Inference in Coarsened Time Series via Generalized Method of Moments

We study statistical inference procedures in coarsened time series through the generalized method of moments. A new model for the coarsened time series via multiple potential outcomes is proposed. It can be naturally extended for inferring multi‐variate coarsened time series. We show that this framework generates a general class of estimators. It neatly generalizes the classical Horvitz–Thompson estimator for handling coarsened time series data. Asymptotic properties, including consistency and limiting distribution, of the proposed estimators are investigated. Estimators of the optimal weight matrix and the long‐run covariance matrix are also derived. In particular, confidence intervals of the mean function of the potential outcome as a function of coarsening index can be constructed. A real‐data application on air quality in the USA is investigated.

• Publication year

  2024

• Venue

  Journal of Time Series Analysis

• Publication date

  2024-04-10

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1111/jtsa.12740
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• General and feasible tests with multiply-imputed datasets

  2021cited by this paper
• Optimal difference-based variance estimators in time series: A general framework

  2021cited by this paper
• Efficient GMM Estimation with Incomplete Data

  2020cited by this paper
• Mobile and Wearable Technology for the Monitoring of Diabetes-Related Parameters: Systematic Review

  2020cited by this paper
• Mean-Structure and Autocorrelation Consistent Covariance Matrix Estimation

  2020cited by this paper
• A doubly corrected robust variance estimator for linear GMM

  2019cited by this paper
• Graphical Models for Processing Missing Data

  2018cited by this paper
• Methods for combining continuously measured glucose and activity data in people with Type 2 diabetes: Challenges and solutions

  2018cited by this paper
• Asymptotic Theory for Estimators of High-Order Statistics of Stationary Processes

  2018cited by this paper
• Causal Inference: A Missing Data Perspective

  2017cited by this paper
• Multiple Improvements of Multiple Imputation Likelihood Ratio Tests

  2017cited by this paper
• High‐order Corrected Estimator of Asymptotic Variance with Optimal Bandwidth

  2017cited by this paper
• Nonparametric Imputation by Data Depth

  2017cited by this paper
• Time Series Experiments and Causal Estimands: Exact Randomization Tests and Trading

  2017cited by this paper
• GMM with Multiple Missing Variables

  2016cited by this paper
• Dissecting multiple imputation from a multi-phase inference perspective: what happens when God’s, imputer’s and analyst’s models are uncongenial?

  2016cited by this paper
• A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix

  2014cited by this paper
• Proofs for large sample properties of generalized method of moments estimators

  2012influential reference
• Nonparametric HAC Estimation for Time Series Data with Missing Observations

  2012influential reference
• Asymptotic theory for stationary processes

  2011cited by this paper
• Efficient semiparametric estimation of multi-valued treatment effects under ignorability

  2010cited by this paper
• The Multiple Adaptations of Multiple Imputation

  2007cited by this paper
• STRONG INVARIANCE PRINCIPLES FOR DEPENDENT RANDOM VARIABLES

  2007cited by this paper
• Multiple Imputation of Missing Income Data in the National Health Interview Survey

  2006cited by this paper
• Nonlinear system theory: another look at dependence.

  2005cited by this paper
• Multiple Edit/Multiple Imputation for Multivariate Continuous Data

  2003cited by this paper
• Multiple Imputation for Incomplete Data With Semicontinuous Variables

  2003cited by this paper
• The Large Sample Behaviour of the Generalized Method of Moments Estimator in Misspecified Models

  2003cited by this paper
• Inference for imputation estimators

  2000cited by this paper
• DETECTING LACK OF IDENTIFICATION IN GMM

  2000cited by this paper
• A Reality Check for Data Snooping

  2000cited by this paper
• TESTS OF RANK

  2000cited by this paper
• Large-sample theory for parametric multiple imputation procedures

  1998cited by this paper
• Inferring the rank of a matrix

  1997cited by this paper
• Non-response models for the analysis of non-monotone ignorable missing data.

  1997cited by this paper
• Multiple Imputation After 18+ Years

  1996cited by this paper
• On the asymptotic properties of ldu-based tests of the rank of a matrix

  1996cited by this paper
• Coarsening at random in general sample spaces and random censoring in continuous time

  1995cited by this paper
• The stationary bootstrap

  1994cited by this paper
• Ignorability and coarse data: some biomedical examples.

  1993cited by this paper
• Multiple imputation in mixture models for nonignorable nonresponse with follow-ups

  1993cited by this paper
• An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator

  1992cited by this paper
• Efficacy of repeated measures in regression models with measurement error.

  1992cited by this paper
• Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation

  1991influential reference
• Ignorability and Coarse Data

  1991cited by this paper
• Hypothesis Testing with Efficient Method of Moments Estimation

  1987cited by this paper
• INFERENCE AND MISSING DATA

  1975cited by this paper
• The Measurement of Power Spectra from the Point of View of Communications Engineering

  1958cited by this paper

• No citing papers are available for this paper.