Simulation based selection of competing structural econometric models

This paper proposes a formal model selection test for choosing between two competing structural econometric models. The procedure is based on a novel lack-of-fit criterion, namely, the simulated mean squared error of predictions (SMSEP), taking into account the complexity of structural econometric models. It is asymptotically valid for any fixed number of simulations, and allows for any estimator which has a vn asymptotic normality or is superconsistent with a rate at n. The test is bi-directional and applicable to non-nested models which are both possibly misspecified. The asymptotic distribution of the test statistic is derived. The proposed test is general regardless of whether the optimization criteria for estimation of competing models are the same as the SMSEP criterion used for model selection. An empirical application using timber auction data from Oregon is used to illustrate the usefulness and generality of the proposed testing procedure.Lack-of-fit, Model selection tests, Non-nested models, Simulated mean squared error of predictions

Consistent Model and Moment Selection Criteria for GMM Estimation with Applications to Dynamic Panel Data Models

This paper develops consistent model and moment selection criteria for GMM estimation. The criteria select the correct model specification and all correct moment conditions asymptotically. The selection criteria resemble the widely used likelihood-based selection criteria BIC, HQIC, and AIC. (The latter is not consistent.) The GMM selection criteria are based on the J statistic for testing over-identifying restrictions. Bonus terms reward the use of fewer parameters for a given number of moment conditions and the use of more moment conditions for a given number of parameters. The paper applies the model and moment selection criteria to dynamic panel data models with unobserved individual effects. The paper shows how to apply the selection criteria to select the lag length for lagged dependent variables, to detect the number and locations of structural breaks, to determine the exogeneity of regressors, and/or to determine the existence of correlation between some regressors and the individual effect. To illustrate the finite sample performance of the selection criteria and their impact on parameter estimation, the paper reports the results of a Monte Carlo experiment on a dynamic panel data model.Akaike information criterion, Bayesian information criterion, consistent selection procedure, generalized method of moments estimator, instrumental variables estimator, model selection, moment selection, panel data model, test of over-identifying restrictions

Testing real-time systems using TINA

The paper presents a technique for model-based black-box conformance testing of real-time systems using the Time Petri Net Analyzer TINA. Such test suites are derived from a prioritized time Petri net composed of two concurrent sub-nets specifying respectively the expected behaviour of the system under test and its environment.We describe how the toolbox TINA has been extended to support automatic generation of time-optimal test suites. The result is optimal in the sense that the set of test cases in the test suite have the shortest possible accumulated time to be executed. Input/output conformance serves as the notion of implementation correctness, essentially timed trace inclusion taking environment assumptions into account. Test cases selection is based either on using manually formulated test purposes or automatically from various coverage criteria specifying structural criteria of the model to be fulfilled by the test suite. We discuss how test purposes and coverage criterion are specified in the linear temporal logic SE-LTL, derive test sequences, and assign verdicts

A Novelty Search-based Test Data Generator for Object-oriented Programs

International audienceIn search-based structural testing, meta-heuristic search techniques have been frequently used to automate test data generation. In this paper, we introduce the use of novelty search algorithm to the test data generation problem based on statement-covered criterion. In this approach, we seek to explore the search space by considering diversity as the unique objective function to be optimized. In fact, instead of having a fitness-based selection, we select test cases based on a novelty score showing how different they are compared to all other solutions evaluated so far

Learning as a rational foundation for macroeconomics and finance

Expectations play a central role in modern macroeconomics. The econometric learning approach, in line with the cognitive consistency principle, models agents as forming expectations by estimating and updating subjective forecasting models in real time. This approach provides a stability test for RE equilibria and a selection criterion in models with multiple equilibria. Further features of learning – such as discounting of older data, use of misspecified models or heterogeneous choice by agents between competing models – generate novel learning dynamics. Empirical applications are reviewed and the roles of the planning horizon and structural knowledge are discussed. We develop several applications of learning with relevance to macroeconomic policy: the scope of Ricardian equivalence, appropriate specification of interest-rate rules, implementation of price-level targeting to achieve learning stability of the optimal RE equilibrium and whether, under learning, price-level targeting can rule out the deflation trap at the zero lower bound.cognitive consistency; E-stability; least-squares; persistent learning dynamics; business cycles; monetary policy; asset prices

Simulation-based Bayesian optimal ALT designs for model discrimination

Accelerated life test (ALT) planning in Bayesian framework is studied in this paper with a focus of differentiating competing acceleration models, when there is uncertainty as to whether the relationship between log mean life and the stress variable is linear or exhibits some curvature. The proposed criterion is based on the Hellinger distance measure between predictive distributions. The optimal stress-factor setup and unit allocation are determined at three stress levels subject to test-lab equipment and test-duration constraints. Optimal designs are validated by their recovery rates, where the true, data-generating, model is selected under the DIC (Deviance Information Criterion) model selection rule, and by comparing their performance with other test plans. Results show that the proposed optimal design method has the advantage of substantially increasing a test plan׳s ability to distinguish among competing ALT models, thus providing better guidance as to which model is appropriate for the follow-on testing phase in the experiment.NOTICE: this is the author's version of a work that was accepted for publication in RELIABILITY ENGINEERING & SYSTEM SAFETY. Changes resulting from the publishing process, such as editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in RELIABILITY ENGINEERING & SYSTEM SAFETY, 134, 1-9. DOI: 10.1016/j.ress.2014.10.00

Test Generation Based on Abstraction and Test Purposes to Complement Structural Tests

International audienceThis paper presents a computer aided model-based test generation method. We propose this approach as a complement to the LTG (Leirios Test Generator) method, which extracts functional tests out of a formal behavioral model M by means of static (or structural) selection criteria. Our method computes additional tests by applying dynamic (or behavioral) selection criteria (test purposes called TP). Applying TP directly to M is usually not possible for industrial applications due to the huge (possibly infinite) size of their state space. We compute an abstraction A of M by predicate abstraction. We propose a method to define a set of abstraction predicates from information of TP. We generate symbolic tests from A by using TP as a dynamic selection criterion. Then we instantiate them on M, which allows us play the tests on the implementation the same way as we play the functional ones. Our experimental results show that our tests are complementary to the structural ones

An Algorithm and Methodology for Static Response Based Damage Detection in Structural Systems

A damage detection algorithm and procedure is presented in this dissertation that utilizes static response data and Optimality Criterion optimization. Static displacement measurements are used as constraints in the damage detection algorithm that identifies potential areas of damage in structural systems. The research aims to improve upon the master\u27s level research performed by the author. First, the robustness of the algorithm is improved by use of a least squares approximation for determining the Lagrange multipliers that are necessary for optimization. Second, an active parameter selection subroutine is used to improve the accuracy of damage detection in the presence of experimental error. Third, an optimal load case algorithm is presented to eliminate procedural ambiguity and help engineers determine the best load case locations for damage detection. Last, modeling was improved with the creation of a new finite element that better models reduced moment resistance in connections. The new element is derived from elementary principles and is very well suited for optimization. The research attempts to utilize experimental test data whenever possible. When test data is not available, efforts are made to simulate real test conditions for damage detection. To illustrate the robustness of the algorithm, damage is detected in three different structural types. Reduced flexural stiffness is detected in a steel moment frame, reduced cross sectional area is detected in a three dimensional truss, and lastly a combination of reduced flexural stiffness and reduced moment capacity of connections is detected in a three dimensional structural grid