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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 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.

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.

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.

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 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.

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 .

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.

I introduce and evaluate a new stochastic simulation method for dynamic economic models. It is based on recent work in the operations research and engineering literatures (Van Roy et. al, 1997; Powell, 2007; Bertsekas, 2011). The baseline method involves rewriting the household's dynamic program in terms of post-decision states. This makes it possible to choose controls optimally without computing an expectation. I add a subroutine to the original algorithm that updates the values of states not visited frequently on the simulation path; and adopt a stochastic stepsize that efficiently weights information. Finally, I modify the algorithm to exploit GPU computing.

In this paper we describe a sequential importance sampling (SIS) procedure for counting the number of vertex covers in general graphs. The optimal SIS proposal distribution is the uniform over a suitably restricted set, but is not implementable. We will consider two proposal distributions as approximations to the optimal. Both proposals are based on randomization techniques. The first randomization is the classic probability model of random graphs, and in fact, the resulting SIS algorithm shows polynomial complexity for random graphs. The second randomization introduces a probabilistic relaxation technique that uses Dynamic Programming. The numerical experiments show that the resulting SIS algorithm enjoys excellent practical performance in comparison with existing methods. In particular the method is compared with cachet--an exact model counter, and the state of the art SampleSearch, which is based on Belief Networks and importance sampling.

Two difficulties arise in the estimation of AB models: (i) the criterion function has no simple analytical expression, (ii) the aggregate properties of the model cannot be analytically understood. In this paper we show how to circumvent these difficulties and under which conditions ergodic models can be consistently estimated by simulated minimum distance techniques, both in a long-run equilibrium and during an adjustment phase.

The package npbr is the first free specialized software for data edge and frontier analysis in the statistical literature. It provides a variety of functions for the best known and most innovative approaches to nonparametric boundary estimation. The selected methods are concerned with empirical, smoothed, unrestricted as well as constrained fits under both single and multiple shape constraints. They also cover data envelopment techniques as well as robust approaches to outliers. The routines included in npbr are user friendly and afford a large degree of flexibility in the estimation specifications. They provide smoothing parameter selection for the modern local linear and polynomial spline methods as well as for some promising extreme value techniques. Also, they seamlessly allow for Monte Carlo comparisons among the implemented estimation procedures. This package will be very useful for statisticians and applied researchers interested in employing nonparametric boundary regression models. Its use is illustrated with a number of empirical applications and simulated examples.

This study proposes an approach based on a perturbation technique to construct global solutions to dynamic stochastic general equilibrium models (DSGE). The main idea is to expand a solution in a series of powers of a small parameter scaling the uncertainty in the economy around a solution to the deterministic model, i.e. the model where the volatility of the shocks vanishes. If a deterministic path is global in state variables, then so are the constructed solutions to the stochastic model, whereas these solutions are local in the scaling parameter. Under the assumption that a deterministic path is already known the higher order terms in the expansion are obtained recursively by solving linear rational expectations models with time-varying parameters. The present work also proposes a method rested on backward recursion for solving general systems of linear rational expectations models with time-varying parameters and determines the conditions under which the solutions of the method exist.

The stable roommates problem with payments has as input a graph G=(V,E)G=(V,E) with an edge weighting w:E→R≥0w:E→R≥0 and the problem is to find a stable solution. A solution is a matching MM with a vector p∈R≥0V that satisfies pu+pv=w(uv)pu+pv=w(uv) for all uv∈Muv∈M and pu=0pu=0 for all uu unmatched in MM. A solution is stable if it prevents blocking pairs, i.e., pairs of adjacent vertices uu and vv with pu+pv

We propose a nonlinear infinite moving average as an alternative to the standard state space policy function for solving nonlinear DSGE models. Perturbation of the nonlinear moving average policy function provides a direct mapping from a history of innovations to endogenous variables, decomposes the contributions from individual orders of uncertainty and nonlinearity, and enables familiar impulse response analysis in nonlinear settings. When the linear approximation is saddle stable and free of unit roots, higher order terms are likewise saddle stable and first order corrections for uncertainty are zero. We derive the third order approximation explicitly, examine the accuracy of the method using Euler equation tests, and compare with state space approximations.

