Michael Lechner
Title
Prof. Dr.
Last Name
Lechner
First name
Michael
Email
michael.lechner@unisg.ch
ORCID
Phone
+41 71 224 28 14
208 results
Now showing 1 - 10 of 208
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Item type:Publication, Random Forest estimation of the ordered choice mode(2024-11-12); In this paper we develop a new machine learning estimator for ordered choice models based on the Random Forest. The proposed Ordered Forest flexibly estimates the conditional choice probabilities while taking the ordering information explicitly into account. In addition to common machine learning estimators, it enables the estimation of marginal effects as well as conducting inference and thus provides the same output as classical econometric estimators. An extensive simulation study reveals a good predictive performance, particularly in settings with nonlinearities and high correlation among covariates. An empirical application contrasts the estimation of marginal effects and their standard errors with an Ordered Logit model. A software implementation of the Ordered Forest is provided both in R and Python in the package orf available on CRAN and PyPI, respectively.Type:journal articleJournal:Empirical EconomicsVolume:68Issue:1Scopus© Citations 13 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Enabling Decision Making with the Modified Causal Forest: Policy Trees for Treatment Assignmen(2024-07-19); ; Federica MascoloDecision making plays a pivotal role in shaping outcomes across various disciplines, such as medicine, economics, and business. This paper provides practitioners with guidance on implementing a decision tree designed to optimise treatment assignment policies through an interpretable and non-parametric algorithm. Building upon the method proposed by Zhou, Athey, and Wager (2023), our policy tree introduces three key innovations: a different approach to policy score calculation, the incorporation of constraints, and enhanced handling of categorical and continuous variables. These innovations enable the evaluation of a broader class of policy rules, all of which can be easily obtained using a single module. We showcase the effectiveness of our policy tree in managing multiple, discrete treatments using datasets from diverse fields. Additionally, the policy tree is implemented in the open-source Python package mcf (modified causal forest), facilitating its application in both randomised and observational research settings.Type:journal article - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The Heterogeneous Response of Real Estate Prices during the Covid-19 Pandemic(2024-08-01); ; Sandro HeinigerWe estimate the transmission of the pandemic shock in 2020 to the residential and commercial real estate market by causal machine learning, using granular data for Germany. We exploit differences in the incidence of Covid infections and short-time work at the municipal level for the identification of epidemiological and economic effects of the pandemic. We find that (i) a larger incidence of Covid infections temporarily reduced rents for retail real estate; (ii) a larger incidence of short-time work temporarily reduced rents of office real estate; (iii) the pandemic increased prices, particularly in the top price segment of commercial real estate.Type:journal article - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Do local expenditures on sports facilities affect sports participation?(2023); Scopus© Citations 6 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Individual Labor Market Effects of Local Public Expenditures on SportsType:journal articleJournal:Labour EconomicsVolume:70Issue:101996Scopus© Citations 5 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The Finite Sample Performance of Inference Methods for Propensity Score Matching and Weighting Estimators(American Statistical Association, 2020-01); ; This article investigates the finite sample properties of a range of inference methods for propensity score-based matching and weighting estimators frequently applied to evaluate the average treatment effect on the treated. We analyze both asymptotic approximations and bootstrap methods for computing variances and confidence intervals in our simulation designs, which are based on German register data and U.S. survey data. We vary the design w.r.t. treatment selectivity, effect heterogeneity, share of treated, and sample size. The results suggest that in general, theoretically justified bootstrap procedures (i.e., wild bootstrapping for pair matching and standard bootstrapping for “smoother” treatment effect estimators) dominate the asymptotic approximations in terms of coverage rates for both matching and weighting estimators. Most findings are robust across simulation designs and estimators.Type:journal articleJournal:Journal of Business & Economic StatisticsVolume:38Issue:1Scopus© Citations 36 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Endogeneity and non‐response bias in treatment evaluation – nonparametric identification of causal effects by instrumentsThis paper proposes a nonparametric method for evaluating treatment effects in the presence of both treatment endogeneity and attrition/non‐response bias, based on two instrumental variables. Using a discrete instrument for the treatment and an instrument with rich (in general continuous) support for non‐response/attrition, we identify the average treatment effect on compliers as well as the total population under the assumption of additive separability of observed and unobserved variables affecting the outcome. We suggest non‐ and semiparametric estimators and apply the latter to assess the treatment effect of gym training, which is instrumented by a randomized cash incentive paid out conditional on visiting the gym, on self‐assessed health among students at a Swiss university. The measurement of health is prone to non‐response, which is instrumented by a cash lottery for participating in the follow‐up survey.Type:journal articleJournal:Journal of Applied EconometricsVolume:35Issue:5DOI:10.1002/jae.2764Scopus© Citations 10 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Does the Estimation of the Propensity Score by Machine Learning Improve Matching Estimation? The Case of Germany's Programmes for Long Term UnemployedMatching-type estimators using the propensity score are the major workhorse in active labour market policy evaluation. This work investigates if machine learning algorithms for estimating the propensity score lead to more credible estimation of average treatment effects on the treated using a radius matching framework. Considering two popular methods, the results are ambiguous: We find that using LASSO based logit models to estimate the propensity score delivers more credible results than conventional methods in small and medium sized high dimensional datasets. However, the usage of Random Forests to estimate the propensity score may lead to a deterioration of the performance in situations with a low treatment share. The application reveals a positive effect of the training programme on days in employment for long-term unemployed. While the choice of the “first stage” is highly relevant for settings with low number of observations and few treated, machine learning and conventional estimation becomes more similar in larger samples and higher treatment shares.Type:journal articleJournal:Labour EconomicsVolume:65(C)Issue:101855Scopus© Citations 19 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, For better or worse? –The effects of physical education on child developmentType:journal articleJournal:Labour EconomicsVolume:67Issue:101904 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo EvidenceType:journal articleJournal:Econometrics JournalScopus© Citations 104