I develop a life-cycle model of household portfolio decisions that accounts for heterogeneity in financial literacy and employ it to examine portfolio adjustments following household-level shocks. I use variation in unplanned births to parameterize the model and identify the margins of portfolio adjustments following household-level fertility shocks. Empirical evidence suggests that households increase the liquidity of their portfolios following such shocks. Using the model, I compare how households with different financial-literacy levels respond to similar shocks, and I show that higher financial literacy is associated with smoother portfolio adjustments following shock onset. All else equal, the more financially literate households appear less susceptible to the detrimental effects of liquidity constraints and the impact of portfolio-adjustment costs. The interaction between liquidity constraints and financial literacy plays a key role in the model, as it explains the differential speed and direction of portfolio adjustments observed in the data. Counterfactual exercises show that financial literacy mitigates the negative welfare effects of unexpected fertility shocks by at least 20%.

We study the accumulation of financial competencies in a model of dynamic skill formation. We find evidence of complementarities between financial literacy and risk attitudes. Risk tolerance facilitates experimentation and learning-by-doing. Latent risk attitudes and financial literacy are unevenly distributed across households and do not align with general human capital. Linking estimates with data on household portfolios, we show that early-life differences in financial literacy may account for more than half of the standard deviation of wealth by age 60. Dynamic complementarities in skill for- mation imply that early interventions could reduce later-life inequality while boosting wealth growth.

In the long run, we are all dead. Nonetheless, even when investigating short-run dynamics, models require boundary conditions on long-run, forward-looking behavior (e.g., transversality and no-bubble conditions). In this paper, we show how deep learning approximations can automatically fulfill these conditions despite not directly calculating the steady state, balanced growth path, or ergodic distribution. The main implication is that we can solve for transition dynamics with forward-looking agents, confident that long-run boundary conditions will implicitly discipline the short-run decisions, even converging towards the correct equilibria in cases with steady-state multiplicity. While this paper analyzes benchmarks such as the neoclassical growth model, the results suggest deep learning may let us calculate accurate transition dynamics with high-dimensional state spaces, and without directly solving for long-run behavior.

In this paper we test the effect of technological capabilities (accumulated knowledge and organization/production routines) on the R&D intensity for a panel of European industries. Our proxy for capabilities is the distance from the technological frontier. Estimation is carried out with System Generalized Methods of Moments and is robust to various specifications. Our identification strategy is limited to the average (reduced form) effect. We find a strong effect of capabilities on the amount invested in R&D, after controlling for demand pull, technology push, size, and cash constraints. The latter ones are the main variables used in the literature on the determinants of innovative expenditure, of which R&D is one of the components. The elasticity of the distance from the technological frontier is 10%, of similar magnitude (but opposite sign) with regard to the effect of internal resources. When we allow for heterogeneous impact, clustering the industries according to their technological level, we see that the effect of capabilities is robust, but concentrated in Medium and Low Tech sectors. Moreover, the effect is stronger in the upswings of the business cycle and is concentrated in peripheral countries. These latter stylized facts may suggest that the divergence induced by lack of capabilities is somehow nonlinear and increases when a critical mass is missing.

We estimate with System Generalized Method of Moments the effect of the distance from the technological frontier on the R&D intensity of companies, using a new European level database. In our estimation the companies that lag behind are less active in research activity.

The impact of the monetary policy on the exchange rate and other macroeconomic variables is analyzed for the case of Colombia, a small open economy. Block exogeneity restrictions are imposed in a novel way in a structural VAR, including local and foreign variables. The results are robust to various specifications and show a partial solution of the forward discount bias puzzle, i.e. partial evidence of the uncovered Interest parity (UIP). This was not the case in previous research with the same methodology for Colombia. It also finds strong evidence in favor of the central bank leaning against the wind and partial evidence of no pass through from exchange rate to inflation.