ECON10005 Lecture Notes - Lecture 15: Point Estimation, Standard Deviation, Probability Mass Function
27 Jul 2018
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Document Summary
Random variable is described entirely by its probability density (or mass) function. Determined from the density/mass function by computing the first four moments. These quantities that describe the shape of a density function are known as parameters. Note: shape characteristics describe the behaviour of probability density functions. Random variable generates observations that form a sample. Can infer the value of an unknown population parametre by using an appropriate sample statistic. Estimator and estimates (cid:3364) is an estimator random variable that"s used to generate estimates of the population mean. Estimator is a rule that tells how to combine data in order to produce an estimate of an unknown quantity. Estimators are described by their probability density/mass functions, known as a sampling distribution. Note: things that connect the parameters of interest are the point estimators. Unbiased if the sampling distribution is centred around the true population parameter. Consistent if the sampling distribution collapses to the true population parameter as n .
