We propose an observation-driven time-varying SVAR model where, in agreement with the Lucas Critique, structural shocks drive both the evolution of the macro variables and the dynamics of the VAR parameters. Contrary to existing approaches where parameters follow a stochastic process with random and exogenous shocks, our observation-driven specification allows the evolution of the parameters to be driven by realized past structural shocks, thus opening the possibility to gauge the impact of observed shocks and hypothetical policy interventions on the future evolution of the economic system.

A group of heterogeneous players gathers information about an uncertain state before making decisions. Each player allocates his limited bandwidth between biased sources and the other players, and the resulting stochastic attention network facilitates the transmission of news from sources to him either directly or indirectly through the other players. The limit in the bandwidth leads the player to focus on his own-biased source, resulting in occasional cross-cutting exposures but most of the time a reinforcement of his predisposition. It also confines his attention to like-minded friends who, by attending to the same primary source as his, serve as secondary sources in case the news transmission from the primary source to him is disrupted. A mandate on impartial exposures to all biased sources disrupts echo chambers but entails ambiguous welfare consequences. Inside an echo chamber, even a small amount of heterogeneity between players can generate fat-tailed distributions of public opinion, and factors affecting the visibility of sources and players could have unintended consequences for public opinion and consumer welfare.

The concept of states of financial markets based on correlations has gained increasing attention during the last 10 years. We propose to retrace some important steps up to 2018, and then give a more detailed view of recent developments that attempt to make the use of this more practical. Finally, we try to give a glimpse to the future proposing the analysis of trajectories in correlation matrix space directly or in terms of symbolic dynamics as well as attempts to analyze the clusters that make up the states in a random matrix context.

While controversial, e-learning has become an essential tool for all kinds of education: especially within the kindergarten-to-twelfth sector. However, pockets of this sector lack access, mainly economically underserved students. This paper explores the options available to underserved and aptly resourced members of the kindergarten-to-twelfth educational sector: a 250-million-person market, with only 9 million students enrolled in online education. The paper also provides a brief overview of the options and challenges of making e-learning available to everyone in the kindergarten-to-twelfth educational sector. To establish whether e-learning is beneficial, it also discusses the results of a survey conducted on students and educators who have experienced e-learning, with the results showing that it is beneficial, with a general trend of teachers showing more comfort with online learning than students. The paper utilizes primary and secondary resources for this purpose, with information both from the internet, and from surveys conducted within people from the system: parents, students, and teachers.

Most explanations of economic growth are based on knowledge spillovers, where the development of some technologies facilitates the enhancement of others. Empirical studies show that these spillovers can have a heterogeneous and rather complex structure. But, so far, little attention has been paid to the consequences of different structures of such cross-technology interactions: Is economic development more easily fostered by homogenous or heterogeneous interactions, by uni- or bidirectional spillovers? Using a detailed description of an r&d sector with cross-technology interactions embedded in a simple growth model, we analyse how the structure of spillovers influences growth prospects and growth patterns. We show that some type of interactions (e.g., one-way interactions) cannot induce exponential growth, whereas other structures can. Furthermore, depending on the structure of interactions, all or only some technologies will contribute to growth in the long run. Finally, some spillover structures can lead to complex growth patterns, such as technology transitions, where, over time, different technology clusters are the main engine of growth.

We investigate and extend the results of Golts and Jones (2009) that an alpha-weight angle resulting from unconstrained quadratic portfolio optimisations has an upper bound dependent on the condition number of the covariance matrix. This implies that better conditioned covariance matrices produce weights from unconstrained mean-variance optimisations that are better aligned with each assets expected return. We provide further clarity on the mathematical insights that relate the inequality between the $\alpha$-weight angle and the condition number and extend the result to include portfolio optimisations with gearing constraints. We provide an extended family of robust optimisations that include the gearing constraints, and discuss their interpretation.

The hidden-action model provides an optimal sharing rule for situations in which a principal assigns a task to an agent who makes an effort to carry out the task assigned to him. However, the principal can only observe the task outcome but not the agent's actual action. The hidden-action model builds on somewhat idealized assumptions about the principal's and the agent's capabilities related to information access. We propose an agent-based model that relaxes some of these assumptions. Our analysis lays particular focus on the micro-level dynamics triggered by limited information access. For the principal's sphere, we identify the so-called Sisyphus effect that explains why the optimal sharing rule can generally not be achieved if the information is limited, and we identify factors that moderate this effect. In addition, we analyze the behavioral dynamics in the agent's sphere. We show that the agent might make even more of an effort than optimal under unlimited information, which we refer to as excess effort. Interestingly, the principal can control the probability of making an excess effort via the incentive mechanism. However, how much excess effort the agent finally makes is out of the principal's direct control.

