Laura Liu
Associate Professor
Department of Economics
University of Pittsburgh
4527 Wesley W. Posvar Hall
230 S. Bouquet St.
Pittsburgh, PA 15260
Email: laura.liu (at) pitt.edu
           liuyu1237 (at) gmail.com
CV: PDF
My Pitt Page: Link
My Google Scholar Page: Link
News:
  • Publication:
    2024: Identification and Estimation of Partial Effects in Nonlinear Semiparametric Panel Models, Journal of Econometrics
    2023: Density Forecasts in Panel Data Models: A Semiparametric Bayesian Perspective, Journal of Business & Economic Statistics
    2023: Full-Information Estimation of Heterogeneous Agent Models Using Macro and Micro Data, Quantitative Economics
    2023: Forecasting with a Panel Tobit Model, Quantitative Economics
  • Updated working papers:
    2024: Binary Outcome Models with Extreme Covariates: Estimation and Prediction
  • I am happy to serve as Associate Editor for Journal of Applied Econometrics, The Econometrics Journal, and Journal of Econometric Methods

Research
Peer-Reviewed Publications
Identification and Estimation of Partial Effects in Nonlinear Semiparametric Panel Models
Joint with Alexandre Poirier (Georgetown) and Ji-Liang Shiu (Jinan U)
Journal of Econometrics, forthcoming
Working Paper Version
Also available at arXiv 2105.12891
Online Appendix
Journal of Business & Economic Statistics, 2023, vol. 41 (2), pp. 349-363
Working Paper Version
Also available at arXiv 1805.04178
Earlier version is available at CAEPR Working Paper 2020-003, FEDS Working paper 2170, and PIER Working Paper 17-006
Joint with Hyungsik Roger Moon (USC) and Frank Schorfheide (UPenn)
Quantitative Economics, 2023, vol. 14 (1), pp. 117-159
Working Paper Version
Also available at arXiv 2110.14117
Earlier version is available at NBER Working Paper 26569 and CAEPR Working Paper 2019-005
Replication Files
Joint with Hyungsik Roger Moon (USC) and Frank Schorfheide (UPenn)
Journal of Econometrics, vol. 200 (1), pp. 2-22
Working Paper Version
Earlier version is available at NBER Working Paper 27248 and CEPR Working Paper 14790
Current forecasts and replication files are available at https://laurayuliu.com/covid19-panel-forecast/
A blog post is available here
Joint with Hyungsik Roger Moon (USC) and Frank Schorfheide (UPenn)
Econometrica, vol. 88 (1), pp. 171-201
Working Paper Version
Earlier versions are available at NBER Working Paper 25102, arXiv 1709.10193, and PIER Working Paper 16-022
Replication Files
Joint with Mert Demirer (MIT), Francis X. Diebold (UPenn), and Kamil Yılmaz (Koç)
Journal of Applied Econometrics, 2018, vol. 33 (1), pp. 1-15
Working Paper Version
Earlier versions are available at NBER Working Paper 23140 and PIER Working Paper 15-025
Other Publications
Monetary Policy across Space and Time
Joint with Christian Matthes (Indiana) and Katerina Petrova (UPF)
In Essays in Honour of Fabio Canova, Advances in Econometrics, 2022, vol. 44, pp. 37-64
Working Paper Version
Earlier version is available at FRB Richmond Working paper 18-14
Online Appendix
Commodity Connectedness
Joint with Francis X. Diebold (UPenn) and Kamil Yılmaz (Koç)
In Monetary Policy and Global Spillovers: Mechanisms, Effects and Policy Measures, Bank of Chile Central Banking Series, 2018, vol. 25, pp. 97-136
Working Paper Version
Earlier versions are available at NBER Working Paper 23685 and PIER Working Paper 17-003
Working Papers

Teaching
Advanced Econometrics II
University of Pittsburgh, advanced Ph.D. level, Spring 2024
Course Description
This is an advanced graduate course on econometric methodology. It is designed for students interested in econometric theory as well as those interested in applying econometric methods in empirical research. We first introduce/review nonlinear parametric estimation and extremum estimators. We then cover a wide range of topics on the frontier of both theoretical and empirical research, including panel data models, treatment effects, shrinkage estimation, and machine learning and double robustness.
Quantitative Methods
University of Pittsburgh, master's level, Fall 2023
Course Description
Quantitative Methods presents a framework for data-driven decision making under conditions of uncertainty and partial information, and it covers data analysis methods and techniques used in economic applications. The class will use R throughout; among the topics covered are graphical and descriptive data analysis, conditional probability, random variables, distribution functions, sampling, estimation, confidence intervals, hypothesis testing, and an introduction to regression methods.
Econometrics: A Mathematical Approach
Princeton University, undergraduate level, Spring 2023
Course Description
This course is an introduction to econometrics. Econometrics is a sub-discipline of statistics that provides methods for inferring economic structure from data. This course has two goals. The first goal is to give you means to evaluate an econometric analysis critically and logically. Second, you should be able to analyze a data set methodically and comprehensively using the tools of econometrics.
Empirical Macro II
Indiana University, advanced Ph.D. level, Spring 2021, Spring 2022
Course Description
This is an advanced graduate course on models and methods that are useful to conduct substantive empirical research in macroeconomics, finance, etc. It focuses on the estimation and evaluation of dynamic stochastic general equilibrium models (DSGE). If time permits, we will also discuss a diverse set of potential topics, such as estimation of heterogeneous agent models and big data and network connectedness.
Econometric Theory and Practice
Indiana University, advanced undergraduate and master's level, Spring 2020, Fall 2020, Fall 2021
Course Description
This course introduces students to basic econometric concepts and their applications. Compared to E371, we will put more emphasis on the mathematical and statistical foundations of econometric methods. The course covers linear and nonlinear regression models, discrete choice models, and panel data models. We will also discuss more advanced topics, such as experiments and quasi-experiments as well as big data and statistical learning, if time permits. All concepts are motivated by real-world applications. Students will learn how to apply econometric methods to data using the statistical software R.
Microeconometrics
Johns Hopkins University, advanced Ph.D. level, Fall 2018
Course Description
This is an advanced graduate course on major econometric techniques and models that are used in empirical microeconomics. We first introduce econometric theories of nonlinear extremal estimation, nonparametric estimation, and semiparametric estimation. Then, we discuss applications of these theories to limited dependent variable models, selection models, panel data models, and endogenous treatment models with unobserved heterogeneity.