Dynamic Linear Models with R

Campagnoli, Patrizia; Petrone, Sonia; Petris, Giovanni De

doi:10.1007/b135794

Cited by 38 publications

(24 citation statements)

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“…The Koyck lag variables were added to a linear time-series regression, dynamic linear model 23,24 . The details of the model, including the intercept and the lag coefficients, are provided in Appendix S3.…”

Section: Methodsmentioning

confidence: 99%

Estimating the lagged effect of price discounting: a time-series study using transaction data of sugar sweetened beverages

Mamiya

Schmidt

Moodie

et al. 2021

Preprint

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Background: Price discount is an unregulated obesogenic environmental risk factor for the purchasing of unhealthy food, including Sugar Sweetened Beverages (SSB). Sales of price discounted food items are known to increase during the period of discounting. However, the presence and extent of the lagged effect of discounting, a sustained level of sales after discounting ends, is previously unaccounted for. We investigated the presence of the lagged effect of discounting on the sales of five SSB categories, which are soda, fruits juice, sport and energy drink, sweetened coffee and tea, and sweetened drinkable yogurt. Methods: We fitted a distributed lag model to weekly volume-standardized sales and percent discounting generated by a supermarket in Montreal, Canada between 2008 and 2013. Results: While the sales of SSB increased during the period of discounting, there was no evidence of a prominent lagged effect of discounting in four of the five SSB; the exception was sports and energy drinks, where a posterior mean of 28,459 servings (95% credible interval: 2,661 to 67,253) of excess sales can be attributed to the lagged effect in the target store during the study period. Conclusions: Our results indicate that previous studies may have underestimated the effect of price discounting for some food categories.

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Section: Methodsmentioning

confidence: 99%

Estimating the lagged effect of price discounting: a time-series study using transaction data of sugar sweetened beverages

Mamiya

Schmidt

Moodie

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…We selected the Koyck lag specification with λ bounded to the interval [0,1], given that the promotion is expected to have a monotonically decaying association with purchasing due to temporarily decreasing awareness among exposed consumers 4 . The time-series regression used in this study is a dynamic linear model, which allows regression parameters to vary smoothly over time 10,16 . The model was fit under the Bayesian framework.…”

Section: Methodsmentioning

confidence: 99%

Transfer functions: learning about a lagged exposure-outcome association in time-series data

Mamiya

et al. 2021

Preprint

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Many population exposures in time-series analysis, including food marketing, exhibit a time-lagged association with population health outcomes such as food purchasing. A common approach to measuring patterns of associations over different time lags relies on a finite-lag model, which requires correct specification of the maximum duration over which the lagged association extends. However, the maximum lag is frequently unknown due to the lack of substantive knowledge or the geographic variation of lag length. We describe a time-series analytical approach based on an infinite lag specification under a transfer function model that avoids the specification of an arbitrary maximum lag length. We demonstrate its application to estimate the lagged exposure-outcome association in food environmental research: display promotion of sugary beverages with lagged sales.

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“…For the process model, these Gaussian variables arise naturally by applying the system size expansion. We find the Gaussian assumptions justified by diagnostics of the fitted model, and it allows for the model's likelihood to be efficiently calculated using the extended Kalman filter [12]. This efficiency makes it feasible to estimate the effects of covariates on the process model's transmission rate in an embedded regression model, as well as allowing for parameters to vary over time according to a random walk to account for non-stationarity.…”

Section: Modelmentioning

confidence: 96%

A semi-parametric, state-space compartmental model with time-dependent parameters for forecasting COVID-19 cases, hospitalizations, and deaths

O’Dea¹,

Drake²

2021

Preprint

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Short-term forecasts of the dynamics of COVID-19 in the period up to its decline following mass vaccination was a task that received much attention but proved difficult to do with high accuracy. A major obstacle has been capturing variations in the underlying kinetics of transmission resulting from changes in public policy, individual behaviors, and evolution of the virus. However, the availability of standardized forecasts and versioned data sets from this period allows for continued work in this area. Here we introduce the Gaussian Infection State Space with Time-dependence (GISST) forecasting model. We evaluate its performance in 1-4 week ahead forecasts of COVID-19 cases, hospital admissions, and deaths in the state of California made with official reports of COVID-19, Google's mobility reports, and vaccination data available each week from June 29, 2020 to April, 26, 2021. Evaluation of these forecasts with a weighted interval score shows them to consistently outperform a naive baseline forecast and often score closer to or better than a high-performing ensemble forecaster. The GISST model also provides parameter estimates for a compartmental model of COVID-19 dynamics, includes a regression submodel for the transmission rate, and allows for parameters to vary over time according to a random walk. GISST provides a novel, balanced combination of computational efficiency, model interpretability, and applicability to large multivariate data sets that may prove useful in improving the accuracy of infectious disease forecasts.

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Dynamic Linear Models with R

Cited by 38 publications

References 0 publications

Estimating the lagged effect of price discounting: a time-series study using transaction data of sugar sweetened beverages

Estimating the lagged effect of price discounting: a time-series study using transaction data of sugar sweetened beverages

Transfer functions: learning about a lagged exposure-outcome association in time-series data

A semi-parametric, state-space compartmental model with time-dependent parameters for forecasting COVID-19 cases, hospitalizations, and deaths

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