Conference Agenda
Overview and details of the sessions of this conference.
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Some information on the session logistics:
If not stated otherwise, the discussant is the following speaker, with the first speaker being the discussant of the last paper. The last speaker of each session is the session chair. (Exception: invited sessions)
Presenters should speak for no more than 20 minutes, and discussants should limit their remarks to no more than 5 minutes. The remaining time should be reserved for audience questions and the presenter’s responses. We suggest following these guidelines also in the (less common) 3-paper sessions in a 2-hour slot, to allow participants to move between sessions. Discussants are encouraged to avoid summarizing the paper. By focusing on a few questions and comments, the discussants can help start a broader discussion with the audience.
Only registered participants can attend this conference. Further information available on the congress website https://www.iseg.ulisboa.pt/en/event/iipf/ .
Venue address: ISEG - Lisbon School of Economics & Management, R. Francesinhas 21, 1200-675 Lisboa, Portugal
Please note that all times are shown in the time zone of the conference. The current conference time is: 17th Sept 2026, 11:39:16am WEST
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Daily Overview |
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A08: Local Finance: Technology, Spillovers, and Economic Shocks Location: Room 108 (Francesinhas 1) | |
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Predicting Fiscal Distress of Local Governments with Machine Learning Methods: Empirical Evidence from Poland (2010-2023) University of Warsaw, Poland This article explores the application of machine learning models to identify financial difficulties in Polish local governments from 2010 to 2023. Poland's current public finance monitoring is reactive and lacks effective forecasting, complicating early detection of fiscal risks. The study examined 2,375 municipalities using a dataset of 33 financial, demographic, and macroeconomic variables. Four classification models were compared: logistic regression, LASSO regression, Random Forest, and XGBoost. Results indicated that XGBoost performed best, achieving an ROC AUC of 0.837 and an average precision of 0.723. SHAP value analysis identified operating profit per capita from the previous year and debt indicators as key predictors. The study highlights that these machine learning models can aid audit institutions, such as Regional Audit Chambers, in developing modern early-warning systems, enabling auditors to concentrate resources on entities in genuine financial distress, despite the challenges posed by sudden regulatory changes.
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