Econometrics: A Simple Introduction

K.H. Erickson
ā§Ē.ā§­
ā§ŠāϟāĻŋ āϰāĻŋāĻ­āĻŋāω
āχ-āĻŦ⧁āĻ•
100
āĻĒ⧃āĻˇā§āĻ āĻž
āωāĻĒāϝ⧁āĻ•ā§āϤ
āϰ⧇āϟāĻŋāĻ‚ āĻ“ āϰāĻŋāĻ­āĻŋāω āϝāĻžāϚāĻžāχ āĻ•āϰāĻž āĻšā§ŸāύāĻŋ  āφāϰāĻ“ āϜāĻžāύ⧁āύ

āĻāχ āχ-āĻŦ⧁āϕ⧇āϰ āĻŦāĻŋāĻˇā§Ÿā§‡

Econometrics: A Simple Introduction offers an accessible guide to the principles and methods of econometrics, with data samples, regressions, equations and diagrams to illustrate the analysis.

Examine a linear and multiple regression model, ordinary least squares method, and the Gauss-Markov conditions for a best linear unbiased estimator.

Understand hypothesis testing, with a null hypothesis, t, F or chi-square test statistics and distributions, and interpret regression results. Dummy variables model qualitative data and Chow tests assess regression equivalence.

Explore heteroscedasticity with the White method and with generalized least squares, Goldfeld-Quandt, Breusch-Pagan, and White tests. Assess autocorrelation with Durbin-Watson, Durbin h, and Breusch-Godfrey tests, lagged variables and auxiliary regressions.

Assess the impact of omitted variables, incorrect variables or functional form, and a non-normal distribution with Ramsey RESET and Jarque-Bera tests. Model random variables with the Method of Moments’ estimators, instrumental variables and Hausman test.

āϰ⧇āϟāĻŋāĻ‚ āĻ“ āĻĒāĻ°ā§āϝāĻžāϞ⧋āϚāύāĻžāϗ⧁āϞāĻŋ

ā§Ē.ā§­
ā§ŠāϟāĻŋ āϰāĻŋāĻ­āĻŋāω

āχ-āĻŦ⧁āϕ⧇ āϰ⧇āϟāĻŋāĻ‚ āĻĻāĻŋāύ

āφāĻĒāύāĻžāϰ āĻŽāϤāĻžāĻŽāϤ āϜāĻžāύāĻžāύāĨ¤

āĻĒāĻ āύ āϤāĻĨā§āϝ

āĻ¸ā§āĻŽāĻžāĻ°ā§āϟāĻĢā§‹āύ āĻāĻŦāĻ‚ āĻŸā§āϝāĻžāĻŦāϞ⧇āϟ
Android āĻāĻŦāĻ‚ iPad/iPhone āĻāϰ āϜāĻ¨ā§āϝ Google Play āĻŦāχ āĻ…ā§āϝāĻžāĻĒ āχāύāĻ¸ā§āϟāϞ āĻ•āϰ⧁āύāĨ¤ āĻāϟāĻŋ āφāĻĒāύāĻžāϰ āĻ…ā§āϝāĻžāĻ•āĻžāωāĻ¨ā§āĻŸā§‡āϰ āϏāĻžāĻĨ⧇ āĻ…āĻŸā§‹āĻŽā§‡āϟāĻŋāĻ• āϏāĻŋāĻ™ā§āĻ• āĻšā§Ÿ āĻ“ āφāĻĒāύāĻŋ āĻ…āύāϞāĻžāχāύ āĻŦāĻž āĻ…āĻĢāϞāĻžāχāύ āϝāĻžāχ āĻĨāĻžāϕ⧁āύ āύāĻž āϕ⧇āύ āφāĻĒāύāĻžāϕ⧇ āĻĒ⧜āϤ⧇ āĻĻā§‡ā§ŸāĨ¤
āĻ˛ā§āϝāĻžāĻĒāϟāĻĒ āĻ“ āĻ•āĻŽā§āĻĒāĻŋāωāϟāĻžāϰ
Google Play āĻĨ⧇āϕ⧇ āϕ⧇āύāĻž āĻ…āĻĄāĻŋāĻ“āĻŦ⧁āĻ• āφāĻĒāύāĻŋ āĻ•āĻŽā§āĻĒāĻŋāωāϟāĻžāϰ⧇āϰ āĻ“ā§Ÿā§‡āĻŦ āĻŦā§āϰāĻžāωāϜāĻžāϰ⧇ āĻļ⧁āύāϤ⧇ āĻĒāĻžāϰ⧇āύāĨ¤
eReader āĻāĻŦāĻ‚ āĻ…āĻ¨ā§āϝāĻžāĻ¨ā§āϝ āĻĄāĻŋāĻ­āĻžāχāϏ
Kobo eReaders-āĻāϰ āĻŽāϤ⧋ e-ink āĻĄāĻŋāĻ­āĻžāχāϏ⧇ āĻĒāĻĄāĻŧāϤ⧇, āφāĻĒāύāĻžāϕ⧇ āĻāĻ•āϟāĻŋ āĻĢāĻžāχāϞ āĻĄāĻžāωāύāϞ⧋āĻĄ āĻ“ āφāĻĒāύāĻžāϰ āĻĄāĻŋāĻ­āĻžāχāϏ⧇ āĻŸā§āϰāĻžāĻ¨ā§āϏāĻĢāĻžāϰ āĻ•āϰāϤ⧇ āĻšāĻŦ⧇āĨ¤ āĻŦā§āϝāĻŦāĻšāĻžāϰāĻ•āĻžāϰ⧀āϰ āωāĻĻā§āĻĻ⧇āĻļā§āϝ⧇ āϤ⧈āϰāĻŋ āϏāĻšāĻžā§ŸāϤāĻž āϕ⧇āĻ¨ā§āĻĻā§āϰāϤ⧇ āĻĻ⧇āĻ“ā§ŸāĻž āύāĻŋāĻ°ā§āĻĻ⧇āĻļāĻžāĻŦāϞ⧀ āĻ…āύ⧁āϏāϰāĻŖ āĻ•āϰ⧇ āϝ⧇āϏāĻŦ eReader-āĻ āĻĢāĻžāχāϞ āĻĒāĻĄāĻŧāĻž āϝāĻžāĻŦ⧇ āϏ⧇āĻ–āĻžāύ⧇ āĻŸā§āϰāĻžāĻ¨ā§āϏāĻĢāĻžāϰ āĻ•āϰ⧁āύāĨ¤