Representation of continuous-time ARMA (Auto-Regressive-Moving-Average), CARMA, time-series models is reviewed. Computational aspects of simulating and calculating the likelihood-function of CARMA models are summarized. Some numerical properties are illustrated by simulations. Methods for enforcing the stationarity restriction on the parameter space are discussed. Due to such methods restricted numerical estimation enforcing stationarity is possible. The impact of scaling of time axis on the magnitude of the parameters is demonstrated. Proper scaling of the time axis can give parameter values of similar magnitude which is useful for numerical work. The practicality of the computational approach is illustrated with some real and simulated data.

This paper provides an efficient way to generate a set of random choices on a set of budgets which satisfy the Generalised Axiom of Revealed Preferences (GARP), that is, they are consistent with utility maximisation. The choices are drawn from an approximate uniform distribution on the admissible region on each budget which ensures consistency with GARP, based on a Markovian Monte Carlo algorithm due to Smith (1984). This procedure can be used to extend Bronars‘ (1987) method as it can be used to approximate the power of tests for conditions for which GARP is a necessary but not sufficient condition (e.g., homotheticity, separability, risk aversion, etc.). For example, it allows to approximate the probability that a set of random choices which happens to satisfy GARP is also consistent with homotheticity. The approach can also be applied to production analysis and nonparametric tests of cost minimisation.

In this paper we focus on the specification of revenue functions in their dual price space. We consider two distance functions–the Shephard output distance function and the directional output distance function–and define both in price space. The former is multiplicative in nature and satisfies homogeneity, whereas the latter is additive and satisfies the translation property. Functional equation methods yield the translog specification in the case of the Shephard distance function and a quadratic specification in the case of the directional distance function. Monte Carlo evidence suggests that the quadratic specification outperforms the translog in large samples and in true models with plenty of curvature.

This paper applies non-linear methods to analyze and predict the daily open S&P index which is one of the most important stock index in the world. The aim of the analysis is to quantitatively show if the corresponding time series is a deterministic chaotic one and if one or more days ahead prediction can be achieved. These results make the present work a valuable tool for traders investors and funds.

The Cross Entropy method is a well-known adaptive importance sampling method for rare-event probability estimation, which requires estimating an optimal importance sampling density within a parametric class. In this article we estimate an optimal importance sampling density within a wider semiparametric class of distributions. We show that this semiparametric version of the Cross Entropy method frequently yields efficient estimators. We illustrate the excellent practical performance of the method with numerical experiments and show that for the problems we consider it typically outperforms alternative schemes by orders of magnitude.

We use a Monte Carlo experiment to compare the quadratic and translog functional forms in terms of their ability to approximate known frontiers that possess convex curvature. Unlike some of the existing simulation studies that have considered concave frontiers, we find that both functional forms provide a reliable approximation when a true frontier is convex. Our results lend support to existing intuitive explanations concerning the translog form’s innate propensity to yield convex, rather than concave, frontier estimates, suggesting that it should fare relatively well when modeling input isoquants. We also demonstrate that the quadratic functional form loses less of its flexibility than the translog function when shape constraints are imposed to satisfy regularity.

This paper develops an agent-based computational model of violent political revolutions in which a subjugated population of citizens and an armed revolutionary organisation attempt to overthrow a central authority and its loyal forces. The model replicates several patterns of rebellion consistent with major historical revolutions, and provides an explanation for the multiplicity of outcomes that can arise from an uprising. The relevance of the heterogeneity of scenarios predicted by the model can be understood by considering the recent experience of the Arab Spring involving several rebellions that arose in an apparently similar way, but resulted in completely different political outcomes: the successful revolution in Tunisia, the failed protests in Saudi Arabia and Bahrain, and civil war in Syria and Libya.

Many indicators of business and growth cycles have been constructed by both private and public agencies and are now in use as monitoring devices of economic conditions and for forecasting purposes. As these indicators are largely composite constructs using other economic data, their frequency composition is likely different to that of the variables they are used as indicators for. In this paper we use the Hilbert-Huang transform, which comprises the empirical mode decomposition (EMD) and the Hilbert spectrum, in order to analyse the frequency content of comparable OECD confidence indicators and national sentiment indicators for industrial production and consumption. We then compare these with the frequency content of both industrial production and real consumption growth data. The Hilbert-Huang methodology first uses a sifting process (EMD) to identify the embedded frequencies within a time series, and the changing nature of these embedded frequencies (IMFs) can then be analysed by estimating the instantaneous frequency (using the Hilbert spectrum). This methodology has several advantages over conventional spectral analysis: it handles non-stationary and non-linear processes, and it can cope with short data series. The aim of this paper is to decompose both indicator and actual economic variables to evaluate i) whether the number of IMFs are equivalent in both indicators and actual variables and ii) to see which frequencies are accounted for in indicators and which frequencies are not.

