Photo of Laura Liu
Assistant Professor
Department of Economics
Indiana University
Wylie Hall 205
100 South Woodlawn Ave.
Bloomington, IN 47405
Email: lauraliu (at) iu.edu
           liuyu1237 (at) gmail.com
CV: PDF
My IU Page: Link
My Google Scholar Page: Link
News:
  • Publication:
    2022: Full-Information Estimation of Heterogeneous Agent Models Using Macro and Micro Data, Quantitative Economics, forthcoming
    2022: Forecasting with a Panel Tobit Model, Quantitative Economics, forthcoming
    2021: Density Forecasts in Panel Data Models: A Semiparametric Bayesian Perspective, Journal of Business & Economic Statistics, forthcoming
    2021: Monetary Policy across Space and Time, Essays in Honour of Fabio Canova, forthcoming
  • Updated working papers:
    2021: Identification and Estimation of APE in Semiparametric Binary Response Panel Models
  • Others:
    2022: I am happy to serve as Associate Editor for Journal of Applied Econometrics and Journal of Econometric Methods

Research
Publications
Full-Information Estimation of Heterogeneous Agent Models Using Macro and Micro Data
Joint with Mikkel Plagborg-Møller (Princeton)
Quantitative Economics, forthcoming
Working Paper Version
Also available at arXiv 2101.04771 and CAEPR Working Paper 2021-001
Online Appendix and Replication Files
Forecasting with a Panel Tobit Model
Joint with Hyungsik Roger Moon (USC) and Frank Schorfheide (UPenn)
Quantitative Economics, forthcoming
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
Journal of Business & Economic Statistics, forthcoming
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
Monetary Policy across Space and Time
Joint with Christian Matthes (Indiana) and Katerina Petrova (UPF)
Essays in Honour of Fabio Canova, forthcoming
Working Paper Version
Earlier version is available at FRB Richmond Working paper 18-14
Online Appendix
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
Commodity Connectedness
Joint with Francis X. Diebold (UPenn) and Kamil Yılmaz (Koç)
In E. Mendoza, D. Saravia and E. Pasten (eds.), Monetary Policy and Global Spillovers: Mechanisms, Effects and Policy Measures. Santiago: 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
Empirical Macro II (E724)
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 (E471/M504)
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 (180.637)
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.