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To analyse the interdependence between monetary and fiscal policy during a financial crisis, we develop an open-economy DSGE model with monetary and fiscal policy as well as financial markets in a continuous-time framework based on stochastic differential equations. Monetary policy is modelled using both a standard and a modified Taylor rule and fiscal policy is modelled as either expansionary or austere. In addition, we differentiate between open economies and monetary union members. We find evidence that the modified Taylor rule notably reduces the likelihood that the financial market crisis affects the real economy. But if we assume that households are averse with respect to outstanding government debt, we find that a combination of expansionary monetary policy and austere fiscal policy provides better stabilisation of both domestic and foreign economies in terms of both output and inflation. In the case of a monetary union, we find that stabilisation of output in the country where the financial shock originates is no longer as easy and, in terms of prices, there is now deflation in the country where the crisis originated and a positive inflation rate in the other country.

The purpose of this paper is to provide an intuitive explanation of the emergence and evolution of the week based on a historical precedent draw from ancient Egypt. In this paper, we view the week as a coordinating social institution that was created to resolve a fundamental problem of society - coordinating market exchange. Artificial adaptive agents are used to simulate the interactions among farmers going to market. The results show that the length of the week that emerges depends on the chosen cost and benefit specifications and random interactions.

Theoretical formulations of dynamic heterogeneous-agent economies typically include a distribution as an aggregate state variable. This paper introduces a method for computing equilibrium of these models by including a distribution directly as a state variable if it is finite-dimensional or a fine approximation of it if it is infinite-dimensional. The method accurately computes equilibrium in an extreme calibration of Huffman's (1987) overlapping-generations economy where quasi-aggregation, the accurate forecasting of prices using a small state space, fails to obtain. The method also accurately solves for equilibrium in a version of Krusell and Smith's (1998) economy wherein quasi-aggregation obtains but households face occasionally binding constraints. The method is demonstrated to be not only accurate but also feasible with equilibria for both economies being computed in under ten minutes in Matlab. Feasibility is achieved by using Smolyak's (1963) sparse-grid interpolation algorithm to limit the necessary number of gridpoints by many orders of magnitude relative to linear interpolation. Accuracy is achieved by using Smolyak's algorithm, which relies on smoothness, only for representing the distribution and not for other state variables such as individual asset holdings.

We study a class of infinite-horizon nonlinear dynamic economic models in which preferences, technology and laws of motion for exogenous variables can change over time either deterministically or stochastically, according to a Markov process with time-varying transition probabilities, or both. The studied models are nonstationary in the sense that the decision and value functions are time-dependent, and they cannot be generally solved by conventional solution methods. We introduce a quantitative framework, called extended function path (EFP), for calibrating, solving, simulating and estimating such models. We apply EFP to analyze a collection of challenging applications that do not admit stationary Markov equilibria, including growth models with anticipated parameters shifts and drifts, unbalanced growth under capital augmenting technological progress, anticipated regime switches, deterministically time-varying volatility and seasonal fluctuations. Also, we show an example of estimation and calibration of parameters in an unbalanced growth model using data on the U.S. economy. Examples of MATLAB code are provided.

We describe some fast algorithms for reconciling large collections of time series forecasts with aggregation constraints. The constraints arise due to the need for forecasts of collections of time series with hierarchical or grouped structures to add up in the same manner as the observed time series. We show that the least squares approach to reconciling hierarchical forecasts can be extended to more general non-hierarchical groups of time series, and that the computations can be handled efficiently by exploiting the structure of the associated design matrix. Our algorithms will reconcile hierarchical forecasts with hierarchies of unlimited size, making forecast reconciliation feasible in business applications involving very large numbers of time series.

This paper develops a new methodology for identifying the structure of VARMA time series models. The analysis proceeds by examining the echelon canonical form and presents a fully automatic data driven approach to model specification using a new technique to determine the Kronecker invariants. A novel feature of the inferential procedures developed here is that they work in terms of a canonical scalar ARMAX representation in which the exogenous regressors are given by predetermined contemporaneous and lagged values of other variables in the VARMA system. This feature facilitates the construction of algorithms which, from the perspective of macroeconomic modeling, are efficacious in that they do not use AR approximations at any stage. Algorithms that are applicable to both asymptotically stationary and unit-root, partially nonstationary (cointegrated) time series models are presented. A sequence of lemmas and theorems show that the algorithms are based on calculations that yield strongly consistent estimates.

This paper revisits the benefits of explicitly announcing an inflation target for the con- duct of monetary policy in the framework of an agent-based model (ABM). This framework offers a flexible tool for modeling heterogeneity among individual agents and their bounded rationality, and to emphasize, on this basis, the role of learning in macroeconomic dynamics. We consider that those three features (heterogeneity, bounded rationality, and learning) are particularly relevant if one desires to question the rationale for the monetary authorities to be transparent about the inflation target, and to achieve credibility. Indeed, the inflation targeting’s potential role in anchoring inflation expectations and stabilizing the inflation and the economy can be analyzed more realistically if we do not assume a representative agent framework based on substantial rationality in behaviors and expectations. Our results show that a dynamic loop between credibility and success can arise, and stabilize inflation, but only in the case of a learning environment that corresponds to a moderate degree in heterogeneity regarding the behavior and decisions of individual agents. In a more general way, we analyze, using this ABM, different assumptions about the nature of the economic volatility, and the degree of disclosure of the target.