The COS method for pricing European and Bermudan options with one underlying asset was developed in [F. Fang and C. W. Oosterlee, SIAM J. Sci. Comput., 31 (2008), pp. 826--848] and [F. Fang and C. W. Oosterlee, Numer. Math., 114 (2009), pp. 27--62]. In this paper, we extend the method to higher dimensions, with a multidimensional asset price process. The algorithm can be applied to, for example, pricing two-color rainbow options but also to pricing under the popular Heston stochastic volatility model. For smooth density functions, the resulting method converges exponentially in the number of terms in the Fourier cosine series summations; otherwise we achieve algebraic convergence. The use of an FFT algorithm, for asset prices modeled by Lévy processes, makes the algorithm highly efficient. We perform extensive numerical experiments.

In this paper, we present a Markov chain Monte Carlo (MCMC) simulation algorithm for estimating parameters in the kernel density estimation of bivariate insurance claim data via transformations. Our data set consists of two types of auto insurance claim costs and exhibit a high-level of skewness in the marginal empirical distributions. Therefore, the kernel density estimator based on original data does not perform well. However, the density of the original data can be estimated through estimating the density of the transformed data using kernels. It is well known that the performance of a kernel density estimator is mainly determined by the bandwidth, and only in a minor way by the kernel choice. In the current literature, there have been some developments in the area of estimating densities based on transformed data, but bandwidth selection depends on pre-determined transformation parameters. Moreover, in the bivariate situation, each dimension is considered separately and the correlation between the two dimensions is largely ignored. We extend the Bayesian sampling algorithm proposed by Zhang, King and Hyndman (2006) and present a Metropolis-Hastings sampling procedure to sample the bandwidth and transformation parameters from their posterior density. Our contribution is to estimate the bandwidths and transformation parameters within a Metropolis-Hastings sampling procedure. Moreover, we demonstrate that the correlation between the two dimensions is well captured through the bivariate density estimator based on transformed data.

We propose a new method of single imputation, reconstruction, and estimation of nonreported, incorrect, implausible, or excluded values in more than one field of the record. In particular, we will be concerned with data sets involving a mixture of numeric, ordinal, binary, and categorical variables. Our technique is a variation of the popular nearest neighbor hot deck imputation (NNHDI) where “nearest” is defined in terms of a global distance obtained as a convex combination of the distance matrices computed for the various types of variables. We address the problem of proper weighting of the partial distance matrices in order to reflect their significance, reliability, and statistical adequacy. Performance of several weighting schemes is compared under a variety of settings in coordination with imputation of the least power mean of the Box-Cox transformation applied to the values of the donors. Through analysis of simulated and actual data sets, we will show that this approach is appropriate. Our main contribution has been to demonstrate that mixed data may optimally be combined to allow the accurate reconstruction of missing values in the target variable even when some data are absent from the other fields of the record.

This note describes how the incomplete markets model with aggregate uncertainty in Den Haan et al. [Comparison of solutions to the incomplete markets model with aggregate uncertainty. Journal of Economic Dynamics and Control, this issue] is solved using standard quadrature and projection methods. This is made possible by linking the aggregate state variables to a parameterized density that describes the cross-sectional distribution. A simulation procedure is used to find the best shape of the density within the class of approximating densities considered. This note compares several simulation procedures in which there is—as in the model—no cross-sectional sampling variation.

In this paper we consider the optimal stopping problem for general dynamic monetary utility functionals. Sufficient conditions for the Bellman principle and the existence of optimal stopping times are provided. Particular attention is paid to representations which allow for a numerical treatment in real situations. To this aim, generalizations of standard evaluation methods like policy iteration and dual and consumption based approaches are developed in the context of general dynamic monetary utility functionals. As a result, it turns out that the possibility of a particular generalization depends on specific properties of the utility functional under consideration.

