Kamila Nowakowicz

I am a PhD candidate in Economics at the LSE and I am on the academic job market in 2024/25. My research focuses on econometric theory.

Working papers

Nonparametric network bootstrap ( latest draft)

Inference on network data is challenging due to the strong dependence between observations, which renders standard techniques incorrect. To address this, I propose a valid bootstrap procedure for network data based on a nonparametric linking function estimator. I characterise the conditions under which this estimator is uniformly consistent. I prove that the distribution of the bootstrap network is consistent for the distribution of the original network in terms of a Wasserstein distance. I also provide conditions under which distributions of a class of functions related to U-statistics on the bootstrapped networks consistently replicate the distributions of the corresponding statistics on the original network. Monte Carlo simulations show good confidence interval coverage for a wider class of network functions than those accounted for by my theory. I apply my method to the data from Banerjee, Chandrasekhar, Duflo, and Jackson (2013): I replicate their findings, but also show that my method works under weaker assumptions and with a significantly smaller sample size. Finally, I propose an alternative specification of their model which takes advantage of my linking function estimator and may be of interest independently of my bootstrap procedure.

Understanding regression shape changes through nonparametric testing (with Tatiana Komarova) (draft coming soon)

We propose a procedure for testing whether a nonparametric regression mean satisfies a shape restriction that varies within the domain of the regressor. Notably, the change points of these shape restrictions are unknown and must be estimated. Our test statistic is based on the empirical process, drawing inspiration from Khmaladze (1982). This paper extends the nonparametric methodology of Komarova and Hidalgo (2023) by proposing a method to estimate the shape change points and consequently addressing the additional estimation errors introduced by that stage. We analyse strategies for managing these errors and adapting the testing approach accordingly. Our framework accommodates various common shapes, such as (inverse) U-shapes, S-shapes, and W-shapes. Furthermore, our method is applicable to partial linear models, thereby encompassing a broad spectrum of applications. We demonstrate the efficacy of our approach through application to several economic problems and data.

Testing for additivity in nonparametric regression models (with Javier Hidalgo and Tatiana Komarova)

We describe and examine a test for additivity in a nonparametric framework using partial sums empirical processes. We show that, after a suitable transformation, its asymptotic distribution is a functional of $\mathcal{B} \left( F_{x}\left( x\right) \right) $, where $\mathcal{B}\left(x\right) $ is the standard Brownian sheet in $\left[ 0,1\right] ^{2}$ and $F_{x}\left(x\right) $ is the probability distribution function of $x\in \left[ 0,1\right] ^{2}$. Although the asymptotic behaviour does not depend on the model or its estimator, it is not pivotal. Due to the latter and the possible poor approximation of the asymptotic critical values to the finite sample ones, we also describe a valid bootstrap algorithm.

CV

You can find my CV here.

Contact information

Department of Economics
London School of Economics
Houghton Street
London WC2A 2AE
UK
k.nowakowicz@lse.ac.uk