Abstract
This thesis consists of four papers that analyze how financial market data can be leveraged to gain timely insights into macroeconomic trends. Each paper stresses the relevance of financial market data - be it stock prices, commodity prices, or firm-level data - in enhancing the analysis of macroeconomic trends and in generating more accurate nowcasts and forecasts. To this aim, novel datasets are assembled and/or new econometric methods are developed. The first paper analyzes unemployment-generating supply shocks, proposing a structural vector autoregressive model identified with a set of sign restrictions. Results show that unemployment-generating supply shocks help explaining movements in macroeconomic and financial variables. An application to the US pharmaceutical industry finds that the supply shock caused by Covid-19 in the sector can be classified as such a shock, which increased industrial production and the unemployment rate, while decreasing producer prices. The second paper investigates the impact of Private Equity buyouts on productivity, using a novel sample of over 10,000 European PE deals. It documents total factor productivity losses of about 15% in the five years following a PE buyout. This decline is driven by a substantial increase in input factors, capital (+21%) and labor (+23%), and modest output growth (+7%). We further explore the heterogeneity of these effects across firm, deal, and investor characteristics, contributing to the broader understanding of the real outcome of PE investments. The third paper proposes a non-parametric test for Granger causality in quantiles to detect causality from a high-frequency driver to a low-frequency target. In an economic application, Granger causality between inflation and a selection of commodity futures is analyzed. Results show that given commodity futures Granger cause inflation at the lower quantiles of the distribution and that incorporating commodity futures in a nowcasting model enhances short-term forecasting accuracy, leveraging timely data for more precise nowcasting of inflationary trends. The fourth paper proposes a new measure of median inflation. Guiding monetary policies and investment decisions, inflation metrics play a crucial role. While traditional measures like core inflation offer insights, they often overlook asymmetries in price dynamics. This paper introduces a new approach that builds on the quantile factor models introduced by Chen et al. (2021) to extract a stable median inflation indicator from disaggregated sectoral-level data. Empirical analysis using Price Index for Personal Consumption Expenditures (PCE) data demonstrates the efficacy of the proposed inflation measure in capturing both common and idiosyncratic components of inflation, providing valuable insights for policymakers and investors alike.