Many efficient and accurate analytical methods for pricing American options now exist. However, while they can produce accurate option prices, they often do not give accurate critical stock prices. In this paper, we propose two new analytical approximations for American options based on the quadratic approximation. We compare our methods with existing analytical methods including the quadratic approximations in Barone-Adesi and Whaley (J Finance 42:301–320, 1987) and Barone-Adesi and Elliott (Stoch Anal Appl 9(2):115–131, 1991), the lower bound approximation in Broadie and Detemple (Rev Financial Stud 9:1211–1250, 1996), the tangent approximation in Bunch and Johnson (J Finance 55(5):2333–2356, 2000), the Laplace inversion method in Zhu (Int J Theor Appl Finance 9(7):1141–1177, 2006b), and the interpolation method in Li (Working paper, 2008). Both of our methods give much more accurate critical stock prices than all the existing methods above.

The paper proposes a numerical solution method for general equilibrium models with a continuum of heterogeneous agents, which combines elements of projection and of perturbation methods. The basic idea is to solve first for the stationary solution of the model, without aggregate shocks but with fully specified idiosyncratic shocks. Afterwards one computes a first-order perturbation of the solution in the aggregate shocks. This approach allows to include a high-dimensional representation of the cross-sectional distribution in the state vector. The method is applied to a model of household saving with uninsurable income risk and liquidity constraints. The model includes not only productivity shocks, but also shocks to redistributive taxation, which cause substantial short-run variation in the cross-sectional distribution of wealth. If those shocks are operative, it is shown that a solution method based on very few statistics of the distribution is not suitable, while the proposed method can solve the model with high accuracy, at least for the case of small aggregate shocks. Techniques are discussed to reduce the dimension of the state space such that higher order perturbations are feasible. Matlab programs to solve the model can be downloaded.

SummaryWe propose a flexible generalized auto-regressive conditional heteroscedasticity type of model for the prediction of volatility in financial time series. The approach relies on the idea of using multivariate B-splines of lagged observations and volatilities. Estimation of such a B-spline basis expansion is constructed within the likelihood framework for non-Gaussian observations. As the dimension of the B-spline basis is large, i.e. many parameters, we use regularized and sparse model fitting with a boosting algorithm. Our method is computationally attractive and feasible for large dimensions. We demonstrate its strong predictive potential for financial volatility on simulated and real data, and also in comparison with other approaches, and we present some supporting asymptotic arguments.

We propose a new methodology for structural estimation of infinite horizon dynamic discrete choice models. We combine the dynamic programming (DP) solution algorithm with the Bayesian Markov chain Monte Carlo algorithm into a single algorithm that solves the DP problem and estimates the parameters simultaneously. As a result, the computational burden of estimating a dynamic model becomes comparable to that of a static model. Another feature of our algorithm is that even though the number of grid points on the state variable is small per solution-estimation iteration, the number of effective grid points increases with the number of estimation iterations. This is how we help ease the "curse of dimensionality." We simulate and estimate several versions of a simple model of entry and exit to illustrate our methodology. We also prove that under standard conditions, the parameters converge in probability to the true posterior distribution, regardless of the starting values. © 2009 The Econometric Society.

Recently, because of Basel II and the subprime mortgage crisis, the quantification of the recovery size and the recovery rate for the debt of a defaulted company is a serious problem for financial institutions and their supervisors, but there has been no study of structure of the recovery process which is the relationship between time and the cumulative recovery size. Existent recovery models do not regard the recovery progress before the time of achievement of recovery. We directly model recovery process for the debt of a single defaulted company. We model the recovery process by a homogeneous compound Poisson process and extend our model to an inhomogeneous compound Poisson process. The interest rate is explicitly used in our model. By our model, the relationship between the cumulative recovery, the increment of recovery, the initial debt amount, the last recovery possible time and the interest rate can be analyzed. We derive the expected value and the variance of the survival value of the debt and the recovery rate, and also derive the probability distribution function and the expected value of the recovery completion time. Moreover we present the numerical methods for calculating the expected value and the variance based on Panjer recursion formula and the fast Fourier transformation, and show numerical results. Also we propose a new method of calculating the transition density of an inhomogeneous compound Poisson process. Our method is based on approximating an inhomogeneous compound Poisson process by a piecewise homogeneous compound Poisson process. This method is used to compute the expected value and the variance of an inhomogeneous compound Poisson process.

