About

Yong Bao is a Professor of Economics at Purdue University. His contact information includes the Department of Economics at Purdue University, located at 403 Mitch Daniels Blvd., West Lafayette, IN 47907, USA. He can be reached via telephone at 765-496-2313 or by email at ybao@purdue.edu. The available information indicates his role as a faculty member within the economics department, but does not provide additional details regarding his research focus, background, or key contributions.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Mathematics
• Natural Language Processing
• Statistics
• Applied mathematics
• Econometrics
• Finance

Selected publications

• Voluntary retirement savings in China: A spatial ordered probit approach

  Regional Science and Urban Economics · 2025-02-03

  article1st authorCorresponding
  PublisherDOI

• A Recentered Method of Moments Estimator for Spatial Dynamic Panels

  SSRN Electronic Journal · 2025-01-01

  articleOpen access1st authorCorresponding
  PublisherDOI

• Selection of spillover channels in spatial dynamic panel models using heterogeneous shrinkage on spatial parameters

  Spatial Economic Analysis · 2024-11-20 · 1 citations

  articleOpen access1st authorCorresponding

  This paper proposes a Bayesian approach to estimating heterogeneous spatial dynamic panel models, subject to possible shrinkage on spatial dependence parameters. This amounts to heterogeneous selection of candidate spatial weight matrices that represent different spillover channels. The shrinkage methods include both the traditional and more flexible ones that allow the shrinkage strength to vary across spatial parameters. Monte Carlo results indicate that when the true model has a relatively low proportion of nonzero spatial parameters, flexible shrinkage in general leads to lower average root mean squared errors in estimating these parameters. An empirical study using this approach shows that there exists substantial heterogeneity in spillover channels across counties that determine the correlation patterns of county COVID-19 vaccination rates in four states in the United States.

  PublisherOA PDFDOI

• Estimating spatial autoregressions under heteroskedasticity without searching for instruments

  Regional Science and Urban Economics · 2024-04-25

  article1st authorCorresponding
  PublisherDOI

• Estimating Linear Dynamic Panels with Recentered Moments

  Econometrics · 2024-01-17

  articleOpen access1st authorCorresponding

  This paper proposes estimating linear dynamic panels by explicitly exploiting the endogeneity of lagged dependent variables and expressing the crossmoments between the endogenous lagged dependent variables and disturbances in terms of model parameters. These moments, when recentered, form the basis for model estimation. The resulting estimator’s asymptotic properties are derived under different asymptotic regimes (large number of cross-sectional units or long time spans), stable conditions (with or without a unit root), and error characteristics (homoskedasticity or heteroskedasticity of different forms). Monte Carlo experiments show that it has very good finite-sample performance.

  PublisherOA PDFDOI

• A Spatial Sample Selection Model*

  Oxford Bulletin of Economics and Statistics · 2024-01-27 · 1 citations

  article1st author

  This paper presents a sample selection model with spatial correlation in the selection and outcome variables and studies the maximum likelihood method of estimation. Consistency and asymptotic normality of the maximum likelihood estimator are established by the spatial near‐epoch dependent properties of the variables. Monte Carlo simulations show its good finite‐sample performance. This model is used to examine the impact of climate change on cereal yields in Southeast Asia and projects that climate change may cause a reduction in cereal yields by () in the minimum‐change (maximum‐change) scenario.

  PublisherDOI

• Indirect inference estimation of higher-order spatial autoregressive models

  Econometric Reviews · 2023-02-07 · 4 citations

  articleOpen access1st authorCorresponding

  This paper proposes estimating parameters in higher-order spatial autoregressive models, where the error term also follows a spatial autoregression and its innovations are heteroskedastic, by matching the simple ordinary least squares estimator with its analytical approximate expectation, following the principle of indirect inference. The resulting estimator is shown to be consistent, asymptotically normal, simulation-free, and robust to unknown heteroskedasticity. Monte Carlo simulations demonstrate its good finite-sample properties in comparison with existing estimators. An empirical study of Airbnb rental prices in the city of Asheville illustrates that the structure of spatial correlation and effects of various factors at the early stage of the COVID-19 pandemic are quite different from those during the second summer. Notably, during the pandemic, safety is valued more and on-line reviews are valued much less.

  PublisherOA PDFDOI

• Indirect inference estimation of dynamic panel data models

  Journal of Econometrics · 2022-10-14 · 4 citations

  article1st authorCorresponding
  PublisherDOI

• DialogueGAT: A Graph Attention Network for Financial Risk Prediction by Modeling the Dialogues in Earnings Conference Calls

  2022 · 8 citations

  Senior authorCorresponding
  • Computer Science
  • Computer Science
  • Artificial Intelligence

  Financial risk prediction is an essential task for risk management in capital markets. While traditional prediction models are built based on the hard information of numerical data, recent studies have shown that the soft information of verbal cues in earnings conference calls is significant for predicting market risk due to its less constrained fashion and direct interaction between managers and analysts. However, most existing models mainly focus on extracting useful semantic information from the textual conference call transcripts but ignore their subtle yet important information of dialogue structures. To bridge this gap, we develop a graph attention network called DialogueGAT for financial risk prediction by simultaneously modeling the speakers and their utterances in dialogues in conference calls. Different from previous studies, we propose a new method for constructing the graph of speakers and utterances in a dialogue, and design contextual attention at both speaker and utterance levels for disentangling their effects on the downstream prediction task. For model evaluation, we extend an existing dataset of conference call transcripts by adding the dialogue structure and speaker information. Empirical results on our dataset of S&P1500 companies demonstrate the superiority of our proposed model over competitive baselines from the extant literature.

  DOI

• Heterogeneous spatial dynamic panels with an application to US housing data

  Spatial Economic Analysis · 2022-10-26 · 5 citations

  articleOpen access1st authorCorresponding

  This paper proposes two models that incorporate both heterogeneity and multiple sources of spatial correlation for dynamic panels. One uses convex combinations of them to form a single weight matrix. The second one includes explicitly different spatial weight matrices to form a higher order model. We use a Bayesian scheme for model estimation by deriving the full conditional distributions of heterogeneous parameters. Our Monte Carlo experiments demonstrate their finite-sample performance relative to a baseline model. In our empirical study we find the importance of including both geographical and non-geographical information in capturing correlations in real house price growth in the United States.

  PublisherOA PDFDOI

Frequent coauthors

• Aman Ullah

  University of California, Riverside

  23 shared

• Tae‐Hwy Lee

  University of California, Riverside

  5 shared

• Melody Lo

  The University of Texas at San Antonio

  5 shared

• X. Liu

  4 shared

• Wei Guo

  Wuhan Ship Development & Design Institute

  4 shared

• Julie Le Gallo

  Université Bourgogne Franche-Comté

  4 shared

• Victoria Zinde‐Walsh

  4 shared

• Raymond J.G.M. Florax

  Purdue University West Lafayette

  4 shared