Estimation of Dynamic Latent Variable Models Using Simulated Nonparametric Moments

Abstract. Given a model that can be simulated, conditional moments at a trial parameter value can be calculated with high accuracy by applying kernel smoothing methods to a long simulation. With such conditional moments in hand, standard method of moments techniques can be used to estimate the parameter. Because conditional moments are calculated using kernel smoothing rather than simple averaging, it is not necessary that the model be simulable subject to the conditioning information that is used to define the moment conditions. For this reason, the proposed estimator is applicable to general dynamic latent variable models. It is shown that as the number of simulations diverges, the estimator is consistent and a higher-order expansion reveals the stochastic difference between the infeasible GMM estimator based on the same moment conditions and the simulated version. In particular, we show how to adjust standard errors to account for the simulations. Monte Carlo results show how the estimator may be applied to a range of dynamic latent variable (DLV) models, and that it performs well in comparison to several other estimators that have been proposed for DLV models.dynamic latent variable models; simulation-based estimation; simulated moments; kernel regression; nonparametric estimation

The Human version of Moore-Shannon's Theorem: The Design of Reliable Economic Systems

Moore & Shannon's theorem is the cornerstone in reliability theory, but cannot be applied to human systems in its original form. A generalization to human systems would therefore be of considerable interest because the choice of organization structure can remedy reliability problems that notoriously plaque business operations, financial institutions, military intelligence and other human activities. Our main result is a proof that provides answers to the following three questions. Is it possible to design a reliable social organization from fallible human individuals? How many fallible human agents are required to build an economic system of a certain level of reliability? What is the best way to design an organization of two or more agents in order to minimize error? On the basis of constructive proofs, this paper provides answers to these questions and thus offers a method to analyze any form of decision making structure with respect to its reliability.Organizational design; reliability theory; decision making; project selection

Indirect likelihood inference

Given a sample from a fully specified parametric model, let Zn be a given finite-dimensional statistic - for example, an initial estimator or a set of sample moments. We propose to (re-)estimate the parameters of the model by maximizing the likelihood of Zn. We call this the maximum indirect likelihood (MIL) estimator. We also propose a computationally tractable Bayesian version of the estimator which we refer to as a Bayesian Indirect Likelihood (BIL) estimator. In most cases, the density of the statistic will be of unknown form, and we develop simulated versions of the MIL and BIL estimators. We show that the indirect likelihood estimators are consistent and asymptotically normally distributed, with the same asymptotic variance as that of the corresponding efficient two-step GMM estimator based on the same statistic. However, our likelihood-based estimators, by taking into account the full finite-sample distribution of the statistic, are higher order efficient relative to GMM-type estimators. Furthermore, in many cases they enjoy a bias reduction property similar to that of the indirect inference estimator. Monte Carlo results for a number of applications including dynamic and nonlinear panel data models, a structural auction model and two DSGE models show that the proposed estimators indeed have attractive finite sample properties.indirect inference; maximum-likelihood; simulation-based

SNM Guide

This is a guide that explains how to use software that implements the simulated nonparametric moments (SNM) estimator proposed by Creel and Kristensen (2009). The guide shows how results of that paper may easily be replicated, and explains how to install and use the software for estimation of simulable econometric models.econometric software; dynamic latent variable models; simulation-based estimation; simulated moments; kernel regression; nonparametric estimation

Ytringsfrihet og tiggeforbud - Finnes det rettslige skranker for innføring av tiggeforbud?

Oppgaven ser på om det finnes skranker som begrenser adgangen til å innføre tiggeforbud. Tiggeforbudet i politiloven § 14 nr. 8 og forslaget om et nasjonalt tiggeforbud som ble fremsatt vinteren 2015 behandles opp mot de skranker som kan utledes av EMK art. 10

Magic angle spinning (MAS) NMR linewidths in the presence of solid-state dynamics

In solid-state NMR, the magic angle spinning (MAS) technique fails to suppress anisotropic spin interactions fully if reorientational dynamics are present, resulting in a decay of the rotational-echo train in the time-domain signal. We show that a simple analytical model can be used to quantify this linebroadening effect as a function of the MAS frequency, reorientational rate constant, and magnitude of the inhomogeneous anisotropic broadening. We compare this model with other theoretical approaches and with exact computer simulations, and show how it may be used to estimate rate constants from experimental NMR data

ABC of SV : limited information likelihood inference in stochastic volatility jump-diffusion models

We develop novel methods for estimation and filtering of continuous-time models with stochastic volatility and jumps using so-called Approximate Bayesian Computation which build likelihoods based on limited information. The proposed estimators and filters are computationally attractive relative to standard likelihood-based versions since they rely on low-dimensional auxiliary statistics and so avoid computation of high-dimensional integrals. Despite their computational simplicity, we find that estimators and filters perform well in practice and lead to precise estimates of model parameters and latent variables. We show how the methods can incorporate intra-daily information to improve on the estimation and filtering. In particular, the availability of realized volatility measures help us in learning about parameters and latent states. The method is employed in the estimation of a flexible stochastic volatility model for the dynamics of the S&P 500 equity index. We find evidence of the presence of a dynamic jump rate and in favor of a structural break in parameters at the time of the recent financial crisis. We find evidence that possible measurement error in log price is small and has little effect on parameter estimates. Smoothing shows that, recently, volatility and the jump rate have returned to the low levels of 2004-2006

Computer Aided Verification of Lamport's Fast Mutual Exclusion Algorithm - Using Coloured Petri Nets and Occurrence Graphs with Symmetries

In this paper, we present a new computer tool for verification of distributed systems. As an example, we establish the correctness of Lamport's Fast Mutual Exclusion Algorithm. The tool implements the method of occurrence graphs with symmetries (OS-graphs) for Coloured Petri Nets(CP-nets). The basic idea in the approach is to exploit the symmetries inherent in many distributed systems to construct a condensed state space. We demonstrate a signigicant increase in the number of states which can be analysed. The paper is to a large extent self-contained and does not assume any prior knowledge of CP-nets (or any other kinds of Petri Nets) or OS-graphs. CP-nets and OS-graphs are not our invention. Our contribution is development of the tool and verification of the example.Index Terms: Modelling and Analysis of Distributed Systems, Formal Verification, Coloured Petri Nets, High-Level Petri Nets, Occurrence Graphs, State Spaces, Symmetries, Mutual Exclusion