What to Do about Missing Values in Time-Series Cross-Section Data
نویسندگان
چکیده
منابع مشابه
What To Do about Missing Values in Time-Series Cross-Section Data
Applications of modern methods for analyzing data with missing values, based primarily on multiple imputation, have in the last half-decade become common in American politics and political behavior. Scholars in this subset of political science have thus increasingly avoided the biases and inefficiencies caused by ad hoc methods like listwise deletion and best guess imputation. However, research...
متن کاملWhat to do (and not to do) with Time-Series Cross-Section Data
T AlTe examine some issues in the estimation of time-series cross-section models, calling into 1/11 \ question the conclusions of many published studies, particularly in thefield of comparative T v political economy. We show that the generalized least squares approach of Parks produces standard errors that lead to extreme overconfidence, often underestimating variability by 50% or more. We also...
متن کاملMissing data imputation in multivariable time series data
Multivariate time series data are found in a variety of fields such as bioinformatics, biology, genetics, astronomy, geography and finance. Many time series datasets contain missing data. Multivariate time series missing data imputation is a challenging topic and needs to be carefully considered before learning or predicting time series. Frequent researches have been done on the use of diffe...
متن کاملWhat to do about data?
The cost of sequencing whole mammalian genomes continues to plummet. New sequencing technologies are in development in many locations around the world, promising miraculously low prices and unbelievably short analysis times. When discussing the ‘$1,000 genome’, the question is no longer whether it will be achievable, but when. No matter how cheap sequencing the human genome can be, there is alw...
متن کاملResampling Time Series Using Missing Values Techniques
Abst rac t . Several techniques for resampling dependent data have already been proposed. In this paper we use missing values techniques to modify the moving blocks jackknife and bootstrap. More specifically, we consider the blocks of deleted observations in the blockwise jackknife as missing data which are recovered by missing values estimates incorporating the observation dependence structure...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: American Journal of Political Science
سال: 2010
ISSN: 0092-5853,1540-5907
DOI: 10.1111/j.1540-5907.2010.00447.x