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This paper aims at filling some gaps in the mainstream debate on automation, the introduction of new technologies at the workplace and the future of work. This debate has concentrated, so far, on how many jobs will be lost as a consequence of technological innovation. This paper examines instead issues related to the quality of jobs in future labour markets. It addresses the detrimental effects on workers of awarding legal capacity and rights and obligation to robots. It examines the implications of practices such as People Analytics and the use of big data and artificial intelligence to manage the workforce. It stresses on an oft-neglected feature of the contract of employment, namely the fact that it vests the employer with authority and managerial prerogatives over workers. It points out that a vital function of labour law is to limit these authority and prerogatives to protect the human dignity of workers. In light of this, it argues that even if a Universal Basic Income were introduced, the existence of managerial prerogatives would still warrant the existence of labour regulation since this regulation is about much more than protecting workers' income. It then highlights the benefits of human-rights based approaches to labour regulation to protect workers' privacy against invasive electronic monitoring. It concludes by highlighting the crucial role of collective regulation and social partners in governing automation and the impact of technology at the workplace. It stresses that collective dismissal regulation and the involvement of workers' representatives in managing and preventing job losses is crucial and that collective actors should actively participate in the governance of technologyenhanced management systems, to ensure a vital "human-in-command" approach. [ABSTRACT FROM AUTHOR]

A recent literature within quantitative macroeconomics has advocated the use of continuous-time methods for dynamic programming problems. In this paper we explore the relative merits of continuous-time and discrete-time methods within the context of stationary and nonstationary income fluctuation problems. For stationary problems in two dimensions, the continuous-time approach is both more stable and typically faster than the discrete-time approach for any given level of accuracy. In contrast, for convex lifecycle problems (in which age or time enters explicitly), simply iterating backwards from the terminal date in discrete time is superior to any continuous-time algorithm. However, we also show that the continuous-time framework can easily incorporate nonconvexities and multiple controls—complications that often require either problem-specific ingenuity or nonlinear root-finding in the discrete-time context. In general, neither approach unequivocally dominates the other, making the choice of one over the other an art, rather than an exact science. [ABSTRACT FROM AUTHOR]

This paper evaluates the accuracy of linear and nonlinear estimation methods for dynamic stochastic general equilibrium models. We generate a large sample of artificial datasets using a global solution to a nonlinear New Keynesian model with an occasionally binding zero lower bound (ZLB) constraint on the nominal interest rate. For each dataset, we estimate the nonlinear model--solved globally, accounting for the ZLB--and the linear analogue of the nonlinear model--solved locally, ignoring the ZLB--with a Metropolis-Hastings algorithm where the likelihood function is evaluated with a Kalman filter, unscented Kalman filter, or particle filter. In datasets that resemble the U.S. experience, the nonlinear model estimated with a particle filter is more accurate and has a higher marginal data density than the linear model estimated with a Kalman filter, as long as the measurement error variances in the particle filter are not too big. [ABSTRACT FROM AUTHOR]

This paper considers a non-stationary dynamic factor model for large datasets to disentangle long-run from short-run co-movements. We first propose a new Quasi Maximum Likelihood estimator of the model based on the Kalman Smoother and the Expectation Maximisation algorithm. The asymptotic properties of the estimator are discussed. Then, we show how to separate trends and cycles in the factors by mean of eigenanalysis of the estimated non-stationary factors. Finally, we employ our methodology on a panel of US quarterly macroeconomic indicators to estimate aggregate real output, or Gross Domestic Output, and the output gap. [ABSTRACT FROM AUTHOR]

We study a generalized version of Coleman (1990)'s time iteration method (GTI) for solving dynamic optimization problems. Our benchmark framework is an irreversible investment model with labor-leisure choice. The GTI algorithm is simple to implement and provides advantages in terms of speed relative to Howard (1960)'s improvement algorithm. A second application on a heterogeneous-agents incomplete-markets model further explores the performance of GTI. [ABSTRACT FROM AUTHOR]

