Estimating spillovers using imprecisely measured networks

In many experimental contexts, whether and how network interactions impact the outcome of interest for both treated and untreated individuals are key concerns. Networks data is often assumed to perfectly represent these possible interactions. This paper considers the problem of estimating treatment effects when measured connections are, instead, a noisy representation of the true spillover pathways. We show that existing methods, using the potential outcomes framework, yield biased estimators in the presence of this mismeasurement. We develop a new method, using a class of mixture models, that can account for missing connections and discuss its estimation via the Expectation-Maximization algorithm. We check our method's performance by simulating experiments on real network data from 43 villages in India. Finally, we use data from a previously published study to show that estimates using our method are more robust to the choice of network measure

Identification and Estimation in an Incoherent Model of Contagion

This paper deals with the issues of identification and estimation in the canonical model of contagion advanced in Pesaran and Pick (2007). The model is a two-equation nonlinear simultaneous equations system with endogenous dummy variables; it also represents an extension of univariate threshold autoregressive (TAR) models to a simultaneous equations framework. For a range of economic fundamentals, the model produces multiple (i.e. two) equilibria, and the choice of the equilibrium is modeled as being driven by a Bernoulli process; further, the presence of multiple equilibria leads to an incoherent econometric specification. The coherency issue is then reflected in the analytical expression for the likelihood function derived in the paper. It is proved that neither identification nor Full Information Maximum Likelihood (FIML) estimation of the model require knowledge of the Bernoulli process driving the solution choice in the multiple equilibria region. Monte Carlo experiments show that the FIML estimator performs better than the GIVE estimators proposed in Pesaran and Pick (2007). Finally, an empirical illustration based on stock market returns is provided

Quantization of Prior Probabilities for Hypothesis Testing

Bayesian hypothesis testing is investigated when the prior probabilities of the hypotheses, taken as a random vector, are quantized. Nearest neighbor and centroid conditions are derived using mean Bayes risk error as a distortion measure for quantization. A high-resolution approximation to the distortion-rate function is also obtained. Human decision making in segregated populations is studied assuming Bayesian hypothesis testing with quantized priors

U.S. State-Level Carbon Dioxide Emissions: A Spatial-Temporal Econometric Approach of the Environmental Kuznets Curve

One of the major criticisms of past environmental Kuznets curve (EKC) studies is that the spatiotemporal aspects within the data have largely been ignored. By ignoring the spatial aspect of pollution emissions past estimates of the EKC implicitly assume that a region’s emissions are unaffected by events in neighboring regions (i.e., assume there are no transboundary pollution emissions between neighbors). By ignoring the spatial aspects within the data several past estimates of the EKC could have generated biased or inconsistent regression results. By ignoring the temporal aspect within the data several past estimates of the EKC could have generated spurious regression results or misspecified t and F statistics. To address this potential misspecification we estimate the relationship between state-level carbon dioxide emissions and income (GDP) accounting for both the spatiotemporal components within the data. Specifically, we estimate a dynamic spatiotemporal panel model using a newly proposed robust, spatial fixed effects model. This new estimation scheme is appropriate for panels with large N and T. Consistent with the EKC hypothesis we find the inverted-U shaped relationship between CO2 emissions and income. Further, we find adequate evidence that carbon dioxide emissions and state-level GDP are temporally and spatially dependent. These findings offer policy implications for both interstate energy trade and pollution emission regulations. These implications are particularly important for the formulation of national policies related to the 2009 Copenhagen Treaty in which the U.S. has committed to significantly reduce greenhouse gas emissions over the next twenty years.Environmental Kuznets Curve, Carbon Dioxide, Spatial Econometrics, Panel Data Econometrics, Time Series Analysis, Environmental Economics, Pollution Economics, Environmental Economics and Policy, Q50, Q53, Q43, C01, C33,

Economic tomography: the possibility to anticipate and respond to socio-economic crises