The recently observed disconnect between inflation and economic activity can be explained by the interplay between the zero lower bound (ZLB) and the costs of external financing. In normal times, credit spreads and the nominal interest rate balance out; factor costs dominate firms' marginal costs. When nominal rates are constrained, larger spreads can more than offset the effect of lower factor costs and induce only moderate inflation responses. The Phillips curve is hence flat at the ZLB, but features a positive slope in normal times and thus a hockey stick shape. Via this mechanism, forward guidance may induce deflationary effects.

We study the impact of alternative innovation policies on the short- and long-run performance of the economy, as well as on public finances, extending the Schumpeter meeting Keynes agent-based model (Dosi et al., 2010). In particular, we consider market-based innovation policies such as R&D subsidies to firms, tax discount on investment, and direct policies akin to the “Entrepreneurial State” (Mazzucato, 2013), involving the creation of public research oriented firms diffusing technologies along specific trajectories, and funding a Public Research Lab conducting basic research to achieve radical innovations that enlarge the technological opportunities of the economy. Simu- lation results show that all policies improve productivity and GDP growth, but the best outcomes are achieved by active discretionary State policies, which are also able to crowd-in private investment and have positive hysteresis effects on growth dynamics. For the same size of public resources allocated to market-based interventions, “Mission” innovation policies deliver significantly better aggregate performance if the government is patient enough and willing to bear the intrinsic risks related to innovative activities.

Can boundedly rational agents survive competition with fully rational agents? The authors develop a highly nonlinear heterogeneous agents model with rational forward looking versus boundedly rational backward looking agents and evolving market shares depending on their relative performance. Their novel numerical solution method detects equilibrium paths characterized by complex bubble and crash dynamics. Boundedly rational trend-extrapolators amplify small deviations from fundamentals, while rational agents anticipate market crashes after large bubbles and drive prices back close to fundamental value. Overall rational and non-rational beliefs co-evolve over time, with time-varying impact, and their interaction produces complex endogenous bubble and crashes, without any exogenous shocks.

This paper demonstrates a failure of standard, generalized Schur (or QZ) decomposition based solutions methods for linear dynamic stochastic general equilibrium (DSGE) models when there is insufficient eigenvalue separation about the unit circle. The significance of this is demonstrated in a simple production-based asset pricing model with external habit formation. While the exact solution afforded by the simplicity of the model matches post-war US consumption growth and the equity premium, QZ-based numerical solutions miss the later by many annualized percentage points.

We analyze the individual and macroeconomic impacts of heterogeneous expectations and action rules within an agent-based model populated by heterogeneous, interacting firms. Agents have to cope with a complex evolving economy characterized by deep uncertainty resulting from technical change, imperfect information and coordination hurdles. In these circumstances, we find that neither individual nor macroeconomic dynamics improve when agents replace myopic expectations with less naïve learning rules. In fact, more sophisticated, e.g. recursive least squares (RLS) expectations produce less accurate individual forecasts and also considerably worsen the performance of the economy. Finally, we experiment with agents that adjust simply to technological shocks, and we show that individual and aggregate performances dramatically degrade. Our results suggest that fast and frugal robust heuristics are not a second-best option: rather they are “rational” in macroeconomic environments with heterogeneous, interacting agents and changing “fundamentals”.

Did the Federal Reserves’ Quantitative Easing (QE) in the aftermath of the financial crisis have macroeconomic effects? To answer this question, the authors estimate a large-scale DSGE model over the sample from 1998 to 2020, including data of the Fed’s balance sheet. The authors allow for QE to affect the economy via multiple channels that arise from several financial frictions. Their nonlinear Bayesian likelihood approach fully accounts for the zero lower bound on nominal interest rates. They find that between 2009 to 2015, QE increased output by about 1.2 percent. This reflects a net increase in investment of nearly 9 percent, that was accompanied by a 0.7 percent drop in aggregate consumption. Both, government bond and capital asset purchases were effective in improving financing conditions. Especially capital asset purchases significantly facilitated new investment and increased the production capacity. Against the backdrop of a fall in consumption, supply side effects dominated which led to a mild disinflationary effect of about 0.25 percent annually.

We apply numerical dynamic programming techniques to solve discrete-time multi-asset dynamic portfolio optimization problems with proportional transaction costs and shorting/borrowing constraints. Examples include problems with multiple assets, and many trading periods in a finite horizon problem. We also solve dynamic stochastic problems, with a portfolio including one risk-free asset, an option, and its underlying risky asset, under the existence of transaction costs and constraints. These examples show that it is now tractable to solve such problems.

