods. Hypothesis testing includes simultaneous and partial tests, with test statistics determined by the maximum likelihood ratio test method.
Article
Computer Science, Information Systems
Xianglan Jin
Summary: This paper studies an amplify-quantize-forward (AQF) relay channel with M-ary quadrature amplitude modulation. Two methods for determining the step size of uniform quantization at the relay are proposed by analyzing the power and mean square error of the quantized signal. The paper also presents maximum-likelihood detection and equivalent maximum ratio combining detection algorithms to reduce the detection complexity at the destination in the AQF relay channel. Simulation results demonstrate the superiority of the proposed quantization methods and detection algorithms.
Article
Neurosciences
Thomas Maullin-Sapey, Thomas E. Nichols
Summary: Large-scale, shared datasets in neuroimaging pose challenges to existing tools in terms of scale and complexity. To address these challenges, researchers have developed the BLMM toolbox, an efficient tool for large-scale fMRI linear mixed models analysis.
Article
Management
Nevin Mutlu, Hadi El-Amine, Ozge Sahin
Summary: Consumers' increasing valuation of Instagrammable memories drives their spending from products to experiences. Retailers offer experiences to attract consumers back, but it is unclear under which settings consumers can benefit and raise retailers' profits.
M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
(2023)
Article
Mathematics, Applied
Qing Xiao
Summary: By using Rosenblatt transformation and copula method, a new multivariate quadrature rule is proposed in this paper, which can significantly alleviate the curse of dimensionality and linearly increase computational burden with respect to uncertain inputs. The method can match moment matching equations neglected by the UDR method, performing more robustly in uncertainty quantification problems.
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
(2022)
Article
Economics
Zhongjian Lin, Yingyao Hu
Summary: This paper proposes a binary choice model with misclassification and social interactions to address the misclassification problems in social interactions studies. The identification of the conditional choice probability of the latent dependent variable is achieved using repeated measurements and a monotonicity condition. The complete likelihood function is constructed from the two repeated measurements, and a nested pseudo likelihood algorithm is proposed for estimation. Consistency and asymptotic normality results are shown for the proposed estimation method.
JOURNAL OF ECONOMETRICS
(2024)
Article
Economics
Ji Hyung Lee, Yuya Sasaki, Alexis Akira Toda, Yulong Wang
Summary: Administrative data, often presented as tabulated summaries for confidentiality reasons, can be more easily accessed in this form. In this study, the authors propose a novel nonparametric density estimation method based on maximum entropy and demonstrate its consistent results. The method does not require tuning parameters and provides a closed-form density for further analysis. The authors apply this method to estimate the income distribution using tabulated summary data from U.S. tax returns.
JOURNAL OF ECONOMETRICS
(2024)
Article
Economics
Di Wang, Yao Zheng, Guodong Li
Summary: This paper proposes a new modeling framework for modeling and forecasting high-dimensional tensor-valued time series using the autoregression method. By considering a low-rank Tucker decomposition, this method can flexibly capture the underlying low-dimensional tensor dynamics, achieving dimension reduction and multidimensional dynamic factor interpretations. The paper also studies different estimation methods and their non-asymptotic properties under different low-rank settings.
JOURNAL OF ECONOMETRICS
(2024)
Article
Economics
Hongfei Wang, Binghui Liu, Long Feng, Yanyuan Ma
Summary: This study addresses the problem of testing mutual independence of high-dimensional random vectors and proposes a series of high-dimensional rank-based max-sum tests. Through extensive simulations and real data analysis, the superiority of these tests is demonstrated.
JOURNAL OF ECONOMETRICS
(2024)
Article
Economics
Julian Martinez-Iriarte, Gabriel Montes-Rojas, Yixiao Sun
Summary: This paper analyzes the unconditional effects of a general policy intervention, including location-scale shifts and simultaneous shifts. The study finds that failing to account for these shifts may lead to incorrect assessment of the potential policy effects on the outcome variable of interest.
JOURNAL OF ECONOMETRICS
(2024)
Article
Economics
Karim Chalak
Summary: This paper generalizes the Gini-Frisch bounds to accommodate nonparametric heterogeneous effects and provides suitable conditions for their application in nonparametric nonseparable equations.
JOURNAL OF ECONOMETRICS
(2024)
Article
Economics
Koki Fusejima
Summary: In this paper, sufficient conditions for identifying treatment effects on continuous outcomes are established in endogenous and multi-valued discrete treatment settings with unobserved heterogeneity. The monotonicity assumption for multi-valued discrete treatments and instruments is employed, and the identification condition has a clear economic interpretation. Additionally, the local treatment effects in multi-valued treatment settings are identified, and closed-form expressions of the identified treatment effects are derived.