The article discusses an approach based on an original hypothesis related to the peculiarities of Russia’s development (on the one hand, its scale, the Russian mentality and a certain closeness of the economy; on the other hand, a significant dominant resource and human potential, and, as a consequence, a genuine role in the global economic community), the diagnosis of which (at the level of the well-being of individuals and inhabited areas) can be used to identify crises, provide an early assessment of threats to socio-economic development of regions as well as help to evaluate the state of the region over a 3 to 5 year period. In other words, in order to ensure that executives have enough time to mount a sufficiently rapid response to the crises and administrative errors and to reduce the impact of emerging threats. The aim of this paper is to present theoretical and methodological tools for the recognition of the early stages of emerging threats, allowing fewer losses to be experienced during the crisis period. Simulation experiments were carried out for the purpose of classifying previously occurring social and economic crises (9 possible variants were reviewed) and mathematically processed trajectories of change in the main indicators for the well-being of individuals and inhabited areas, taking the influence of various factors into account. On the basis of the authors’ proposed approach (referred to as economic tomography) an attempt is made to comprehensively assess the state of sample representative regions of Russia.The research has been supported by the Russian Science Foundation (project № 14–18–00574 'Information-analytical system "Anticrisis:" diagnostics of the regions, threat assessment and scenario forecasting for the preservation and strengthening of economic security and well-being of Russia')

Testing jointly for structural changes in the error variance and coefficients of a linear regression model

We provide a comprehensive treatment for the problem of testing jointly for structural changes in both the regression coefficients and the variance of the errors in a single equation system involving stationary regressors. Our framework is quite general in that we allow for general mixing-type regressors and the assumptions on the errors are quite mild. Their distribution can be non-normal and conditional heteroskedasticity is permitted. Extensions to the case with serially correlated errors are also treated. We provide the required tools to address the following testing problems, among others: a) testing for given numbers of changes in regression coefficients and variance of the errors; b) testing for some unknown number of changes within some pre-specified maximum; c) testing for changes in variance (regression coefficients) allowing for a given number of changes in the regression coefficients (variance); d) a sequential procedure to estimate the number of changes present. These testing problems are important for practical applications as witnessed by interests in macroeconomics and finance where documenting structural changes in the variability of shocks to simple autoregressions or Vector Autoregressive Models has been a concern.First author draf

An Improved Bootstrap Test of Stochastic Dominance

We propose a new method of testing stochastic dominance that improves on existing tests based on the standard bootstrap or subsampling. The method admits prospects involving infinite as well as finite dimensional unknown parameters, so that the variables are allowed to be residuals from nonparametric and semiparametric models. The proposed bootstrap tests have asymptotic sizes that are less than or equal to the nominal level uniformly over probabilities in the null hypothesis under regularity conditions. This paper also characterizes the set of probabilities that the asymptotic size is exactly equal to the nominal level uniformly. As our simulation results show, these characteristics of our tests lead to an improved power property in general. The improvement stems from the design of the bootstrap test whose limiting behavior mimics the discontinuity of the original test's limiting distribution.Set estimation, Size of test, Similarity; Bootstrap, Subsampling

Identification and Estimation of Nonlinear Dynamic Panel Data Models with Unobserved Covariates

This paper considers nonparametric identification of nonlinear dynamic models for panel data with unobserved voariates. Including such unobserved covariates may control for both the individual-specific unobserved heterogeneity and the endogeneity of the explanatory variables. Without specifying the distribution of the initial condition with the unobserved variables, we show that the models are nonparametrically identified from three periods of data. The main identifying assumption requires the evolution of the observed covariates depends on the unobserved covariates but not on the lagged dependent variable. We also propose a sieve maximum likelihood estimator (MLE) and focus on two classes of nonlinear dynamic panel data models, i.e., dynamic discrete choice models and dynamic censored models. We present the asymptotic property of the sieve MLE and investigate the finite sample properties of these sieve-based estimator through a Monte Carlo study. An intertemporal female labor force participation model is estimated as an empirical illustration using a sample from the Panel Study of Income Dynamics (PSID).