GRADUATE SCHOOL OF WESTMINSTER INTERNATIONAL UNIVERSITY IN TASHKENT PROFESSORSHIP OF STATISTICS AND ECONOMETRICS

Linear regression is one of the widely used statistical methods in social sciences. The core part of the regressions are coefficients which bring some inference. Yet, we rely on hypothesis testing or confidence intervals and certain assumptions underlying linear models such as sample size being large enough. In this study, we suggest alternative way of constructing confidence intervals using bootstrap which is expected to work well even when the sample size is smaller than required per OLS assumptions. We find that even in small samples, bootstrap confidence intervals can perform better than traditional interval estimations