Econometric Modelling with Time Series

Specification, Estimation and Testing

PART ONE: Maximum Likelihood

Chapter 2: Properties of Maximum Likelihood Estimators

(1) WLLN (Necessary Condition)

prop_wlln1.g
prop_wlln1.m
prop_wlln1.R

(2) WLLN (Sufficient Condition)

prop_wlln2.g
prop_wlln2.m
prop_wlln2.R

(3) Slutsky’s Theorem

prop_slutsky.g
prop_slutsky.m
prop_slutsky.R

(4) Properties of the Gradient Function

prop_gradient.g
prop_gradient.m
prop_gradient.R

(5) Graphical Demonstration of Consistency

prop_consistency.g
prop_consistency.m
prop_consistency.R

(6) Consistency of the Sample Mean Assuming Normality

prop_normal.g
prop_normal.m
prop_normal.R

(7) Inconsistency of the Sample Mean of a Cauchy Distribution

prop_cauchy.g
prop_cauchy.m
prop_cauchy.R

(8) Efficiency Property of Maximum Likelihood Estimators

prop_efficiency.g
prop_efficiency.m
prop_efficiency.R

(9) Asymptotic Normality (Exponential Distribution)

prop_asymnorm.g
prop_asymnorm.m
prop_asymnorm.R

(10) Asymptotic Normality (Chi Square Distribution)

prop_chisq.g
prop_chisq.m
prop_chisq.R

(11) Central Limit Theorem - Student t Distribution

prop_clt_student.g
prop_clt_student.m
prop_clt_student.R

(12) Edgeworth Expansions

prop_edgeworth.g
prop_edgeworth.m
prop_edgeworth.R

(13) Bias of the Sample Variance

prop_bias.g
prop_bias.m
prop_bias.R

(14) Portfolio Diversification

prop_diversify.g prop_diversify.m prop_diversify.R

apple.csv ford.csv diversify.mat diversify.Rdata

(15) Bimodal Likelihood

prop_bimodal.g
prop_bimodal.m
prop_bimodal.R

(16) Nonlinear Regression

prop_nonlinear.g
prop_nonlinear.m
prop_nonlinear.R