Sequential Monte Carlo Methods for Estimating Dynamic Microeconomic Models

This paper develops methods for estimating dynamic structural microeconomic models with serially correlated latent state variables. The proposed estimators are based on sequential Monte Carlo methods, or particle filters, and simultaneously estimate both the structural parameters and the trajectory of the unobserved state variables for each observational unit in the dataset. We focus two important special cases: single agent dynamic discrete choice models and dynamic games of incomplete information. The methods are applicable to both discrete and continuous state space models. We first develop a broad nonlinear state space framework which includes as special cases many dynamic structural models commonly used in applied microeconomics. Next, we discuss the nonlinear filtering problem that arises due to the presence of a latent state variable and show how it can be solved using sequential Monte Carlo methods. We then turn to estimation of the structural parameters and consider two approaches: an extension of the standard full-solution maximum likelihood procedure (Rust, 1987) and an extension of the two-step estimation method of Bajari, Benkard, and Levin (2007), in which the structural parameters are estimated using revealed preference conditions. Finally, we introduce an extension of the classic bus engine replacement model of Rust (1987) and use it both to carry out a series of Monte Carlo experiments and to provide empirical results using the original data.dynamic discrete choice, latent state variables, serial correlation, sequential Monte Carlo methods, particle filtering

A Classification and Survey of Computer System Performance Evaluation Techniques

Classification and survey of computer system performance evaluation technique

Efficient and Convergent Sequential Pseudo-Likelihood Estimation of Dynamic Discrete Games

We propose a new sequential Efficient Pseudo-Likelihood (k-EPL) estimator for dynamic discrete choice games of incomplete information. We show that each iteration in the k-EPL sequence is consistent and asymptotically efficient, so the first-order asymptotic properties do not vary across iterations. Furthermore, we show the sequence achieves higher-order equivalence to the finite-sample maximum likelihood estimator with iteration and that the sequence of estimators converges almost surely to the maximum likelihood estimator at a nearly-superlinear rate when the data are generated by any regular Markov perfect equilibrium, including equilibria that lead to inconsistency of other sequential estimators. When utility is linear in parameters, k-EPL iterations are computationally simple, only requiring that the researcher solve linear systems of equations to generate pseudo-regressors which are used in a static logit/probit regression. Monte Carlo simulations demonstrate the theoretical results and show k-EPL's good performance in finite samples in both small- and large-scale games, even when the game admits spurious equilibria in addition to one that generated the data

BLITZEN: A highly integrated massively parallel machine

The architecture and VLSI design of a new massively parallel processing array chip are described. The BLITZEN processing element array chip, which contains 1.1 million transistors, serves as the basis for a highly integrated, miniaturized, high-performance, massively parallel machine that is currently under development. Each processing element has 1K bits of static RAM and performs bit-serial processing with functional elements for arithmetic, logic, and shifting

Suspension of flexible cylinders in laminar liquid flow

Peer reviewedPostprin

When should you consider implanted nerve stimulators for lower back pain?

Patients can gain more pain relief from spinal cord stimulation (SCS) than from reoperation (strength of recommendation [SOR]: A, 2 randomized controlled trials [RCTs]). SCS can also treat chronic low back pain effectively (SOR: B, cohort studies). It's indicated when conservative measures have failed (SOR: C, expert opinion). The side effects and failure rates of SCS are well documented and should be considered before recommending the therapy to patients (SOR: A, systematic review of RCTs and cohort studies)

Nitrous Oxide Flux from Poultry-Manured Erosion Plots and Grass Filters after Simulated Rain

Adding carbon-rich materials to fields, like manure, may enhance denitrification. Grass filters, which are used to trap surface runoff from these fields, may also provide a carbon-rich environment that favors water infiltration and denitrification. Nitrous oxide (N2O) may be evolved these settings. It is a radiatively important trace gas and intermediate in the denitrification pathway and several other microbial processes. We measured N2O flux, after simulated rain, using a soil cover technique in poultry-manured plots and grass filters receiving their runoff. Intact soil cores were used to relate the N2O flux to the denitrification potential of the plots. Nitrous oxide fluxes were smaller in grass filters than in manured plots, even though more denitrifying bacteria were present. The average N2O flux in the three most dynamic erosion plots was 755 µg N2O-N m−2h−1, which was 39% of the maximal denitrification rate measured in acetylene-blocked, NO−3-amended soil cores. Nitrous oxide flux immediately after rainfall was greater than N2O flux measurements reported for similar agricultural settings

Time-Gated Topographic LIDAR Scene Simulation

The Digital Imaging and Remote Sensing Image Generation (DIRSIG) model has been developed at the RochesterInstitute of Technology (RIT) for over a decade. The model is an established, first-principles based scene simulationtool that has been focused on passive multi- and hyper-spectral sensing from the visible to long wave infrared (0.4 to 14 µm). Leveraging photon mapping techniques utilized by the computer graphics community, a first-principles based elastic Light Detection and Ranging (LIDAR) model was incorporated into the passive radiometry framework so that the model calculates arbitrary, time-gated radiances reaching the sensor for both the atmospheric and topographicreturns. The active LIDAR module handles a wide variety of complicated scene geometries, a diverse set of surface and participating media optical characteristics, multiple bounce and multiple scattering effects, and a flexible suite of sensormodels. This paper will present the numerical approaches employed to predict sensor reaching radiances andcomparisons with analytically predicted results. Representative data sets generated by the DIRSIG model for a topographical LIDAR will be shown. Additionally, the results from phenomenological case studies including standard terrain topography, forest canopy penetration, and camouflaged hard targets will be presented