SSME structural dynamic model development

A mathematical model of the Space Shuttle Main Engine (SSME) as a complete assembly, with detailed emphasis on LOX and High Fuel Turbopumps is developed. The advantages of both complete engine dynamics, and high fidelity modeling are incorporated. Development of this model, some results, and projected applications are discussed

Dynamic Discrete Choice and Dynamic Treatment Effects

This paper considers semiparametric identification of structural dynamic discrete choice models and models for dynamic treatment effects. Time to treatment and counterfactual outcomes associated with treatment times are jointly analyzed. We examine the implicit assumptions of the dynamic treatment model using the structural model as a benchmark. For the structural model we show the gains from using cross equation restrictions connecting choices to associated measurements and outcomes. In the dynamic discrete choice model, we identify both subjective and objective outcomes, distinguishing ex post and ex ante outcomes. We show how to identify agent information sets.

Thermal-dynamic modeling study

Study provides basic information for designing models and conducting thermal-dynamic structural tests. Factors considered are development and interpretation of thermal-dynamic structural scaling laws; identification of major problem areas; and presentation of model fabrication, instrumentation, and test procedures

Identification of structural dynamic discrete choice models

This paper presents new identification results for the class of structural dynamic discrete choice models that are built upon the framework of the structural discrete Markov decision processes proposed by Rust (1994). We demonstrate how to semiparametrically identify the deep structural parameters of interest in the case where utility function of one choice in the model is parametric but the distribution of unobserved heterogeneities is nonparametric. The proposed identification method does not rely on the availability of terminal period data and hence can be applied to infinite horizon structural dynamic models. For identification we assume availability of a continuous observed state variable that satisfies certain exclusion restrictions. If such excluded variable is accessible, we show that the structural dynamic discrete choice model is semiparametrically identified using the control function approach. This is a substantial revision of "Semiparametric identification of structural dynamic optimal stopping time models", CWP06/07.

Semiparametric identification of structural dynamic optimal stopping time models

This paper presents new identification results for the class of structural dynamic optimal stopping time models that are built upon the framework of the structural discrete Markov decision processes proposed by Rust (1994). We demonstrate how to semiparametrically identify the deep structural parameters of interest in the case where the utility function of an absorbing choice in the model is parametric but the distribution of unobserved heterogeneity is nonparametric. Our identification strategy depends on availability of a continuous observed state variable that satisfies certain exclusion restrictions. If such excluded variable is accessible, we show that the dynamic optimal stopping model is semiparametrically identified using control function approaches

Pseudo-maximum likelihood estimation of a dynamic structural investment model

This paper belongs to the recent investment literature focused on the modelling of microeconomic investment decisions. The increasing concern about this topic is related to the growing availability of microeconomic datasets which show the investment behavior taking place at the firm level. This behavior is far from the smooth capital adjustment pattern derived from the traditional investment models. Rather it is characterized by infrequent and lumpy adjustment. New investment models must be considered to capture this behavior. In this paper we formulate a dynamic structural investment model with irreversibility and nonconvex adjustment costs and try to stress the importance of these costs in the firms' investment decisions. From the methodological point of view, we set the investment decision on the dynamic programming framework. More specifically, we consider a discrete choice dynamic programming problem in which firms decide to invest or not to invest. The estimation strategy we adopt is the Nested Pseudo-Likelihood (NPL) algorithm recently proposed by Aguirregabiria and Mira (2002). It is an estimation method which has clear advantages over previous techniques proposed in this context. Up to our knowledge, this paper constitutes the first empirical application of this estimation method

Heterogeneity in Returns to Work Experience: A Dynamic Model of Female Labor Force Participation

This paper provides structural estimates of heterogeneous returns to work experience for Japanese married women. A dynamic model of labor force participation is used to account for dynamic selfselection into employment. Heterogeneity is incorporated into the model in a way that allows for the multidimensional skill heterogeneity in employment and home production and for the individual-specific slope and curvature of experience effect on earnings. The structural estimates and their comparison to the reduced-form estimates highlight the importance of dynamic self-selection into employment and heterogeneity in returns to work experience.

Structural health monitoring and bridge condition assessment

Thesis (Ph.D.) University of Alaska Fairbanks, 2016This research is mainly in the field of structural identification and model calibration, optimal sensor placement, and structural health monitoring application for large-scale structures. The ultimate goal of this study is to identify the structure behavior and evaluate the health condition by using structural health monitoring system. To achieve this goal, this research firstly established two fiber optic structural health monitoring systems for a two-span truss bridge and a five-span steel girder bridge. Secondly, this research examined the empirical mode decomposition (EMD) method’s application by using the portable accelerometer system for a long steel girder bridge, and identified the accelerometer number requirements for comprehensively record bridge modal frequencies and damping. Thirdly, it developed a multi-direction model updating method which can update the bridge model by using static and dynamic measurement. Finally, this research studied the optimal static strain sensor placement and established a new method for model parameter identification and damage detection.Chapter 1: Introduction -- Chapter 2: Structural Health Monitoring of the Klehini River Bridge -- Chapter 3: Ambient Loading and Modal Parameters for the Chulitna River Bridge -- Chapter 4: Multi-direction Bridge Model Updating using Static and Dynamic Measurement -- Chapter 5: Optimal Static Strain Sensor Placement for Bridge Model Parameter Identification by using Numerical Optimization Method -- Chapter 6: Conclusions and Future Work

Semiparametric identification of structural dynamic optimal stopping time models

This paper presents new identification results for the class of structural dynamic optimal stopping time models that are built upon the framework of the structural discrete Markov decision processes proposed by Rust (1994). We demonstrate how to semiparametrically identify the deep structural parameters of interest in the case where the utility function of an absorbing choice in the model is parametric but the distribution of unobserved heterogeneity is nonparametric. Our identification strategy depends on availability of a continuous observed state variable that satisfies certain exclusion restrictions. If such excluded variable is accessible, we show that the dynamic optimal stopping model is semiparametrically identified using control function approaches.Structural dynamic discrete choice models, semiparametric identification, optimal stopping