Joint Analysis of the Discount Factor and Payoff Parameters in Dynamic Discrete Choice Games

Most empirical models of dynamic games assume the discount factor to be known and focus on the estimation of the payoff parameters. However, the discount factor can be identifed when the payoffs satisfy parametric or other nonparametric restrictions. We show when the payoffs take the popular linear-in-parameter specification, the joint identification of the discount factor and payoff parameters can be simplified to a one-dimensional model that is easy to analyze. We also show that switching costs (e.g. entry costs) that often feature in empirical work can be identifed in closed-form, independently of the discount factor and other specification of the payoff function. Our identification strategies are constructive. They lead to easy to compute estimands that are global solutions. Estimating the discount factor permits direct inference on borrowing rate. Our estimates of the switching costs can be used for specification testing. We illustrate with a Monte Carlo study and the dataset from Ryan (2012)

Nonparametric Euler Equation Identi cation and Estimation

We consider nonparametric identification and estimation of pricing kernels, or equivalently of marginal utility functions up to scale, in consumption based asset pricing Euler equations. Ours is the first paper to prove nonparametric identification of Eu

Nonparametric Euler Equation Identification andEstimation

We consider nonparametric identification and estimation of pricing kernels, or equivalently of marginal utility functions up to scale, in consumption based asset pricing Euler equations.Ours is the first paper to prove nonparametric identification of Euler equations under low level conditions (without imposing functional restrictions or just assuming completeness). We also propose a novel nonparametric estimator based on our identification analysis, which combines standard kernel estimation with the computation of a matrix eigenvector problem. Our esti-mator avoids the ill-posed inverse issues associated with existing nonparametric instrumental variables based Euler equation estimators. We derive limiting distributions for our estimator and for relevant associated functionals. We provide a Monte Carlo analysis and an empirical application to US household-level consumption data.nonparametric identificatio

Peripheral blood gene expression profiles in COPD subjects

To identify non-invasive gene expression markers for chronic obstructive pulmonary disease (COPD), we performed genome-wide expression profiling of peripheral blood samples from 12 subjects with significant airflow obstruction and an equal number of non-obstructed controls. RNA was isolated from Peripheral Blood Mononuclear Cells (PBMCs) and gene expression was assessed using Affymetrix U133 Plus 2.0 arrays.Tests for gene expression changes that discriminate between COPD cases (FEV1 80% predicted, FEV1/FVC > 0.7) were performed using Significance Analysis of Microarrays (SAM) and Bayesian Analysis of Differential Gene Expression (BADGE). Using either test at high stringency (SAM median FDR = 0 or BADGE p < 0.01) we identified differential expression for 45 known genes. Correlation of gene expression with lung function measurements (FEV1 & FEV1/FVC), using both Pearson and Spearman correlation coefficients (p < 0.05), identified a set of 86 genes. A total of 16 markers showed evidence of significant correlation (p < 0.05) with quantitative traits and differential expression between cases and controls. We further compared our peripheral gene expression markers with those we previously identified from lung tissue of the same cohort. Two genes, RP9and NAPE-PLD, were identified as decreased in COPD cases compared to controls in both lung tissue and blood. These results contribute to our understanding of gene expression changes in the peripheral blood of patients with COPD and may provide insight into potential mechanisms involved in the disease. © 2011 Bhattacharya et al; licensee BioMed Central Ltd

Identification in discrete Markov decision models

Copyright © Cambridge University Press 2014.We derive conditions for the identification of the structural parameters in Markov decision model under the assumptions of Rust (1987, Econometrica 55, 999-1033) when the payoff function is parametrically specified. Identification in this class of dynamic problems is difficult to establish since the parameters of interest enter the value function nonlinearly, and the value function is only defined implicitly as a fixed point of some functional equation. We show it is sufficient to verify identification in the pseudomodel, which is more tractable as it is originally designed to reduce the computational burden in the estimation problem, for the identification of the data generating parameter of the underling model. Our results extend naturally to a class of dynamic discrete action games commonly used in empirical industrial organizations