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Pierre Hoonhout

Associate Professor in Econometrics

ISEG (Lisbon School of Economics and Management)

Biography

Pierre Hoonhout is associate professor in econometrics at the Lisbon School of Economics and Management (ISEG). His research interests include panel data, panel data attrition, missing data, causal inference, policy evaluation, response based sampling and reproducible research using R and Stata.

Interests

  • Econometrics
  • Statistics
  • Missing Data
  • Causal Inference
  • Policy Evaluation
  • Reproducible Research using Stata and R

Education

  • PhD in Econometrics

    University College London

  • Msc in Mathematical Statistics

    Universiteit van Amsterdam

Experience

 
 
 
 
 

Lecturer

Gades and cemapre

January 2015 – Present Lisbon

R courses in the following topics:

  • Introduction to R and RStudio
  • Introductory Statistics
  • Linear Regression Models
  • Time-Series Analysis
  • Multivariate Statistics
 
 
 
 
 

Lecturer

Timberlake Portugal and Timberlake UK

January 2012 – Present Lisbon

Stata courses in the following topics:

  • Introduction to Stata
  • Linear Regression Models
  • Nonlinear Regression Models
  • Systems of Linear Regression Models
  • Panel Data Models
  • Causal Inference
  • Policy Evaluation
  • Structural Equation Models
  • Generalized Structural Equation Models
  • Stata Programming
  • Mata Programming
 
 
 
 
 

Associate Professor

ISEG (Lisbon School of Economics and Management

January 2011 – Present Lisbon
Econometrics and Statistics courses (undergraduate, masters and Ph.D level).

Recent Posts

Hugo Shortcodes

Include HTML snippets into your posts.
Last updated on Sep 24, 2019 1 min read

RMarkdown: Print a code chunk "as is"

Printing code-chunks as-is can be a nightmare.
Last updated on Sep 24, 2019 1 min read

Add a Thumbnail and a Header-Image to a Blogpost using the Hugo-Academic Theme and blogdown

If you use the blogdown package to create your content, it is not obvious how to do this. This post contains a simple solution.
Last updated on Sep 23, 2019 2 min read

Featured Publications

September 2019 Journal of Business and Economic Statistics, 37(3)

Nonignorable Attrition in Multi-period Panels with Refreshment Samples

In panel surveys, some observation units drop out before the end of the observation period. This panel attrition cannot be ignored if it is related to the variables of interest. Hirano, Imbens, Ridder and Rubin (Econometrica, 2001) propose the Additively Nonignorable (AN) attrition model to correct for the potential selectivity of the attrition in panels with two periods. If a refreshment sample is available in the second period, their model nonparametrically just-identifies the population distribution and the observation probability. We propose the Sequential Additively Nonignorable attrition model, that just-identifies the population distribution and the sequence of observation hazards for panels with more than two periods.

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Recent Publications

Nonignorable Attrition in Multi-period Panels with Refreshment Samples

In panel surveys, some observation units drop out before the end of the observation period. This panel attrition cannot be ignored if …
PDF

Nonignorable Attrition in Multi-period Panels with Refreshment Samples (working paper)

In panel surveys, some observation units drop out before the end of the observation period. This panel attrition cannot be ignored if …
PDF DOI

Identification and Estimation of Panel Data Models with Attrition Using Refreshment Samples

This thesis deals with attrition in panel data. The problem associated with attrition is that it can lead to estimation results that …
PDF

Waiting Lists, Waiting Times and Admissions: an Empirical Analysis at Hospital and General Practice Level

We report an empirical analysis of the responses of the supply and demand for secondary care to waiting list size and waiting times. …
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Waiting Lists, Waiting Times and Admissions: an Empirical Analysis at Hospital and General Practice Level (Working Paper)

We report an empirical analysis of the responses of the supply and demand for secondary care to waiting list size and waiting times. …
PDF DOI