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About 10,000 papers for “Distributed lag”

Distributed lag models

Patrik Dian · Digital Repository (National Repository of Grey Literature) · 2022

The aim of this bachelor thesis is to unite the theory about distribu- ted lag models and autoregressive distributed lag model, which includes lagged dependent variables and application of these models on real data. The properties of these models are also presented. Dynamic models are highly used for financial and economic data because of their ability to capture lagged effect on dependent variable. As a similar topic there are mentioned models of intervention analysis which are used to examine the external effects on time series and to model the in- terventions using indicator variables. Finally, applications of mentioned models on two data sets are introduced and analysis of the effect of coronavirus pandemic on time series is demonstrated. 1

Cited by 0

Distributed Lag Models

Jonggyu Baek, Brisa N. Sánchez, Veronica J. Berrocal et al. · Epidemiology · 2015

Built environment factors constrain individual level behaviors and choices, and thus are receiving increasing attention to assess their influence on health. Traditional regression methods have been widely used to examine associations between built environment measures and health outcomes, where a fixed, prespecified spatial scale (e.g., 1 mile buffer) is used to construct environment measures. However, the spatial scale for these associations remains largely unknown and misspecifying it introduces bias. We propose the use of distributed lag models (DLMs) to describe the association between built environment features and health as a function of distance from the locations of interest and circumvent a-priori selection of a spatial scale. Based on simulation studies, we demonstrate that traditional regression models produce associations biased away from the null when there is spatial correlation among the built environment features. Inference based on DLMs is robust under a range of scenarios of the built environment. We use this innovative application of DLMs to examine the association between the availability of convenience stores near California public schools, which may affect children's dietary choices both through direct access to junk food and exposure to advertisement, and children's body mass index z scores.

Cited by 29Open access

Distributed Lag Order Determination

Richard Meese · International Finance Discussion Paper · 1978

This paper is organized as follows: parameterization problems endemic to time series models are discussed in section one. New approaches to the parameterization problems are summarized and then applied to the problem of simulataneously estimating the length and the coefficients of a distributed lat regression model. The asymptotic properties of the new estimator of the distributed lag model (DLM) are examined in section two, and the small sample properties of the estimator are examined in section three using Mote Carlo experiments.

Cited by 0Open access

Lag Specification in Rational Distributed Lag Structural Models

Dominique M. Hanssens, Lon‐Mu Liu · Journal of Business and Economic Statistics · 1983

This article presents methods for the preliminary specification of distributed lags in structural models in the absence of sufficient a priori information. It is argued that an instrumental-variables procedure produces good results as long as the goodness of fit in the instrument equations is satisfactory. When this is not the case, simple OLS specification is preferable to more complex instrumental-variables methods, even though it may produce some spurious parameters. The procedures are illustrated with a simulated two-equation system and a model of annual supply and price of hogs.

Cited by 24

Treed distributed lag nonlinear models

Daniel Mork, Ander Wilson · Biostatistics · 2020

In studies of maternal exposure to air pollution, a children's health outcome is regressed on exposures observed during pregnancy. The distributed lag nonlinear model (DLNM) is a statistical method commonly implemented to estimate an exposure-time-response function when it is postulated the exposure effect is nonlinear. Previous implementations of the DLNM estimate an exposure-time-response surface parameterized with a bivariate basis expansion. However, basis functions such as splines assume smoothness across the entire exposure-time-response surface, which may be unrealistic in settings where the exposure is associated with the outcome only in a specific time window. We propose a framework for estimating the DLNM based on Bayesian additive regression trees. Our method operates using a set of regression trees that each assume piecewise constant relationships across the exposure-time space. In a simulation, we show that our model outperforms spline-based models when the exposure-time surface is not smooth, while both methods perform similarly in settings where the true surface is smooth. Importantly, the proposed approach is lower variance and more precisely identifies critical windows during which exposure is associated with a future health outcome. We apply our method to estimate the association between maternal exposures to PM$_{2.5}$ and birth weight in a Colorado, USA birth cohort.

Cited by 32Open access

Generalized Linear Distributed Lag Models

Hanh Nguyen, Qin Shao · Journal of Data Science · 2021

We propose distributed generalized linear models for the purpose of incorporating lagged effects. The model class provides a more accurate statistical measure of the relationship between the dependent variable and a series of covariates. The estimators from the proposed procedure are shown to be consistent. Simulation studies not only confirm the asymptotic properties of the estimators, but exhibit the adverse effects of model misspecification in terms of accuracy of model estimation and prediction. The application is illustrated by analyzing the presidential election data of 2016.

Cited by 0Open access

dlim: Distributed Lag Interaction Model

Danielle Demateis, Kayleigh P. Keller, Ander Wilson · 2024

Collection of functions for fitting and interpreting distributed lag interaction models (DLIM). A DLIM regresses a scalar outcome on repeated measures of exposure and allows for modification by a continuous variable. Includes a dlim() function for fitting, predict() function for inference, and plotting functions for visualization. Details on methodology are described in Demateis et al. (2024) &lt;<a href="https://doi.org/10.1002%2Fenv.2843" target="_top">doi:10.1002/env.2843</a>&gt;.

Cited by 0Open access

Distributed lag non‐linear models

Antonio Gasparrini, Ben Armstrong, Michael G. Kenward · Statistics in Medicine · 2010

Environmental stressors often show effects that are delayed in time, requiring the use of statistical models that are flexible enough to describe the additional time dimension of the exposure-response relationship. Here we develop the family of distributed lag non-linear models (DLNM), a modelling framework that can simultaneously represent non-linear exposure-response dependencies and delayed effects. This methodology is based on the definition of a 'cross-basis', a bi-dimensional space of functions that describes simultaneously the shape of the relationship along both the space of the predictor and the lag dimension of its occurrence. In this way the approach provides a unified framework for a range of models that have previously been used in this setting, and new more flexible variants. This family of models is implemented in the package dlnm within the statistical environment R. To illustrate the methodology we use examples of DLNMs to represent the relationship between temperature and mortality, using data from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) for New York during the period 1987-2000.

Cited by 2,384Open access

Treed distributed lag nonlinear models

Mork, Daniel, Wilson, Ander · arXiv (Cornell University) · 2020

In studies of maternal exposure to air pollution a children's health outcome is regressed on exposures observed during pregnancy. The distributed lag nonlinear model (DLNM) is a statistical method commonly implemented to estimate an exposure-time-response function when it is postulated the exposure effect is nonlinear. Previous implementations of the DLNM estimate an exposure-time-response surface parameterized with a bivariate basis expansion. However, basis functions such as splines assume smoothness across the entire exposure-time-response surface, which may be unrealistic in settings where the exposure is associated with the outcome only in a specific time window. We propose a framework for estimating the DLNM based on Bayesian additive regression trees. Our method operates using a set of regression trees that each assume piecewise constant relationships across the exposure-time space. In a simulation, we show that our model outperforms spline-based models when the exposure-time surface is not smooth, while both methods perform similarly in settings where the true surface is smooth. Importantly, the proposed approach is lower variance and more precisely identifies critical windows during which exposure is associated with a future health outcome. We apply our method to estimate the association between maternal exposure to PM$_{2.5}$ and birth weight in a Colorado USA birth cohort.

Cited by 0Open access