Any function from a non-empty polytope into itself that is locally gross direction preserving is shown to have the fixed point property. Brouwer's fixed point theorem for continuous functions is a special case. We discuss the application of the result in the area of non-cooperative game theory.This discussion paper has resulted in a publication in Operations Research Letters. (2008, vol. 36, issue 1, pp.89-93.)

Computational Economics, 31 (2), ISSN:0927-7099, ISSN:1572-9974

At the time of writing this article, Fourier inversion is the computational method of choice for a fast and accurate calculation of plain vanilla option prices in models with an analytically available characteristic function. Shifting the contour of integration along the complex plane allows for different representations of the inverse Fourier integral. In this article, we present the optimal contour of the Fourier integral, taking into account numerical issues such as cancellation and explosion of the characteristic function. This allows for robust and fast option pricing for almost all levels of strikes and maturities.

In linear-quadratic control (LQC) problems with singular control cost matrix and/or singular transition matrix, we derive a reduction of the dimension of the Riccati matrix, simplifying iteration and solution. Employing a novel transformation, we show that, under a certain rank condition, the matrix of optimal feedback coefficients is linear in the reduced Riccati matrix. For a substantive class of problems, our technique permits scalar iteration, leading to simple analytical solution. By duality the technique can also be applied to Kalman filtering problems with a singular measurement error covariance matrix.

Motivated by computerized markets, this paper considers direct exchange between matched agents, just two at a time. Each party holds a ”commodity vector,” and each seeks, whenever possible, a better holding. Focus is on feasible, voluntary exchanges, driven only by (projected) differences in generalized gradients. The paper plays down the importance of agents’ competence, experience and foresight. It also reduces the role of optimization, and it allows non-smooth data. Yet it identifies reasonable conditions which suffice for convergence to competitive equilibrium.

As a refinement of the concept of stationary point, the notion of perfect stationary point was formulated in the literature. Although simplicial methods could be applied to approximate such a point, these methods do not make use of the possible differentiability of the problem and can be very time-consuming even for small-scale problems. To fully exploit the differentiability of the problem, this paper develops an interior-point path-following method for computing a perfect stationary point of a polynomial mapping on a polytope. By incorporating a logarithmic barrier term into the linear objective function with an appropriate convex combination, the method closely approximates some stationary points of the mapping on a perturbed polytope, especially when the perturbation is sufficiently small. It is proved that there exists a smooth path which starts from a point in the interior of a polytope and ends at a perfect stationary point. A predictor-corrector method is adopted for numerically following the path. Numerical results further confirm the effectiveness of the method.

Ng and Perron (2001) designed a unit root test, which incorporates the properties of DF-GLS and Phillips Perron test. Ng and Perron claim that the test performs exceptionally well especially in the presence of a negative moving average. However, the performance of the test depends heavily on the choice of the spectral density estimators used in the construction of the test. Various estimators for spectral density exist in the literature; each have a crucial impact on the output of test, however there is no clarity on which of these estimators gives the optimal size and power properties. This study aims to evaluate the performance of the Ng-Perron for different choices of spectral density estimators in the presence of a negative and positive moving average using Monte Carlo simulations. The results for large samples show that: (a) in the presence of a positive moving average, testing with the kernel based estimator gives good effective power and no size distortion, and (b) in the presence of a negative moving average, the autoregressive estimator gives better effective power, however, huge size distortion is observed in several specifications of the data-generating process.

Euler-equation methods for solving nonlinear dynamic models involve parameterizing some policy functions. We argue that in the typical macroeconomic model with valuable leisure, labor function is particularly convenient for parameterizing. This is because under the labor-function parameterization, the intratemporal first-order condition admits a closed-form solution, while under other parameterizations, there should be a numerical solution. In the context of a simulation-based parameterized expectations algorithm, we find that using the labor-function parameterization instead of the standard consumption-function parameterization reduces computational time by more than a factor of ten.

In this paper I present a proposal to obtain appropriate initial conditions when solving general equilibrium rational expectations models with the Parameterized Expectations Algorithm. The proposal is based on a log-linear approximation to the model under study, so that it can be though of as a particular variant of the homotopy approach.The main advantages of the proposal are: i. it guarantees the ergodicity of the initial time series used as an input to the Parameterized Expectations algorithm; ii. it performs well as regards speed of convergence when compared to some homotopy alternatives; iii. it is easy to implement. The claimed advantages are successfully illustrated in the framework of the Cooley and Hansen (1989) model with indivisible labor and money demand motivated via a cash-in-advance constraint, as compared to a procedure based on the standard implementation of homotopy principles.