Using a nonlinear Bayesian likelihood approach that fully accounts for the lower bound on nominal interest rates, we analyze US post-crisis macroeconomic dynamics and provide reference parameter estimates. We find that despite the attention received in the literature, neither the inclusion of financial frictions nor that of household heterogeneity improves the empirical fit of the standard model or its ability to provide a joint explanation for the post-2007 dynamics. Associated financial shocks mis-predict an increase in consumption. We illustrate that the common practice of omitting the ZLB period in the estimation severely distorts the analysis of the latest economic dynamics.

We develop a macroprudential contagion stress test framework to examine how a network of Norwegian banks can amplify a shock to bank capital at the macro level. The framework looks at how fire sales of common asset holdings can lead to valuation losses for banks (indirect contagion), and how recapitalisation of banks can lead to direct contagion. We perform Monte Carlo simulations to quantify contagion-driven systemic risk and to evaluate the importance of the mechanisms in our model. Using data for 22 banks from 2019 Q2 we find that losses due to contagion can reach 2 percentage points (pp) of the banking sector’s Common Equity Tier 1 (CET1) ratio, but most likely losses are around one-fourth of this. The losses result almost exclusively from indirect contagion. Further, we find that losses are high in the cases where banks quickly run into funding problems. We also find that market liquidity and which assets banks’ fire sale first (pecking order) are important determinants of the results. Last but not least, losses due to contagion are highly correlated with losses on covered bonds.

Integrated energy-economic modelling is needed to support the development of energy and climate policies. This study asserts that it is important to consider a system dynamics modelling approach that includes dynamics, endogenous treatment of uncertainty and risks, and both aggregate economic and disaggregate technical or engineering levels of analysis. The study examined the economic growth and the factors of production, elasticities, macro- and technical substitutability; energy cost shares, heat engine efficiencies and energy services efficiencies. Emphasis was laid on the support of the future development of integrated energy-economic models covering (a) the key factors or components; (b) the relationships among these components; (c) a quantification of parameters; and (d) the implications for the development of an integrated energy-economic system dynamics model. The study suggested the following: a non-linear relationship in production and consumption; large variations among price and income elasticity values across time frames, across countries and regions, and across energy goods; a far from perfect substitution among factors of production and among energy goods on a macro-level; technical/engineering limits to substitution on a micro-level; and engineering and behavioural limits on what can be achieved with increased efficiencies. The study argues that integrated energy-economic modelling intensifies the accounting for the factors, relationships, quantifications, and implications, and that this practice allows for such models to describe a complex, emergent energy-economic reality that informs better energy policy.

In this paper methods of mixing decision rules are investigated and applied to the so-called multiple job type assignment problem with specialized servers. This problem is modeled as continuous time Markov decision process. For this assignment problem performance optimization is in general considered to be difficult. Moreover, for optimal dynamic Markov decision policies the corresponding decision rules have in general a complicated structure not facilitating a smooth implementation. On the other hand optimization over the subclass of so-called static policies is known to be tractable. In the current paper a suitable static decision rule is mixed with dynamic decision rules which are selected such that these rules are relatively easy to describe and implement. Some mixing methods are discussed and optimization is performed over corresponding classes of so-called mixing policies. These mixing policies maintain the property that they are easy to describe and implement compared to overall optimal dynamic Markov decision policies. Besides for all investigated instances the optimized mixing policies perform substantially better than optimal static policies.

The diffusion of social media has radically changed the number of peers with whom consumers interact with when making a decision. While consumption decisions depend on many factors, such as prices, qualities, distribution channels, and marketing, in this article, we study the effects of a single aspect: the role of the number of social connections in shaping consumers’ decisions. We present an agent-based simulation model where virtual consumers respond solely to information provided by peers from their social network. We obtain that increasing the number of connections consumers rely upon to gather information changes radically the distributional properties of markets where consumers cannot obtain direct information about the available options, such as experience goods. In particular, we show that increasing the number of connections among consumers increases the concentration of the top- and low-end market share options, sharply decreasing the number of “midsized” options. This effect is in line with evidence from markets for movies and music, which rely heavily on information gathered through peers.

We analyze the effects of local market size and accessibility on the spatial distribution of economic activity and wages in general equilibrium trade models with many asymmetric countries and costly trade for all goods. In models with a homogeneous sector, local market size is generally more strongly correlated with a country's industry share, whereas accessibility better explains a country's wage. We analytically show that result in a simplified case and then confirm it using simulations with random trading networks. In models with only differentiated sectors, both local market size and accessibility are highly correlated with wages. The impact of local market size on industry location is more robust than the impact of local market size on wages in economic geography models.

