This work examines the economic benefits of learning a new skill from a different domain: cross-skilling. To assess this, a network of skills from the job profiles of 4,810 online freelancers is constructed. Based on this skill network, relationships between 3,525 different skills are revealed and marginal effects of learning a new skill can be calculated via workers' wages. The results indicate that the added economic value of learning a new skill strongly depends on the already existing skill bundle but that acquiring a skill from a different domain is often beneficial. Likewise, the data illustrate how to reveal valuable skills required for new and opaque technology domains, such as Artificial Intelligence. As technological and social transformation is reshuffling jobs' task profiles at a fast pace, the findings of this study help to clarify skill sets required for mastering new technologies and designing individual training pathways. This can help to increase employability and reduce labour market shortages.

The Hicks induced innovation hypothesis states that a price increase of a production factor is a spur to invention. We propose an alternative hypothesis restating that a spur to invention require not only an increase of one factor but also a decrease of at least one other factor to offset the companies' cost. We illustrate the need for our alternative hypothesis in a historical example of the industrial revolution in the United Kingdom. Furthermore, we econometrically evaluate both hypotheses in a case study of research and development (R&D) in 29 OECD countries from 2003 to 2017. Specifically, we investigate dependence of investments to R&D on economic environment represented by average wages and oil prices using panel regression. We find that our alternative hypothesis is supported for R&D funded and/or performed by business enterprises while the original Hicks hypothesis holds for R&D funded by the government and R&D performed by universities. Our results reflect that business sector is significantly influenced by market conditions, unlike the government and higher education sectors.

Data augmentation methods in combination with deep neural networks have been used extensively in computer vision on classification tasks, achieving great success; however, their use in time series classification is still at an early stage. This is even more so in the field of financial prediction, where data tends to be small, noisy and non-stationary. In this paper we evaluate several augmentation methods applied to stocks datasets using two state-of-the-art deep learning models. The results show that several augmentation methods significantly improve financial performance when used in combination with a trading strategy. For a relatively small dataset ($\approx30K$ samples), augmentation methods achieve up to $400\%$ improvement in risk adjusted return performance; for a larger stock dataset ($\approx300K$ samples), results show up to $40\%$ improvement.

Complex computational models are increasingly used by business and governments for making decisions, such as how and where to invest to transition to a low carbon world. Complexity arises with great evidence in the outputs generated by large scale models, and calls for the use of advanced Sensitivity Analysis techniques. To our knowledge, there are no methods able to perform sensitivity analysis for outputs that are more complex than scalar ones and to deal with model uncertainty using a sound statistical framework. The aim of this work is to address these two shortcomings by combining sensitivity and functional data analysis. We express output variables as smooth functions, employing a Functional Data Analysis (FDA) framework. We extend global sensitivity techniques to function-valued responses and perform significance testing over sensitivity indices. We apply the proposed methods to computer models used in climate economics. While confirming the qualitative intuitions of previous works, we are able to test the significance of input assumptions and of their interactions. Moreover, the proposed method allows to identify the time dynamics of sensitivity indices.

Are low-income individuals relying on government transfers liquidity constrained by the end of the month to a degree that they postpone medical treatment? I investigate this question using Danish administrative data comprising the universe of welfare recipients and the filling of all prescription drugs. I find that on transfer income payday, recipients have a 52% increase in the propensity to fill a prescription. By separating prophylaxis drugs used to treat chronic conditions, where the patient can anticipate the need to fill the prescription, e.g. cholesterol-lowering statins, I find an increase of up to 99% increase on payday. Even for drugs used to treat acute conditions, where timely treatment is essential, I find a 22% increase on payday for antibiotics and a 5-8% decrease in the four days preceding payday. Lastly, exploiting the difference in day the doctor write the prescription and the day the patient fill it, I show that liquidity constraints is the key operating mechanism for postponing antibiotic treatment.

We consider the problem of determining an upper bound for the value of a spectral risk measure of a loss that is a general nonlinear function of two factors whose marginal distributions are known, but whose joint distribution is unknown. The factors may take values in complete separable metric spaces. We introduce the notion of Maximum Spectral Measure (MSP), as a worst-case spectral risk measure of the loss with respect to the dependence between the factors. The MSP admits a formulation as a solution to an optimization problem that has the same constraint set as the optimal transport problem, but with a more general objective function. We present results analogous to the Kantorovich duality, and we investigate the continuity properties of the optimal value function and optimal solution set with respect to perturbation of the marginal distributions. Additionally, we provide an asymptotic result characterizing the limiting distribution of the optimal value function when the factor distributions are simulated from finite sample spaces. The special case of Expected Shortfall and the resulting Maximum Expected Shortfall is also examined.

