Working Papers

Seria wydawnicza WNE WORKING PAPERS prowadzona jest przez WNE UW od 2008 r. i jak dotąd opublikowano w niej ponad 400 prac.

Seria WNE Working Papers służy szybkiemu upowszechnianiu wyników badań prowadzonych na Wydziale Nauk Ekonomicznych UW. Publikowane tu teksty mają charakter wersji roboczych (pre-printów): mogą być uzupełniane lub poprawiane w miarę postępu prac nad tekstem i procesu wydawniczego. Prezentujemy aktualną wersję udostępnioną przez autorów, opatrzoną datą oraz identyfikatorami DOI/ISSN – co ułatwia jej cytowanie i śledzenie kolejnych iteracji. Każdy dokument zachowuje pełne prawa autorskie swoich twórców, a w przypadku późniejszych zmian, autorzy mogą w dowolnym momencie zamieścić zaktualizowany plik lub wskazać miejsce ostatecznego wydania.

Do serii WORKING PAPERS przyjmowane są artykuły pracowników naukowych WNE UW oraz publikacje z konferencji organizowanych na WNE UW. Artykuły powinny dotyczyć ekonomii, mieć charakter oryginalnych prac badawczych, nie być wcześniej publikowane. Przyjmowane są wyłącznie teksty w języku angielskim.

Prace prosimy przesyłać e-mailem na adres: VgB}9`{e&Ea'M*#cmL@!=.uod$r]#[NW1i}OcVe2GuAE}RSU2wGnZemq_

Prosimy przesyłać dwa pliki:

(1) główny tekst bez podania tytułu artykułu oraz autorów (plik DOC/DOCX) oraz

(2) stronę tytułową zawierającą: tytuł opracowania, autorów oraz ich afiliację (plik DOC/DOCX). 

Przed przesłaniem tekstu prosimy zapoznać się z szczegółowymi wymogami edycyjnymi.

Redaktorem wydawniczym serii Working Papers jest dr Maciej Bukowski.

Ilość wyników 4

WP(7/2026)501. Causal Inference under Algorithmic Interference: Identification and Estimation without SUTVA in Platform Economies

Autorzy: Ryłow Jakub
The Stable Unit Treatment Value Assumption (SUTVA) fails systematically in platform economies where a deterministic algorithm mediates all interactions, making interference structural and mechanistically knowable. We introduce algorithmic interferenc…

WP501. Causal Inference under Algorithmic Interference: Identification and Estimation without SUTVA in Platform Economies

The Stable Unit Treatment Value Assumption (SUTVA) fails systematically in platform economies where a deterministic algorithm mediates all interactions, making interference structural and mechanistically knowable. We introduce algorithmic interference — a structural potential-outcomes model in which spillovers flow through the platform's known decision rule — and construct the Debiased Algorithmic Instrumental Variable (DAIV) estimator: a cross-fitted semiparametric procedure combining Double Machine Learning with the IV equation implied by the algorithmic mechanism. Under local algorithmic monotonicity (LAM), both the ATE and CATE are point identified; without LAM the sharp identified set is characterised. DAIV is sqrt(n)-consistent, asymptotically normal, and semiparametrically efficient, with a formal LAM test supplied. A synthetic ride-sharing example (n = 10,000) shows that standard DML overstates the treatment effect by 52% relative to DAIV; a Hausman-type specification test strongly rejects no algorithmic interference.