We study a generalized version of Coleman (1990)'s time iteration method (GTI) for solving dynamic optimization problems. Our benchmark framework is an irreversible investment model with labor-leisure choice. The GTI algorithm is simple to implement and provides advantages in terms of speed relative to Howard (1960)'s improvement algorithm. A second application on a heterogeneous-agents incomplete-markets model further explores the performance of GTI. [ABSTRACT FROM AUTHOR]

We develop a sequential Monte Carlo (SMC) algorithm for Bayesian inference in vector autoregressions with stochastic volatility (VAR-SV). The algorithm builds particle approximations to the sequence of the model's posteriors, adapting the particles from one approximation to the next as the window of available data expands. The parallelizability of the algorithm's computations allows the adaptations to occur rapidly. Our particular algorithm exploits the ability to marginalize many parameters from the posterior analytically and embeds a known Markov chain Monte Carlo (MCMC) algorithm for the model as an effective mutation kernel for fighting particle degeneracy. We show that, relative to using MCMC alone, our algorithm increases the precision of inference while reducing computing time by an order of magnitude when estimating a mediumscale VAR-SV model. [ABSTRACT FROM AUTHOR]

In this paper, I consider forecasting from a reduced-form VAR under the zero lower bound (ZLB) for the short-term nominal interest rate. The ZLB constraint expands the number of states exponentially, making the exact computation of forecast moments infeasible. I develop a method that a) computes the exact moments for the fi rst n + 1 periods when n previous periods are tracked and b) approximates moments for the periods beyond n + 1 period using techniques for truncated normal distributions and approximations a la Kim (1994). In its simplest form, the algorithm tracks only the previous forecast period. The approximations become more accurate as additional previous periods are tracked at the cost of longer computational time, although when the method is tracking two or three previous periods, it is competitive with Monte Carlo simulation in terms computational time. I show that the algorithm produces satisfactory results for VAR systems with moderate to high persistence even when only one previous period is tracked. For very persistent VAR systems, however, tracking more periods is needed in order to obtain reliable approximations. I also show that the method is suitable for affine term-structure modeling, where the underlying state vector includes the short-term interest rate as in Taylor rules with inertia. [ABSTRACT FROM AUTHOR]

Theory emphasizes the central role of the structure of networks in the behavior of financial systems and their response to policy. Real-world networks, however, are rarely directly observable: Banks' assets and liabilities are typically known, but not who is lending how much and to whom. We first show how to simulate realistic networks that are based on balance-sheet information by minimizing costs where there is a fixed cost to forming a link. Second, we also show how to do this for a model with fixed costs that are decreasing in the number of links. To approach the optimization problem, we develop a new algorithm based on the transportation planning literature. Computational experiments find that the resulting networks are not only consistent with the balance sheets, but also resemble real-world financial networks in their density (which is sparse but not minimally dense) and in their core-periphery and disassortative structure. [ABSTRACT FROM AUTHOR]

Taste shocks result in nondegenerate choice probabilities, smooth policy functions, continuous demand correspondences, and reduced computational errors. They also cause significant computational cost when the number of choices is large. However, I show that, in many economic models, a numerically equivalent approximation may be obtained extremely efficiently. If the objective function has increasing differences (a condition closely tied to policy function monotonicity) or is concave in a discrete sense, the proposed algorithms are O(n log n) for n states and n choices|a drastic improvement over the naive algorithm's O(n²) cost. If both hold, the cost can be further reduced to O(n). Additionally, with increasing differences in two state variables, I propose an algorithm that in some cases is O(n²) even without concavity (in contrast to the O(n³) naive algorithm). I illustrate the usefulness of the proposed approach in an incomplete markets economy and a long-term sovereign debt model, the latter requiring taste shocks for convergence. For grid sizes of 500 points, the algorithms are up to 200 times faster than the naive approach. [ABSTRACT FROM AUTHOR]