JOURNAL OF ECONOMETRICS
(2024)
Article
Economics
Li Hou, Baisuo Jin, Yuehua Wu
Summary: The spatiotemporal modeling of networks is highly significant in epidemiology and social network analysis. This research proposes a method for estimating the parameters of spatial dynamic panel models effectively and efficiently. The study also introduces a complex orthogonal greedy algorithm for variable selection and incorporates fixed effects into the model. Extensive simulation studies and data examples demonstrate the effectiveness of the proposed method.
JOURNAL OF ECONOMETRICS
(2024)
Article
Economics
Weilun Zhou, Jiti Gao, David Harris, Hsein Kew
Summary: This paper discusses the estimation of a semi-parametric single-index regression model that allows for nonlinear predictive relationships. The presence of cointegrated predictors balances the nonstationarity properties of the predictors with the stationarity properties of asset returns and avoids the curse of dimensionality. In an empirical application, it is found that using cointegrated predictors produces better out-of-sample forecasts.
JOURNAL OF ECONOMETRICS
(2024)
Article
Economics
Eric Beutner, Alexander Heinemann, Stephan Smeekes
Summary: This paper proposes a fixed-design residual bootstrap method for the two-step estimator associated with the conditional Value-at-Risk. The consistency of the bootstrap is proven for a general class of volatility models, and intervals are constructed for the conditional Value-at-Risk. Simulation results show that the reversed-tails bootstrap interval provides accurate coverage compared to the equal-tailed percentile bootstrap interval.
JOURNAL OF ECONOMETRICS
(2024)
Article
Economics
Federico M. Bandi, Davide Pirino, Roberto Reno
Summary: The article examines the staleness of asset prices, including systematic (market-wide) staleness and idiosyncratic (asset specific) staleness. The authors provide a limit theory based on joint asymptotics, utilizing increasingly-frequent observations and an increasing number of assets. They introduce novel structural estimates of systematic and idiosyncratic measures of liquidity obtained solely from transaction prices, and assess the economic signal contained in these estimates using suitable metrics.
JOURNAL OF ECONOMETRICS
(2024)
Article
Economics
Vassilis Hajivassiliou, Frederique Savignac
Summary: The paper develops new methods for establishing coherency and completeness conditions in Static and Dynamic Limited Dependent Variables (LDV) Models. It characterizes the two distinct problems as empty-region incoherency and overlap-region incoherency or incompleteness and shows that the two properties can co-exist. The paper focuses on the class of models that can be Simultaneously Incomplete and Incoherent (SII) and proposes estimation strategies based on Conditional Maximum Likelihood Estimation (CMLE) for simultaneous dynamic LDV models.
JOURNAL OF ECONOMETRICS
(2024)
Article
Economics
Thomas Macurdy, David Glick, Sonam Sherpa, Sriniketh Nagavarapu
Summary: In a successful transition from youth to adulthood, individuals go through a series of roles in school, work, and family formation, culminating in becoming self-sufficient adults. However, some disconnected youth spend significant time outside of any role that leads to adult independence. Understanding the meaning of disconnection, the number of disconnected youth, their characteristics, and how the problem has evolved is essential in assisting these youth. Using comprehensive data, a study examined disconnection spells and found that in the early 2000s, approximately 19% of young men and 25% of young women experienced disconnection before the age of 23. These rates were even higher for certain sub-groups, reaching over 30% for some. The study also revealed that the majority of disconnected youth remained disconnected for more than a year, but once reconnected, they typically stayed connected for at least three years. The findings highlight the need for targeted interventions to prevent lengthy disconnection spells.
JOURNAL OF ECONOMETRICS
(2024)
Article
Economics
Jean-Jacques Forneron
Summary: This paper develops an approach to detect identification failure in moment condition models by introducing a quasi-Jacobian matrix. The quasi-Jacobian matrix is singular when local and/or global identification fails, and equivalent to the usual Jacobian matrix when the model is globally and locally identified. A simple test is introduced to conduct subvector inferences allowing for various levels of identification without prior knowledge about the underlying identification structure.
JOURNAL OF ECONOMETRICS
(2024)
Article
Economics
Zhao Chen, Vivian Xinyi Cheng, Xu Liu
Summary: This paper focuses on the testing problems of high-dimensional quantile regression and proposes a new test statistic based on the quantile regression score function. The paper investigates the limiting distributions of the proposed test statistic and shows through Monte Carlo simulations and empirical analysis that the proposed method outperforms existing methods in terms of controlling error rate and power.
JOURNAL OF ECONOMETRICS
(2024)