This paper introduces a non-parametric binary classification tree approach to inferring unobserved strategies from the observed actions of economic agents. The strategies are in the form of possibly nested if–then statements. We apply our approach to experimental data from the repeated ultimatum game, which was conducted in four different countries by Roth et al. (Am. Econ. Rev. 81 (1991) 1068). We find that strategy inference is consistent with existing inference, provides new explanations for subject behavior, and provides new empirically based hypotheses regarding ultimatum game strategies. We conclude that strategy inference is potentially useful as a complementary method of statistical inference in applied research.

The problem of computing equilibria for general equilibrium models with incomplete real asset markets, or GEI models for the sake of brevity, is reconsidered. It is shown here that the rank-dropping behavior of the asset return matrix could be dealt with in rather a simple fashion: We first compute its singular value decomposition, and then, through this decomposition, construct, by the introduction of a homotopy parameter, a new matrix such that it has constant rank before a desired equilibrium is reached. By adjunction of this idea to the homotopy method, a simpler constructive proof is obtained for the generic existence of GEI equilibria. For the purpose of computing these equilibria, from this constructive proof is then derived a path-following algorithm whose performance is finally demonstrated by means of three numerical examples.

This study aims to describe the size distribution of Portuguese firms, as measured by annual sales and total assets, between 2006 and 2012, giving an economic interpretation for the time evolution of the distribution. Three distributions are fitted to data: the lognormal, the Zipf and the Simplified Canonical Law (SCL). Methods of estimation include Maximum Likelihood, modified Ordinary Least Squares in log-log scale and Nonlinear Least Squares considering the Levenberg-Marquardt algorithm. Lognormal and Zipf distributions can be justified from the Gibrat's law and, in Zipf case a constraint must be added, for instance, on the minimum size of firms. As for SCL, firstly presented by Mandelbrot in the context of linguistics, the argument used to deduce the distribution of words frequency in texts is adapted to the distribution of firms. This is done by defining production units which can be glued to constitute firms so that the network of firms can be used flexibly to satisfy the most diverse needs in the economy in the less costly way and reducing at the maximum the delay in achieving it. For this distribution, different interpretations of the estimated parameters found in the literature are confronted and discussed in the light of data. Using this interpretation, we attempt to find a characterization of the Portuguese economic activity rhythm, diversity of activity sectors and competition. Diversity is captured by an entropy measure at different levels of aggregation and not just at the higher level as is usually done in literature. The interrelations between these economic characteristics and others, such as the level of concentration of firm size distribution, are reassembled from literature and confronted. Analyzing entropy at different levels of aggregation allows verifying how the evolution of activity rhythm can have a distinct impact in diversity at those different levels.

In this paper we will consider Foata map as a function which associates another permutation, known as F(δ ), to each permutation &#948 = &#948 1 &#948 2 ⋅ ⋅ ⋅ &#948 m of the integers {1, 2, . . . ,m}, in such a way that the number of cycles of &#948 is equal to the number of left to right minima of F( δ). We will analyze the behaviour of the iterated maps, F2(δ ) = F&#948(F(δ )), F3(δ ), ... and so on. It is clear that given a permutation &#948, positive integer numbers k exist such that Fk( δ) = . Consider the smallest of these numbers and call it n. We shall call the (ordered) set of permutations C(δ ) = {&#948 , F( δ), . . . , Fn−1(δ )} a Foata circuit and n its length. The length of Foata circuits is the main concern of this paper: to our knowledge, no study has been devoted to this subject yet. Experimental results are quite surprising. In computable cases, generally speaking, there are one (at most two) circuit(s) of very large length (up to 85% of the total number of permutations) and a number of little circuits, even of some units of components. In this paper we will give a list of these circuits for permutations up to 11 elements. However these data give rise to some conjectures, difficult to verify: the investigation on permutations of more than 10 elements is a challenge and the case of m = 11 is a demonstration of this issue! At the same time, in this paper we will also discuss some properties of 'short' circuits, i.e. the ones consisting in 2, 3 or 4 permutations.

This note provides some examples of plant leaves whose outline maps exhibit Poincare recurrence and mixing while subjected to repeated Anosov diffeomorphic transformation. The datasets and a computer program (Fortran 77) have also been given.