Vector autoregressions with Markov-switching parameters (MS-VARs) fit the data better than do their constant-parameter predecessors. However, Bayesian inference for MS-VARs with existing algorithms remains challenging. For our first contribution, we show that Sequential Monte Carlo (SMC) estimators accurately estimate Bayesian MS-VAR posteriors. Relative to multi-step, model-specific MCMC routines, SMC has the advantages of generality, parallelizability, and freedom from reliance on particular analytical relationships between prior and likelihood. For our second contribution, we use SMC's flexibility to demonstrate that the choice of prior drives the key empirical finding of Sims, Waggoner, and Zha (2008) as much as does the data. [ABSTRACT FROM AUTHOR]

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The goal of classical normalization is to maintain data consistency under updates, with a minimum level of effort. Given functional dependencies (FDs) alone, this goal is only achievable in the special case an FD-preserving Boyce-Codd Normal Form (BCNF) decomposition exists. As we show, in all other cases the level of effort can be neither controlled nor quantified. In response, we establish the -Bounded Cardinality Normal Form, parameterized by a positive integer l. For every ', the normal form condition requires from every instance that every value combination over the left-hand side of every non-trivial FD does not occur in more than l tuples. BCNF is captured when l = 1. We demonstrate that schemata in this normal form characterize the instances that are i) free from level l data redundancy and update inefficiency, and ii) permit level l join efficiency. We establish algorithms that compute schemata in l-Bounded Cardinality Normal Form for the smallest level l attainable across all FD-preserving decompositions. Additional algorithms i) attain even smaller levels of effort based on the loss of some FDs, and ii) decompose schemata based on prioritized FDs that cause high levels of effort. Our framework informs de-normalization already during logical design. In particular, level l quantifies both the incremental maintenance and join support of materialized views. Experiments with synthetic and real-world data illustrate which properties the schemata have that result from our algorithms, and how these properties predict the performance of update and query operations on instances over the schemata, without and with materialized views. [ABSTRACT FROM AUTHOR]

Embedded functional dependencies (eFDs) were recently introduced to tailor relational schema design to data completeness requirements of applications. They also facilitate data cleaning and data integration. A problem that is essential to unlocking these applications is the discovery of all eFDs that hold on a given data set. We show that the discovery problem of eFDs is NP-complete, W[2]-complete in the output, and has a minimum solution space that is larger than the maximum solution space for functional dependencies. Despite these computational challenges, we use novel data structures and search strategies to develop row-efficient, column-efficient, and hybrid algorithms that can efficiently solve the discovery problem for eFDs on large real-world benchmark data sets. Our experiments also demonstrate that the algorithms scale well in terms of their design targets, and that ranking the eFDs by the number of redundant data values they cause can provide useful guidance in identifying meaningful eFDs for applications. Finally, we demonstrate the benefits of introducing completeness requirements and ranking by the number of redundant data values for approximate and genuine functional dependencies. [ABSTRACT FROM AUTHOR]

Seemingly overnight, Artificial Intelligence (AI) has moved from a plot point in science fiction movies to a core technology for companies such as Google, Facebook, Baidu, Microsoft, and Amazon. But the idea of AI -- of machines that can sense, classify, learn, reason, predict, and interact -- has been around for decades. Today, the combination of massive and available datasets, inexpensive parallel computing, and advances in algorithms has made it possible for machines to function in ways that were previously unthinkable. While the more obvious examples such as robotics, driverless cars, and intelligent agents such as Siri and Alexa tend to dominate the news, artificial intelligence has much wider implications. Gartner predicts that "by 2020, algorithms will positively alter the behavior of billions of global workers." Markets & Markets expects the AI market to reach $5.05B by 2020. This report lays out the current state of AI for business, describes primary and emerging use cases, and states the risks, opportunities, and organizational considerations that businesses are facing. It concludes with recommendations for companies thinking about applying AI to their own organizations and a look at some of the business, legal, and technical trends that are likely to shape the future. [ABSTRACT FROM AUTHOR]