This paper studies optimal consumption, investment, and healthcare spending under Epstein-Zin preferences. Given consumption and healthcare spending plans, Epstein-Zin utilities are defined over an agent's random lifetime, partially controllable by the agent as healthcare reduces mortality growth. To the best of our knowledge, this is the first time Epstein-Zin utilities are formulated on a controllable random horizon, via an infinite-horizon backward stochastic differential equation with superlinear growth. A new comparison result is established for the uniqueness of associated utility value processes. In a Black-Scholes market, the stochastic control problem is solved through the related Hamilton-Jacobi-Bellman (HJB) equation. The verification argument features a delicate containment of the growth of the controlled morality process, which is unique to our framework, relying on a combination of probabilistic arguments and analysis of the HJB equation. In contrast to prior work under time-separable utilities, Epstein-Zin preferences facilitate calibration. The model-generated mortality closely approximates actual mortality data in the US and UK; moreover, the efficacy of healthcare can be calibrated and compared between the two countries.

Several phenomena are available representing market activity: volumes, number of trades, durations between trades or quotes, volatility - however measured - all share the feature to be represented as positive valued time series. When modeled, persistence in their behavior and reaction to new information suggested to adopt an autoregressive-type framework. The Multiplicative Error Model (MEM) is borne of an extension of the popular GARCH approach for modeling and forecasting conditional volatility of asset returns. It is obtained by multiplicatively combining the conditional expectation of a process (deterministically dependent upon an information set at a previous time period) with a random disturbance representing unpredictable news: MEMs have proved to parsimoniously achieve their task of producing good performing forecasts. In this paper we discuss various aspects of model specification and inference both for the univariate and the multivariate case. The applications are illustrative examples of how the presence of a slow moving low-frequency component can improve the properties of the estimated models.

We analyze differences in mode of transportation to work by sexual orientation, using the American Community Survey 2008-2019. Individuals in same-sex couples are significantly less likely to drive to work than men and women in different-sex couples. This gap is particularly stark among men: on average, almost 12 percentage point (or 13%) lower likelihood of driving to work for men in same-sex couples. Individuals in same-sex couples are also more likely to use public transport, walk, or bike to work: on average, men and women are 7 and 3 percentage points more likely, respectively, to take public transportation to work than those in different-sex couples. These differences persist after controlling for demographic characteristics, partner's characteristics, location, fertility, and marital status. Additional evidence from the General Social Survey 2008-2018 suggests that these disparities by sexual orientation may be due to lesbian, gay, and bisexual individuals caring more for the environment than straight individuals.

We develop a generic calibration strategy for climate models used in economics. The key idea is to choose the free model parameters to match the output of large-scale Earth System Models, which are run on pre-defined future emissions scenarios and collected in the Coupled Model Intercomparison Project (CMIP5). We propose to use four test cases that are considered pivotal in the climate science literature. Two of these tests are highly idealized to allow for the separate examination of the carbon cycle and the temperature response. Another two tests incorporate gradual changes in CO2 emissions, exogenous forcing, and the temperature response. We re-calibrate the free parameters of the climate part of the seminal DICE-2016 model for three different CMIP5 model responses: the multi-model mean as well as two other CMIP5 models that exhibit extreme equilibrium climate sensitivities. As an additional novelty, our calibrations of DICE-2016 allow for an arbitrary time step in the model explicitly. We show that i) both the temperature equations and the carbon cycle in DICE-2016 are miscalibrated and that ii) by re-calibrating its coefficients, we can match all three CMIP5 targets. We apply the economic model from DICE-2016 in combination with the newly calibrated climate model to compute the social cost of carbon and the optimal warming. We find that in our updated model, the social cost of carbon is very similar to DICE-2016, however, the optimal long-run temperature lies almost one degree below that obtained by DICE-2016. This difference in climate behavior is reflected in the over-sensitivity of the social cost of carbon to the discount rate. Under the optimal mitigation scenario, the temperature predictions of DICE-2016 (in contrast to our proposed calibration) fall outside of the CMIP5 scenarios, suggesting that one might want to be skeptical about policy predictions derived from DICE-2016.