The Input-Output model has made extensive use of graph and network theory, methods and conclusions, in order to carry out structural analysis; such extensions have yielded powerful insights on the relationships existing between industries in an economic system. It is customary in the field to take a deterministic perspective when analysing economic structures, using various measures derived from the Input-Output tables; yet the model can include stochastic experiments. This paper takes that course of action, aiming at modelling the Greek economic structure from a qualitative viewpoint for 2005 and 2010; moreover, sectors are divided by groups of differentiated technology intensity. The methodology is based on a model based on families of distributions that allow predicting and analysing network structures. Results are reached by a probabilistic approach, producing interesting insights about the economic structures under study, while revealing different behaviour of the different groups of industries, classified by technological intensity.

Robust optimization is a methodology that can be applied to problems that are affected by uncertainty in their parameters. The classical robust counterpart of a problem requires the solution to be feasible for all uncertain parameter values in a so-called uncertainty set and offers no guarantees for parameter values outside this uncertainty set. The globalized robust counterpart (GRC) extends this idea by allowing controlled constraint violations in a larger uncertainty set. The constraint violations are controlled by the distance of the parameter from the original uncertainty set. We derive tractable GRCs that extend the initial GRCs in the literature: our GRC is applicable to nonlinear constraints instead of only linear or conic constraints, and the GRC is more flexible with respect to both the uncertainty set and distance measure function, which are used to control the constraint violations. In addition, we present a GRC approach that can be used to provide an extended trade-off overview between the objective value and several robustness measures.

This paper is an exploratory analysis of the role that banks play in supporting what Jevons called the "mechanism of exchange." It considers a model economy in which exchange activities are facilitated and coordinated by a self-organizing network of entrepreneurial trading firms. Collectively, these firms play the part of the Walrasian auctioneer, matching buyers with sellers and helping the economy to reach prices at which peoples' trading plans are mutually compatible. Banks affect macroeconomic performance in this economy because their lending activities facilitate the entry and influence the exit decisions of trading firms. Both entry and exit have ambiguous effects on performance, and we resort to computational analysis to understand how they are resolved. Our analysis draws an important distinction between normal and worst-case scenarios, with the economy experiencing systemic breakdowns in the latter. We show that banks can provide a "financial stabilizer" that more than counteracts the familiar financial accelerator, and that the stabilizing role of the banking system is particularly apparent in worst-case scenarios. In line with this result, we also find that under less restrictive lending standards banks are able to more effectively improve macroeconomic performance in the worst-case scenarios.

The computation of various risk metrics is essential to the quantitative risk management of variable annuity guaranteed benefits. The current market practice of Monte Carlo simulation often requires intensive computations, which can be very costly for insurance companies to implement and take so much time that they cannot obtain information and take actions in a timely manner. In an attempt to find low-cost and efficient alternatives, we explore the techniques of comonotonic bounds to produce closed-form approximation of risk measures for variable annuity guaranteed benefits. The techniques are further developed in this paper to address in a systematic way risk measures for death benefits with the consideration of dynamic policyholder behavior, which involves very complex path-dependent structures. In several numerical examples, the method of comonotonic approximation is shown to run several thousand times faster than simulations with only minor compromise of accuracy.

A (conservative) test is constructed to investigate the optimal lag structure for forecasting realized volatility dynamics. The testing procedure relies on the recent theoretical results that show the ability of the adaptive least absolute shrinkage and selection operator (adaptive lasso) to combine efficient parameter estimation, variable selection, and valid inference for time series processes. In an application to several constituents of the S&P 500 index it is shown that (i) the optimal significant lag structure is time-varying and subject to drastic regime shifts that seem to happen across assets simultaneously; (ii) in many cases the relevant information for prediction is included in the first 22 lags, corroborating previous results concerning the accuracy and the difficulty of outperforming out-of-sample the heterogeneous autoregressive (HAR) model; and (iii) some common features of the optimal lag structure can be identified across assets belonging to the same market segment or showing a similar beta with respect to the market index.

Trabajo presentado en el 8th Shanghai Macroeconomics Workshop, celebrado en Shanghai (China), del 17 al 19 de junio de 2017.--Trabajo presentado en el Government Debt: Constraints and Choices, organizado por la Universidad de Chicago durante los días 21 y 22 de abril de 2017.--This version: October 2018 (November 2014), Standard optimal Debt Management (DM) models prescribe a dominant role for long bonds and advocate against issuing short bonds. They require very large positions in order to complete markets and assume each period that governments repurchase all outstanding bonds and reissue ( r/r ) new ones. These features of DM are inconsistent with US data. We introduce incomplete markets via small transaction costs which serves to make optimal DM more closely resemble the data : r/r are negligible, short bond issuance substantial and persistent and short and long bonds positively co-vary. Intuitively long bonds help smooth taxes over states and short bonds over time. Solving incomplete market models with multiple assets is challenging so a further contribution of this paper is introducing a novel computational method to find global solutions., Faraglia gratefully acknowledges support from the Cambridge Endowment of Research in Finance (CERF), Marcet from Plan Nacional (Spanish Ministry of Science), Monfispol, AGAUR (Generalitat de Catalunya), the Axa Foundation, the Excellence Program of Banco de Espa˜na, European Research Council under the EU 7th Framework Programme (FP/2007-2013) Advanced ERC Grant Agreement n. 324048 - APMPAL and the Severo Ochoa Programme for Centres of Excellence in R&D (SEV-2015-0563) and Scott from the ESRCs World Economy and Finance program.

