Panel data

Summary

In statistics and econometrics, panel data and longitudinal data are both multi-dimensional data involving measurements over time. Panel data is a subset of longitudinal data where observations are for the same subjects each time. Time series and cross-sectional data can be thought of as special cases of panel data that are in one dimension only (one panel member or individual for the former, one time point for the latter). A literature search often involves time series, cross-sectional, or panel data. Cross-panel data (CPD) is an innovative yet underappreciated source of information in the mathematical and statistical sciences. CPD stands out from other research methods because it vividly illustrates how independent and dependent variables may shift between countries. This panel data collection allows researchers to examine the connection between variables across several cross-sections and time periods and analyze the results of policy actions in other nations. A study that uses pa

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https://en.wikipedia.org/wiki/Panel_data

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The additive hazard estimator is consistent for continuous-time marginal structural models

Pal Christie Ryalen, Mats Julius Stensrud

Marginal structural models (MSMs) allow for causal analysis of longitudinal data. The standard MSM is based on discrete time models, but the continuous-time MSM is a conceptually appealing alternative for survival analysis. In applied analyses, it is often assumed that the theoretical treatment weights are known, but these weights are usually unknown and must be estimated from the data. Here we provide a sufficient condition for continuous-time MSM to be consistent even when the weights are estimated, and we show how additive hazard models can be used to estimate such weights. Our results suggest that continuous-time weights perform better than IPTW when the underlying process is continuous. Furthermore, we may wish to transform effect estimates of hazards to other scales that are easier to interpret causally. We show that a general transformation strategy can be used on weighted cumulative hazard estimates to obtain a range of other parameters in survival analysis, and explain how this strategy can be applied on data using our R packages ahw and transform.hazards.

2019

Standard innovation surveys do not consider incoming spillovers for non-innovative firms. The Swiss innovation surveys presented here measure the importance of competitors' knowledge for both innovating and noninnovating firms. This original feature not only enables us to accurately identify the role of incoming knowledge on R&D decisions and innovation output, but also to compare resulting data with those which standard innovation questionnaires provide. Using a panel data over four periods, we show that knowledge from rivals actually deters manufacturing firms from engaging in R&D activities. Moreover, we provide stronger evidence that intra-industry spillovers are more detrimental to innovation than that generally provided by data from standard surveys. The results suggest that the dominance of the absorptive capacity effect is more important to firms investing in R&D and that non-innovative firms rely more heavily than expected on their competitors to maintain their technological capacities.

2009

2003