This paper is concerned with the analysis of blow-ups for two McKean-Vlasov equations involving hitting times. Let $(B(t); \, t \ge 0)$ be standard Brownian motion, and $\tau:= \inf\{t \ge 0: X(t) \le 0\}$ be the hitting time to zero of a given process $X$. The first equation is $X(t) = X(0) + B(t) - \alpha \mathbb{P}(\tau \le t)$. We provide a simple condition on $\alpha$ and the distribution of $X(0)$ such that the corresponding Fokker-Planck equation has no blow-up, and thus the McKean-Vlasov dynamics is well-defined for all time $t \ge 0$. Our approach relies on a connection between the McKean-Vlasov equation and the supercooled Stefan problem, as well as several comparison principles. The second equation is $X(t) = X(0) + \beta t + B(t) + \alpha \log \mathbb{P}(\tau > t)$, whose Fokker-Planck equation is non-local. We prove that for $\beta > 0$ sufficiently large and $\alpha$ no greater than a sufficiently small positive constant, there is no blow-up and the McKean-Vlasov dynamics is well-defined for all time $t \ge 0$. The argument is based on a new transform, which removes the non-local term, followed by a relative entropy analysis.

In this paper, I present a visual representation of the relationship between mean hourly total compensation divided by per-capita GDP, hours worked per capita, and the labor share, and show the represented labor equilibrium equation is the definition of the labor share. I also present visual examination of the productivity horizon and wage compression, and use these to show the relationship between productivity, available employment per capita, and minimum wage. From this I argue that wages are measured in relation to per-capita GDP, and that minimum wage controls income inequality and productivity growth.

The adoption of a "makeup" strategy is one of the proposals in the ongoing review of the Fed's monetary policy framework. Another suggestion, to avoid the zero lower bound, is a more active role for fiscal policy. We put together these ideas to study monetary-fiscal interactions under price level targeting. Under price level targeting and a fiscally-led regime, we find that following a deflationary demand shock: (i) the central bank increases (rather than decreases) the policy rate; (ii) the central bank, thus, avoids the zero lower bound; (iii) price level targeting is generally welfare improving if compared to inflation targeting.

We show that the barrier function in Root's solution to the Skorokhod embedding problem is continuous and finite at every point where the target measure has no atom and its absolutely continuous part is locally bounded away from zero.

Recent research on the response of stock prices to trading activity revealed long lasting effects, even across stocks of different companies. These results imply non-Markovian effects in price formation and when trading many stocks at the same time, in particular trading costs and price correlations. How the price response is measured depends on data set and research focus. However, it is important to clarify, how the details of the price response definition modify the results. Here, we evaluate different price response implementations for the Trades and Quotes (TAQ) data set from the NASDAQ stock market and find that the results are qualitatively the same for two different definitions of time scale, but the response can vary by up to a factor of two. Further, we show the key importance of the order between trade signs and returns, displaying the changes in the signal strength. Moreover, we confirm the dominating contribution of immediate price response directly after a trade, as we find that delayed responses are suppressed. Finally, we test the impact of the spread in the price response, detecting that large spreads have stronger impact.

How should central banks optimally aggregate sectoral inflation rates in the presence of imperfect labor mobility across sectors? We study this issue in a two-sector New-Keynesian model and show that a lower degree of sectoral labor mobility, ceteris paribus, increases the optimal weight on inflation in a sector that would otherwise receive a lower weight. We analytically and numerically find that, with limited labor mobility, adjustment to asymmetric shocks cannot fully occur through the reallocation of labor, thus putting more pressure on wages, causing inefficient movements in relative prices, and creating scope for central banks intervention. These findings challenge standard central banks practice of computing sectoral inflation weights based solely on sector size, and unveil a significant role for the degree of sectoral labor mobility to play in the optimal computation. In an extended estimated model of the U.S. economy, featuring customary frictions and shocks, the estimated inflation weights imply a decrease in welfare up to 10 percent relative to the case of optimal weights.