I am a macroeconometrician with a primary interest in macroeconomics. In particular, I am interested in the study of household wealth concentration and its consequence on the macro economy. My work employs a variety of econometric techniques and I do not shy away from contributing to the econometric literature when useful for my macroeconomic investigation. My secondary research interest is time series analysis.

My job market paper, *Wealth Inequality, Uninsurable Entrepreneurial Risk and Market Power*, studies the effects of wealth inequality on firm entry and market power. I calibrate a Schumpeterian model of endogenous growth with heterogeneous households where firm entry is driven by entrepreneurial activity. I find that higher wealth inequality across households leads to lower entrepreneurial activity and a higher average firm markup. These results are supported by time series evidence. I estimate a panel vector autoregression (PVAR) with data from eight OECD countries and propose a new bootstrap-based bias-correction method that I apply to the estimation of the dynamic model to correct for the well-known bias of the maximum likelihood estimator in small sample.

My primary focus is macroeconomics, but I also work on theoretical econometrics projects wherever a new technique would be relevant to provide empirical support to my macroeconomic work. In a technical note titled *An Improved Nonparametric Estimator for Households Marginal Utility Function*, I propose an improvement to the nonparametric estimator of the marginal utility function proposed by \cite{Escanciano2015} based on the asset pricing Euler equation. The proposed estimator takes advantage of the desirable properties of the local-linear least-squares (LLLS) estimator and is based on a new set of less stringent assumptions that are easy to verify in practice. This technical note is to be the basis for future work on household utility function in which the assumption of homogeneous constant relative risk aversion (CRRA) utility across wealth levels is to be tested against other hypotheses.

In the field of theoretical econometrics, I also have a joint paper with Morten Orregard Nielson and Michael Jansson (R&R, Econometric theory) providing a proof for the distribution of the unconditional likelihood ratio test for the unit root. This new result provides for an efficient unit root test with better power properties than other powerful tests, such as \cite{Elliott1996}’s DF-GLS test, particularly in the presence of a trend.

As a hobby, I also work on asset value prediction using machine learning techniques. In the paper titled Can Positions of Large Traders Predict Daily Returns on the LBMA gold fix (work in progress), I investigate whether an investment strategy based on the position of large traders may yield unusually returns. The strategy is predicated on the possibility of insider information regarding the LBMA Gold Fix. I found that the information collected by the Commodity Futures Trading Commission about large traders positions does contain important predictive information on future gold prices at the time of survey, but this predictive power vanishes before the data is made public, three days later.

In the coming years, I intend to extend my work on wealth inequality. The framework I developed where firm entry is a consequence of entrepreneurship could be extended to examine other phenomena, such as the recent decline in initial public offerings. Quick progress is being made in the estimation of markups and I expect new estimation techniques to be soon available that relax some strong assumptions underlying the markup estimates of De Loecker, Eeckhout and Mongey (2020). Another interesting exercise would be to characterise the relationship between inequality and markups by industry and location.