Koen Jochmans | MiMo

 

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ERC Starting Grant MiMo

ERC Consolidator Grant NETWORK

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Inference in Microeconometric Models (MiMo)
ERC Starting Grant 715787 (2017--2022)

ERC

Unobserved differences between economic agents are an important driver behind the differences in their economic outcomes such as schooling decisions, wages, and employment durations. Allowing for such unobserved heterogeneity in economic modelling equips the specification with an additional dimension of realism but presents major challenges for econometric practice. Hence, reconciling heterogeneity in the data with econometric models is an issue of utmost importance.

The aim of this project is to develop inference methods for models with unobserved heterogeneity by exploiting the identifying power of longitudinal (panel) data. The project consists of three Work Packages (WP). Together, they span the largest part of modern applications of panel data.

The first WP deals with inference on nonlinear models and enhances the performance of statistical hypothesis tests. So far, the literature has focused on point estimation. However, it is statistical inference that accounts for uncertainty in the data and forms the basis for testing economic restrictions. The second WP makes progress on the estimation of models for network data. The importance of social and economic connections is well established but few formal results are available. We exploit the fact that network data can be seen as a type of panel data to derive such results. The third WP uses panel data to identify and nonparametrically estimate dynamic discrete-choice models with unobserved type heterogeneity and/or latent state variables. Such results are inexistent even though dynamic discrete-choice models are a workhorse tool in labor economics and industrial organization.

The performance of the tools will be assessed theoretically and via simulation, and they will be applied to various empirical problems.

Research output

A. Higgins and K. Jochmans. Learning Markov processes with latent variables. Preprint, 2024.

A. Higgins and K. Jochmans. Bootstrap inference for fixed-effect models. Econometrica 92, 411-427.

A. Higgins and K. Jochmans. Identification of mixtures of dynamic discrete choices. Journal of Econometrics 237, 105462.

A. Higgins and K. Jochmans. Joint approximate asymmetric diagonalization by non-orthogonal matrices. Preprint, 2021.

K. Jochmans Peer effects and endogenous social interactions. Journal of Econometrics 235, 1203-1214.

A. Higgins. Fixed-T estimation of linear panel data models with interactive fixed effects. Preprint, 2021.

K. Jochmans and M. Weidner. Inference on a distribution from noisy draws. Econometric Theory 40, 60-97.

K. Jochmans and V. Verardi. Instrumental-variable estimation of exponential regression models with two-way fixed effects, with an application to gravity equations. Journal of Applied Econometrics 37, 1121–1137.

K. Jochmans. Heteroskedasticity-robust inference in linear regression models with many covariates. Journal of the American Statistical Association 117, 887–896.

K. Jochmans. Bias in instrumental-variable estimators of fixed-effect models for count data. Economics Letters 212, 110318.

K. Jochmans. Testing random assignment to peer groups. Journal of Applied Econometrics 38, 321-333.

K. Jochmans. A portmanteau test for correlation in short panels. Econometric Theory 36, 1159–1166.

K. Jochmans. Testing for correlation in error-component models. Journal of Applied Econometrics 35, 860–878.

K. Jochmans and V. Verardi. twexp and twgravity: Fitting exponential regression models with two-way fixed effects. Stata Journal 20, 468–480.

K. Jochmans and V. Verardi. xtserialpm: A portmanteau test for serial correlation in a linear panel model. Stata Journal 20, 149–161.

K. Jochmans and M. Weidner. Fixed-effect regressions on network data. Econometrica 87, 1543–1560.

K. Jochmans and T. Otsu. Likelihood corrections for two-way models. Annals of Economics and Statistics 134, 227–242.

K. Jochmans. Modified-likelihood estimation of fixed-effect models for dyadic data. SERIEs - Journal of the Spanish Economic Association 14, 417-433.

K. Jochmans. Semiparametric analysis of network formation. Journal of Business & Economic Statistics 36, 705–713.

S. Bonhomme, K. Jochmans, and J.-M. Robin. Nonparametric estimation of non-exchangeable latent-variable models. Journal of Econometrics 201, 237–248.

K. Jochmans and T. Magnac. A note on sufficiency in binary panel models. Econometrics Journal 20, 259–269.

Stata modules (co-written with Vincenzo Verardi.)

Peer effects: rassign, ivnet.

Testing in panel data: xtserialpm.

Estimation of two-way models: twexp, twgravity, ivgravity.

Events

Conference on Econometric Methods for Modern Data Structures, July 7-8, 2022. The program is here.

Conference on Estimation and Inference in Econometric Models, December 12-13, 2022.