What is he best known for?
The fields of study he is best known for:
- Statistics
- Econometrics
- Machine learning
Labour economics, Matching, Econometrics, German and Unemployment are his primary areas of study.
His Labour economics research includes themes of Matching methods, Market policy, Active labour and Active labour market policies.
His Matching research includes elements of Public sector, Actuarial science, Training and Demographic economics.
His work deals with themes such as Outcome, Selection, Estimator, Conditional independence and Causal model, which intersect with Econometrics.
His Estimator study integrates concerns from other disciplines, such as Nonparametric statistics, Sample, Monte Carlo method and Propensity score matching.
As part of one scientific family, Michael Lechner deals mainly with the area of Unemployment, narrowing it down to issues related to the Subsidy, and often Wage.
His most cited work include:
- Identification and estimation of causal effects of multiple treatments under the conditional independence assumption (588 citations)
- Program Heterogeneity and Propensity Score Matching: An Application to the Evaluation of Active Labor Market Policies (383 citations)
- Earnings and Employment Effects of Continuous Off-the-job Training in East Germany after Unification (372 citations)
What are the main themes of his work throughout his whole career to date?
Michael Lechner mainly focuses on Econometrics, Matching, German, Labour economics and Estimator.
His Econometrics study combines topics from a wide range of disciplines, such as Outcome, Conditional independence and Selection.
In Matching, Michael Lechner works on issues like Active labour market policies, which are connected to Matching methods.
His research in Labour economics intersects with topics in Subsidy, Human capital and Market policy, Unemployment, Active labour.
The various areas that Michael Lechner examines in his Unemployment study include Actuarial science and Social security.
His Estimator research incorporates elements of Covariate, Monte Carlo method and Propensity score matching.
He most often published in these fields:
- Econometrics (30.86%)
- Matching (26.00%)
- German (24.86%)
What were the highlights of his more recent work (between 2014-2021)?
- Estimator (22.00%)
- Econometrics (30.86%)
- Matching (26.00%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Estimator, Econometrics, Matching, Labour economics and Machine learning.
His Estimator study results in a more complete grasp of Statistics.
His study on Econometrics is mostly dedicated to connecting different topics, such as Advertising.
His work carried out in the field of Matching brings together such families of science as Panel data, Order, Propensity score matching and Demographic economics.
His Labour economics research is multidisciplinary, incorporating elements of Earnings and Human capital.
His studies in Earnings integrate themes in fields like Productivity and Unemployment.
Between 2014 and 2021, his most popular works were:
- Radius Matching on the Propensity Score with Bias Adjustment: Tuning Parameters and Finite Sample Behaviour. (57 citations)
- Sports and Child Development (46 citations)
- Practical procedures to deal with common support problems in matching estimation (31 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Econometrics
- Microeconomics
Michael Lechner spends much of his time researching Estimator, Matching, Labour economics, Econometrics and Statistics.
His Estimator study incorporates themes from Covariate, Monte Carlo method and Sample.
The concepts of his Matching study are interwoven with issues in Panel data, Wage, Demographic economics, Instrumental variable and Propensity score matching.
His study in Demographic economics is interdisciplinary in nature, drawing from both Profit, Social security and Active labour.
His Labour economics research incorporates themes from Earnings and Human capital.
His work is dedicated to discovering how Econometrics, Inference are connected with Value and Sample size determination and other disciplines.
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