DOI: https://doi.org/10.33138/2957-0506.2026.7.501
Ryłow Jakub
algorithmic interference SUTVA potential outcomes causal identification double machine learning platform economies instrumental variables semiparametric efficiency partial identification

WP(8/2025)471. Monetary-fiscal interactions during large-scale asset purchase programs

Autorzy: Marcin Kolasa, Małgorzata Walerych, Wesołowski Grzegorz
This paper examines the effects of asset purchase programs (APPs) that were implemented in a number of countries during the COVID-19 pandemic in concert with large fiscal stimulus packages. We identify APP shocks for 14 advanced and emerging market e…

WP471. Monetary-fiscal interactions during large-scale asset purchase programs

This paper examines the effects of asset purchase programs (APPs) that were implemented in a number of countries during the COVID-19 pandemic in concert with large fiscal stimulus packages. We identify APP shocks for 14 advanced and emerging market economies using high-frequency identification techniques. We next estimate panel local projections, finding that APPs tend to stimulate output, but decrease prices. By using a Kitagawa-Blinder-Oaxaca decomposition, we demonstrate that these responses significantly depend on the magnitude of the simultaneously applied fiscal stimulus. Remarkably, higher government purchases during that period crowded in private consumption and had a large effect on inflation. We show that these empirical findings, some of which are inconsistent with a standard New Keynesian framework, can be rationalized in a simple general equilibrium model with segmented asset markets and fiscal dominance.


DOI: https://doi.org/10.33138/2957-0506.2025.8.471
Marcin Kolasa, Małgorzata Walerych, Wesołowski Grzegorz
asset purchases monetary-fiscal interactions fiscal dominance high-frequency identification local projections general equilibrium models

WP(10/2022)386. Subjective Expectations and Uncertainty

Autorzy: Kocięcki Andrzej, Łyziak Tomasz, Stanisławska Ewa
Analysis of macroeconomic expectations of private sector agents reveals not only the path of expected macroeconomic developments and the mechanism of expectation formation, but also the degree of uncertainty faced by economic agents. Relying on this …

WP386. Subjective Expectations and Uncertainty

Analysis of macroeconomic expectations of private sector agents reveals not only the path of expected macroeconomic developments and the mechanism of expectation formation, but also the degree of uncertainty faced by economic agents. Relying on this observation the aim of this paper is twofold. First, we lay down the formal theory of subjective expectations and derive the subjective assessment hypothesis, which defines the optimal forecast of an individual under a specific loss function. Keeping the mathematical rigor, the proposed theory attempts to reflect the process of expectation formation that is consistent with well known behavioral features. Second, we apply the subjective assessment hypothesis to pin down the notion of uncertainty. We propose a novel uncertainty index, retrieved from forecast revisions of professional forecasters using an empirical model within the Bayesian approach. The two versions of this index derived for the US—based either on GDP growth or on inflation forecasts—describe different kinds of macroeconomic uncertainty. Their shocks act similarly as demand shocks, pushing economic activity and inflation down.


Kocięcki Andrzej, Łyziak Tomasz, Stanisławska Ewa
Subjective expectations Uncertainty Stochastic volatility Identification Bayesian approach Survey of Professional Forecasters

WP(1/2022)377. A solution to the global identification problem in DSGE models.

Autorzy: Kocięcki Andrzej, Kolasa Marcin
We develop an analytical framework to study global identification in structural models with forward-looking expectations. Our identification condition combines the similarity transformation linking the observationally equivalent state space systems w…

WP377. A solution to the global identification problem in DSGE models.

We develop an analytical framework to study global identification in structural models with forward-looking expectations. Our identification condition combines the similarity transformation linking the observationally equivalent state space systems with the constraints imposed on them by the model parameters. The key step of solving the identification problem then reduces to finding all roots of a system of polynomial equations. We show how it can be done using the concept of a Gröbner basis and recently developed algorithms to compute it analytically. In contrast to papers relying on numerical search, our approach can effectively prove whether a model is identified or not at the given parameter point, explicitly delivering the complete set of observationally equivalent parameter vectors. We present the solution to the global identification problem for several popular DSGE models. Our findings indicate that observational equivalence in medium-sized models of this class might be actually not as widespread as suggested by earlier, small model-based evidence.


Kocięcki Andrzej, Kolasa Marcin
global identification state space systems DSGE models Gröbner basis
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