Our paper investigates the impact of rising inequality in a two-country macroeconomic model with an agent-based household sector characterized by peer effects in consumption. In particular, the model highlights the role of inequality in determining diverging balance of payments dynamics within a currency union. Inequality may drive the two countries into different growth patterns: where peer effects in consumption interact with higher credit availability, rising income inequality leads to the emergence of a debt-led growth. Where social norms determine weaker emulation and credit availability is lower, an export-led regime arises. Eventually, a crisis emerges endogenously due to the sudden-stop of capital flows from the net lending country, triggered by the excessive risk associated with the dramatic amount of private debt accumulated by households in the borrowing country. Monte Carlo simulations for a wide range of calibrations confirm the robustness of our results.

We study a stochastic dynamic game of process innovation in which firms can initiate and terminate R&D efforts and production at different times. We discern the impact of knowledge spillovers on the investments in existing markets, as well as on the likely structure of newly forming markets, for all possible asymmetries in production costs between firms. While an increase in spillovers may improve the likelihood of a competitive market, it may at the same time reduce the level to which a technology is developed. We show that the effects of spillovers on investments and surpluses crucially depend on the stage of technology development considered. In particular, we show that high spillovers are not necessarily pro-competitive as they can make it harder for the laggard to catch up with the technology leader.

International audience; In this work we study the granular origins of business cycles and their possible underlying drivers. As shown by Gabaix (Econometrica 79:733–772, 2011), the skewed nature of firm size distributions implies that idiosyncratic (and independent) firm-level shocks may account for a significant portion of aggregate volatility. Yet, we question the original view grounded on “supply granularity”, as proxied by productivity growth shocks – in line with the Real Business Cycle framework–, and we provide empirical evidence of a “demand granularity”, based on investment growth shocks instead. The role of demand in explaining aggregate fluctuations is further corroborated by means of a macroeconomic Agent-Based Model of the “Schumpeter meeting Keynes” family Dosi et al. (J Econ Dyn Control 52:166–189, 2015). Indeed, the investigation of the possible microfoundation of RBC has led us to the identification of a sort of microfounded Keynesian multiplier.

We obtain a recursive formulation for a general class of optimization problems with forward‐looking constraints which often arise in economic dynamic models, for example, in contracting problems with incentive constraints or in models of optimal policy. In this case, the solution does not satisfy the Bellman equation. Our approach consists of studying a recursive Lagrangian. Under standard general conditions, there is a recursive saddle‐point functional equation (analogous to a Bellman equation) that characterizes a recursive solution to the planner's problem. The recursive formulation is obtained after adding a co‐state variable μ t summarizing previous commitments reflected in past Lagrange multipliers. The continuation problem is obtained with μ t playing the role of weights in the objective function. Our approach is applicable to characterizing and computing solutions to a large class of dynamic contracting problems.

International audience; Kiyotaki and Wright (1989) model has exerted considerable in ‡uence in the monetary search literature. We argue that the model delivers also important insights on a broader range of macroeconomic and development issues. The analysis studies how market frictions and the liquidity of assets a¤ect the distribution of income. Experiments illustrate how the economy adjusts to shocks to assets' returns and to the matching technology. They also deal with long-run transition. An experiment interprets the reversal of fortune hypothesis as a situation in which an economy with a low-return asset takes over a similar economy with a high-return asset.

This paper applies a social learning model to the optimal consumption rule of Allen and Carroll [Macroeconomic Dynamics5(2001), 255–271] and delivers convincing convergence dynamics toward the optimal rule. These findings constitute a significant improvement over previous results in the literature, in terms of both speed of convergence and parsimony of the learning model. The learning model exhibits several appealing features: it is frugal, is easy to apply to a various range of learning objectives, and requires few procedures and little information. Particular care is given to behavioral interpretation of the modeling assumptions in light of evidence from the fields of psychology and social science. Our results highlight the need to depart from the genetic metaphor, and account for intentional decision-making, based on agents' relative performances. By contrast, we show that convergence is strongly hindered by exact imitation processes, or random exploration mechanisms, which are usually assumed when modeling social learning behavior. Our results suggest a method for modeling bounded rationality, which could be interestingly tested in a wide range of economic models with adaptive dynamics.

The recent increase in the breath of computational methodologies has been matched with a corresponding increase in the difficulty of comparing the relative explanatory power of models from different methodological lineages. In order to help address this problem a Markovian information criterion (MIC) is developed that is analogous to the Akaike information criterion (AIC) in its theoretical derivation and yet can be applied to any model able to generate simulated or predicted data, regardless of its methodology. Both the AIC and proposed MIC rely on the Kullback---Leibler (KL) distance between model predictions and real data as a measure of prediction accuracy. Instead of using the maximum likelihood approach like the AIC, the proposed MIC relies instead on the literal interpretation of the KL distance as the inefficiency of compressing real data using modelled probabilities, and therefore uses the output of a universal compression algorithm to obtain an estimate of the KL distance. Several Monte Carlo tests are carried out in order to (a) confirm the performance of the algorithm and (b) evaluate the ability of the MIC to identify the true data-generating process from a set of alternative models.

