This study explores the possibility that cheep credit, provided to firms when profit opportunities on real investments are low, and when opportunities of financial investments are present, will loose some of it’s stimulus effect due to a crowding out effect of financial investments on real investments. Analyzing the changes in debt, and it’s channels of use during the recession of the covid19 pandemic, between firms with a history of stock buybacks, and firms without such a history, the study finds a significantly higher increase in debt for firms with a history of doing stock buybacks. The study concludes that this effect is due to firms finding financial uses of more cheap credit, which does crowd out real investments.

The objective of this study was to investigate the utilization of the diagnose-based Charlson Comorbidity Index (CCI) and the medication-based Rx-Risk Comorbidity Index on Swedish administrative data. Data was collected over a ten-year period from the National Patient Register and the National Prescribed Medication Register on 3609 respondents from the national public health survey 2018, aged 16-84 and registered in Stockholm County. The overall aim was to identify comorbid conditions in the study population; and to examine if the identified comorbidities differ between indices, based on subject characteristics such as age and gender. Moreover, the specific aim was to quantify correlation between the indices, as well as within indices over look-back periods of up to ten years. Among the study population, 13 % were identified with at least one comorbid condition through CCI, and 87 % had medications indicative of at least one condition covered by Rx-Risk. Both the original Charlson weights and updated weights by Quan were used to compute the comorbidity scores for CCI. Results showed that when CCI and Quan may have scored low, the Rx-Risk picked up more conditions. The Spearman rank correlation between CCI and Quan scores resulted in relatively high correlation with a coefficient of 0.82 (p-value < 0.05) over look-back periods of 2, 5 and 10 years. Moreover, the correlation between CCI and Rx-Risk was fairly low over all look-back periods with a correlation coefficient of 0.34 (p-value < 0.05) at most. The within-correlation showed that CCI identified much of the comorbidity between the one- and two-year look-back periods, whilst Rx-Risk identified much comorbidity within the one-year look-back period. The overall implications of the presented results are that a utilization of Charlson index and Rx-Risk is likely to capture comorbid conditions in different health care settings, and thus expected correlation is to be of modest level between the two indices. The research question of interest should therefore determine which index is favorable when assessment of comorbidity is desired.

Non-response is an error common to most surveys. In this dissertation, the error of non-response is described in terms of its sources and its contribution to the Mean Square Error of survey estimates. Various response and completion rates are defined. Techniques are examined that can be used to identify the extent of nonresponse bias in surveys. Methods to identify auxiliary variables for use in nonresponse adjustment procedures are described. Strategies for dealing with nonresponse are classified into two types, namely preventive strategies and post hoc adjustments of data. Preventive strategies discussed include the use of call-backs and follow-ups and the selection of a probability sub-sample of non-respondents for intensive follow-ups. Post hoc adjustments discussed include population and sample weighting adjustments and raking ratio estimation to compensate for unit non-response as well as various imputation methods to compensate for item non-response. / Mathematical Sciences / M. Com. (Statistics)

Non-response is an error common to most surveys. In this dissertation, the error of non-response is described in terms of its sources and its contribution to the Mean Square Error of survey estimates. Various response and completion rates are defined. Techniques are examined that can be used to identify the extent of nonresponse bias in surveys. Methods to identify auxiliary variables for use in nonresponse adjustment procedures are described. Strategies for dealing with nonresponse are classified into two types, namely preventive strategies and post hoc adjustments of data. Preventive strategies discussed include the use of call-backs and follow-ups and the selection of a probability sub-sample of non-respondents for intensive follow-ups. Post hoc adjustments discussed include population and sample weighting adjustments and raking ratio estimation to compensate for unit non-response as well as various imputation methods to compensate for item non-response. / Mathematical Sciences / M. Com. (Statistics)