This paper proposes a new way to measure and deal with risk within the portfolio selection problem using a skewness/semivariance biobjective optimization framework. The solutions of this biobjective optimization problem allow the investor to analyse the efficient trade-off between skewness and semivariance. Due to the endogeneity of the cosemivariance matrix, the biobjective problem is solved using a derivative-free algorithm based on direct multisearch. For four datasets, collected from the Fama/French benchmark collection, the direct multisearch was able to determine the in-sample Pareto frontier. The out-of-sample performance of the skewness/semivariance model was assessed by choosing three portfolios (the portfolio that maximizes a skewness per semivariance ratio, the portfolio that maximizes the Sharpe ratio and the portfolio that maximizes the Sortino ratio) at each in-sample Pareto frontier and measuring their performance in terms of skewness per semivariance ratio, Sharpe ratio, Sortino ratio and turnover. The results show that the efficient skewness/semivariance portfolios are consistently competitive, and often superior, comparatively to the benchmark portfolios considered. Both in-sample and the out-of-sample performance analysis were conducted using three different benchmark returns for the semivariance computations.

A recursive method for solving an integrated assessment model of climate and the economy is developed in this paper. The method approximates a value function with a logarithmic basis function and searches for solutions on a set satisfying optimality conditions. These features make the method suitable for a nonlinear model with many state variables and various constraints. The method produces exact solutions to a simple economic growth model and is useful for solving more demanding models such as the well-known DICE model (dynamic integrated model of climate and the economy).

The EU member states have declared to strongly increase the share of Renewable Energy Sources in the next decades. Given a large deployment of wind and solar capacities as well as limited cost-efficient storage technologies, this has two major impacts on electricity systems. First, the electricity system has to be flexible enough to cope with the volatile RES-E generation i.e. ramp up supply or ramp down demand on short notice. Second, sufficient back-up capacities are needed for times with low feed-in from wind and solar capacities. The provision of both back-up capacity has been intensively discussed in the previous literature of recent years (for instance Cramton and Stoft, 2008 and Joskow, 2008). In addition, Lamadrid et al (2011) argue that with increasing volatility, incentives to invest in flexible resources should be implemented in market design. However, they did not have a look at the dynamic view in an integrated analysis necessary to to answer the questions of how an electricity system can adapt to an increasing share of renewables. This paper therefore analyses the flexibility requirements of the future European electricity system and the policy implications for market design with a system-economic dynamic approach. For this purpose, we simulate the development of the European electricity markets up to 2050 by using a linear investment and dispatch optimization model. Flexibility requirements are implemented in the model via constraints for ramping and provision of balancing power. We find that although an increase of fluctuating renewables has a tremendous impact on volatility and therefore flexibility requirements, the main trigger for investments into flexible conventional capacity are the achievable full load hours rather than ramping capabilities. Therefore any market design with investment incentives of achievable full load hours does not need additional incentives for flexibility.

This paper puts forward kernel ridge regression as an approach for forecasting with many predictors that are related to the target variable nonlinearly. In kernel ridge regression, the observed predictor variables are mapped nonlinearly into a high-dimensional space, where estimation of the predictive regression model is based on a shrinkage estimator in order to avoid overfitting. We extend the kernel ridge regression methodology to enable its use for economic time series forecasting, by including lags of the dependent variable or other individual variables as predictors, as is typically desired in macroeconomic and financial applications. Both Monte Carlo simulations and an empirical application to various key measures of real economic activity confirm that kernel ridge regression can produce more accurate forecasts than traditional linear and nonlinear methods for dealing with many predictors based on principal components.

We introduce a macroeconomic model with heterogeneous households and an aggregate banking sector in order to analyze the impact of rising income inequality under different credit scenarios. Growing inequality produces debt‐led consumption boom dynamics when the banking sector is characterized by a lower capital requirement and a higher willingness to lend. Instead, when inequality rises but the banking sector is highly regulated, aggregate demand and output fall. Our results also yield new insights on the appropriate fiscal policy reaction to stabilize the economy: acting on the progressivity of the tax system seems more effective than a proactive countercyclical fiscal policy. (JEL C63, D31, E62, G01)

We build an agent-based model of a financial market that is able to jointly reproduce many of the stylized facts at different time-scales. These include properties related to returns (leptokurtosis, absence of linear autocorrelation, volatility clustering), trading volumes (volume clustering, correlation between volume and volatility), and timing of trades (number of price changes, autocorrelation of durations between subsequent trades, heavy tail in their distribution, order-side clustering). With respect to previous contributions we introduce a strict event scheduling borrowed from the EURONEXT exchange, and an endogenous rule for traders participation. We show that such a rule is crucial to match stylized facts.

In this work, we employ an agent-based integrated assessment model to study the likelihood of transition to green, sustainable growth in presence of climate damages. The model comprises heterogeneous fossil-fuel and renewable plants, capital- and consumption-good firms and a climate box linking greenhouse gasses emission to temperature dynamics and microeconomic climate shocks affecting labour productivity and energy demand of firms. Simulation results show that the economy possesses two statistical equilibria: a carbon-intensive lock-in and a sustainable growth path characterized by better macroeconomic performances. Once climate damages are accounted for, the likelihood of a green transition depends on the damage function employed. In particular, aggregate and quadratic damage functions overlook the impact of climate change on the transition to sustainability; to the contrary, more realistic micro-level damages are found to deeply influence the chances of a transition. Finally, we run a series of policy experiments on carbon (fossil fuel) taxes and green subsidies. We find that the effectiveness of such marketbased instruments depends on the different channels climate change affects the economy through, and complementary policies might be required to avoid carbon-intensive lock-ins.

In this work we study the granular origins of business cycles and their possible underlying drivers. As shown by Gabaix (2011), the skewed nature of firm size distributions implies that idiosyncratic (and independent) firm-level shocks may account for a significant portion of aggregate volatility. Yet, we question the original view grounded on “supply granularity”, as proxied by productivity growth shocks – in line with the Real Business Cycle framework–, and we provide empirical evidence of a “demand granularity”, based on investment growth shocks instead. The role of demand in explaining aggregate fluctuations is further corroborated by means of a macroeconomic Agent-Based Model of the “Schumpeter meeting Keynes” family (Dosi et al., 2015). Indeed, the investigation of the possible microfoundation of RBC has led us to the identification of a sort of microfounded Keynesian multiplier.

In this work we discuss the research findings from the labour-augmented Schumpeter meeting Keynes (K+S) agent-based model. It comprises comparative dynamics experiments on an artificial economy populated by heterogeneous, interacting agents, as workers, firms, banks and the government. The exercises are characterised by different degrees of labour flexibility, or by institutional shocks entailing labour market structural reforms, wherein the phenomenon of hysteresis is endogenous and pervasive. The K+S model constitutes a laboratory to evaluate the effects of new institutional arrangements as active/passive labour market policies, and fiscal austerity. In this perspective, the model allows mimicking many of the customary policy responses which the European Union and many Latin American countries have embraced in reaction to the recent economic crises. The obtained results seem to indicate, however, that most of the proposed policies are likely inadequate to tackle the short-term crises consequences, and even risk demoting the long-run economic prospects. More objectively, the conclusions offer a possible explanation to the negative path traversed by economies like Brazil, where many of the mentioned policies were applied in a short period, and hint about some risks ahead.

This paper assesses the quantitative impact of ambiguity on the historically observed equity premium. We consider a Lucas-tree pure–exchange economy with a single agent where we introduce two key non- standard assumptions. First, the agent’s beliefs about the dividend/consumption process is ambiguous, i.e., she is uncertain about the exact probability distribution governing the realization of future dividends and consumption. Second, the agent’s preferences are sensitive to this ambiguity, a property formalized using the smooth ambiguity model. The consumption and dividend process is assumed to evolve according to a hidden state model, popularized by Bansal and Yaron (2004), where a persistent latent state variable describes temporary shocks to the mean of consumption growth prospects. We further extend the model to allow for uncertainty about the magnitude of the persistence of the latent state. The agent’s beliefs are am- biguous due to the uncertainty about the conditional mean of the probability distribution on consumption and dividends in the next period. We show that in this model ambiguity is endogenously dynamic, for example, increasing during recessions. This results in an endogenously volatile and (counter-)cyclical equity premium. We calibrate the level of ambiguity aversion to match only the first moment of the risk-free rate in data, and ambiguity to match the uncertainty conditional on the historical growth path, and evaluate the model using moderate levels of risk aversion. We find that this simple modification of a Lucas-tree model accounts for a large part of the historical equity premium, both in terms of its level and variation over time.

In this work we develop an agent-based model that offers an alternative to standard, computable general equilibrium integrated assessment models (IAMs). The Dystopian Schumpeter meeting Keynes (DSK) model is composed of heterogeneous firms belonging to capital-good, consumption-good and energy sectors. Production and energy generation lead to greenhouse gas emissions, which affect temperature dynamics. Climate damages are modelled at the individual level as stochastic shocks hitting workers' labour productivity, energy efficiency, capital stock and inventories of firms. In that, aggregate damages emerge from the aggregation of losses suffered by heterogeneous, interacting and boundedly rational agents. The model is run focusing on a business-as-usual carbon-intensive scenario consistent with a Representative Concentration Pathway 8.5. We find that the DSK model is able to account for a wide ensemble of micro- and macro-empirical regularities concerning both economic and climate dynamics. Simulation experiments show a substantial lack of isomorphism between the effects of micro- and macro-level shocks, as it is typical in complex system models. In particular, different types of shocks have heterogeneous impact on output growth, unemployment rate, and the likelihood of economic crises, pointing to the importance of the different economic channel affected by the shock. Overall, we report much larger climate damages than those projected by standard IAMs under comparable scenarios, suggesting possible shifts in the growth dynamics, from a self-sustained pattern to stagnation and high volatility, and the need of urgent policy interventions.

We investigate the reliability of the 'Furfine filter' often used to identify interbank loans and interest rates from interbank payments settled at central banks. To this end, we have been granted access to records of all unsecured overnight interbank loans during a month from the banks that participated in Norges Bank's real-time gross settlement system. The filter applied was able to identify each of these loans and correctly derive the associated interest rates. The filter's reliability is also supported by additional evidence based on the Norwegian Overnight Weighted Average (NOWA) interest rates beyond the survey month. Sensitivity analyses suggest the share of false or overlooked loans may remain small if the filter design largely incorporates interbank market conventions regarding loan size requests and interests rate quotes.

The principal dominance concept for inequality-averse multidimensional social welfare comparisons, commonly known as lower orthant dominance, entails less or equal mass on all lower hyperrectangles of outcomes. Recently, it was shown that bivariate lower orthant dominance can be characterized in terms of two elementary mass transfer operations: diminishing mass transfer (reducing welfare) and correlation-increasing switches (increasing inequality). In this paper we provide a constructive algorithm, which decomposes the mass transfers into such welfare reductions and inequality increases.

We introduce a novel scheme based on a blending of Fourier-Motzkin elimination (FME) and adjustable robust optimization techniques to compute the maximum volume inscribed ellipsoid (MVE) in a polytopic projection. It is well-known that deriving an explicit description of a projected polytope is NP-hard. Our approach does not require an explicit description of the projection, and can easily be generalized to find a maximally sized convex body of a polytopic projection. Our obtained MVE is an inner approximation of the projected polytope, and its center is a centralized relative interior point of the projection. Since FME may produce many redundant constraints, we apply an LP-based procedure to keep the description of the projected polytopes at its minimal size. Furthermore, we propose an upper bounding scheme to evaluate the quality of the inner approximations. We test our approach on a simple polytope and a color tube design problem, and observe that as more auxiliary variables are eliminated, our inner approximations and upper bounds converge to optimal solutions. The online supplement is available at https://doi.org/10.1287/ijoc.2017.0763 .

In this work we develop a set of labour market and fiscal policy experiments upon the labour and credit augmented “Schumpeter meeting Keynes” agent-based model. The labour market is declined under two institutional variants, the “Fordist” and the “Competitive” set-ups meant to capture the historical transition from the Fordist toward the post “Thatcher- Reagan” period. Inside these two regimes, we study the different effects of supply-side active labour market policies (ALMPs) vs. demand-management passive labour market ones (PLMPs). In particular, we analyse the effects of ALMPs aimed at promoting job search, and at providing training to unemployed people. Next, we compare the effects of these policies with unemployment benefits simply meant to sustain income and therefore aggregate demand. Considering the burden of unemployment benefits in terms of public budget, we link such provision with the objectives of the European Stability and Growth Pact. Our results show that (i) an appropriate level of skills is not enough to sustain growth when workers face adverse labour demand; (ii) supply-side policies are not able to reverse the perverse interaction between flexibility and austerity; (iii) PLMPs outperform ALMPs in reducing unemployment and workers’ skills deterioration; and (iv) demand-management policies are better suited to mitigate inequality and to improve and sustain long-run growth.

The method of endogenous gridpoints (ENDG) significantly speeds up the solution to dynamic stochastic optimization problems with continuous state and control variables by avoiding repeated computations of expected outcomes while searching for optimal policy functions. While the method has been used in specific settings with one endogenous state dimension and one control, it has never been characterized for use in n-dimensional models. Using a general theoretical framework for dynamic stochastic optimization problems, I formalize the method of endogenous gridpoints and present conditions for the class of models that can be solved using ENDG. The framework is applied to several example models to show the breadth of problems for which endogenous gridpoints can be used. Further, I provide an interpolation technique for non-rectilinear grids that allows ENDG to be used in n-dimensional problems in an intuitive and computationally educient way. Relative to the traditional approach, the method of endogenous gridpoints with non-linear grid interpolation" solves a benchmark 2D model 7.0 to 7.8 times faster than the traditional solution method.

In this paper, we extend the concept of mutual exclusivity proposed by Dhaene and Denuit (1999) to its tail counterpart and baptise this new dependency structure as tail mutual exclusivity. Probability levels are first specified for each component of the random vector. Under this dependency structure, at most one exceedance over the corresponding VaRs is possible, the other components being zero in such a case. No condition is imposed when all components stay below the VaRs. Several properties of this new negative dependence concept are derived. We show that this dependence structure gives rise to the smallest value of Tail-VaR of a sum of risks within a given Frechet space, provided that the probability level of the Tail-VaR is close enough to one.