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Econometrics Melody
เข้าร่วมเมื่อ 22 ก.พ. 2020
multicollinearity, missing obs, outliers influential data point | Econometrics | U/Grad | MPhil| PhD
@ Data Problems
* Multicollinearity
* Missing Observations
[Examples in Stata]
* Outliers
[Influential Data Points]
* Summary of the results of
the Finite-Sample Properties
of the Least Squares Estimator
* Multicollinearity
* Missing Observations
[Examples in Stata]
* Outliers
[Influential Data Points]
* Summary of the results of
the Finite-Sample Properties
of the Least Squares Estimator
มุมมอง: 84
วีดีโอ
distribution of ols beta | signific of regression | Oaxaca dec | Econometrics | U/Grad | MPhil | PhD
มุมมอง 65ปีที่แล้ว
* Distribution of the ols beta estimate * Use of the CLRM Assumptions * Independence of bols and e [or f(e) = s2=e'e/n-k] * Confidence Interval of β * Oaxaca's Decomposition * Significance of the regression * Marginal distribution of t|X
estimating the variance of the OLS beta estimate || Econometrics || U/Grad | MPhil | PhD
มุมมอง 103ปีที่แล้ว
* Estimating the variance of the ols beta estimate * s2 = e'e / n-k E(s2/X) = σ2 * var(bols/X) = σ2 (X'X)-1 var(bols/X) = [e'e/n-k] (X'X)-1 var(bols/X) = s2 (X'X)-1 * Assumed Homoscedasticity E(εε'/X) = σ2 I
Gauss Markov Theorem | min variance linear unbiased estimate || Econometrics || U/Grad | MPhil | PhD
มุมมอง 117ปีที่แล้ว
* Gauss Markov Theorem * For constant vector w, in the CLRM, the MVLUE of w'β is w'b, where b = ols estimate * Whether X is stochastic or nonstochastic, the ols beta is the MVLUE of population beta in the CLRM.
(Un)Conditional mean and variance of the OLS beta estimate || Econometrics || U/Grad || MPhil || PhD
มุมมอง 245ปีที่แล้ว
* (Un)Conditional mean and variance of the ols beta estimate * population model: y = Xβ ε sample model: y = Xb e * E(bols/X) = β E(bols) = β * var(bols/X) = σ2(X'X)-1 var(bols) = σ2 Ex[(X'X)-1] Assumed Homoscedasticity E(εε'/X) = σ2 I
r-square when multiple variables are added to the model || Econometrics || U/Grad || MPhil || PhD
มุมมอง 63ปีที่แล้ว
* Coefficient of Determination (R2), when multiple variables are added to the model * y = X1b1 e1 ; y = X1b1 X2b2 e1.2 e1.2'e1.2 = e1'e1 - b2'X2'M1X2b2 R21.2 = R21 ( 1- R21 ) r*2y2.1 * Amemiya's prediction criterion for comparing different models
(adjusted) r-square || r-square when a variable is added || Econometrics || U/Grad || MPhil || PhD
มุมมอง 133ปีที่แล้ว
* Coefficient of Determination (R2), adjusted R2, and R2 when a variable is added * R2 = SSR/SST = 1 - (SSE/SST) R2 = b'X'M0Xb / y'M0y R2 = 1 - (e'e / y'M0y) * Adj. R2 = 1-[(n-1)/(n-k)](1-R2) * R2Xz = R2X ( 1- R2X ) r*2yz
distribution of variation in dependent variable (ANOVA) || Econometrics || U/Grad || MPhil || PhD
มุมมอง 127ปีที่แล้ว
* Distribution of variation in the dependent variable across the independent variables and the residual term. [ANOVA] * y'M0y = b'X'M0Xb e'e (n-1) (k-1) (n-k) (SST) = (SSR) (SSE)
Δ in the sum of the square of error if we add a variable || Econometrics || U/Grad || MPhil || PhD
มุมมอง 145ปีที่แล้ว
* Change in the sum of the square of errors when a variable is added *u'u = e'e - c2 z*'z* = e'e - r*2yz y*'y* = e'e - r*2yz e*'e* = e'e (1 - r*2yz ) = e'e- [(z*'y*)2/z*'z*]
relation between partial correlation coefficient, b and t || Econometrics || U/Grad || MPhil || PhD
มุมมอง 126ปีที่แล้ว
* Relation between partial correlation coefficient, beta, and t-score. * r*2yz = t2z / [t2z (n-k-1) ] = c2 z*'z* / [c2 z*'z* u'u ] = r*2yz / [r*2yz (u'u/y*'y*) ] = (z*'y*)2/ [(z*'y*)2 (u'u * z*'z*]
(semi) partial correlation coefficients || Stata || Econometrics || U/Grad || MPhil || PhD
มุมมอง 102ปีที่แล้ว
* Correlation Coefficient ryz = cov(y,z) /√var(y) √var(z) * Partial Correlation Coefficient r*yz = cov(ey.x,ez.x) / √var(ey.x) √var(ez.x) * Semi-Partial Correlation Coeff semi r*yz = cov(y,ez.x) / √var(y) √var(ez.x)
inverse of a 2x2 partitioned matrix || Mata in Stata || Econometrics || U/Grad || MPhil || PhD
มุมมอง 139ปีที่แล้ว
* Inverse of a 2x2 partitioned Matrix A= [A11 , A12 \ A21 , A22] * F1 = [A11 - A12A22-1A21]-1 A-1 = [F1 , -F1A12A22-1 \ -A22-1A21F1 , A22-1(I A21F1A12A22-1)] * F2 = [A22 - A21A11-1A12]-1 A-1 = [A11-1(I A12F2A21A11-1) , -A11-1A12F2 \ -F2 A21 A11-1 , F2 ]
constant term in ols regression | Deviation from Mean Matrix | Econometrics | U/Grad | MPhil | PhD
มุมมอง 203ปีที่แล้ว
* Deviation from mean maker Matrix (M0) = [I - (ii'/i'i)] * Constant term in Regression X = [i X] b' = [b0 b'] y = ib0 Xb e b =(X'MiX)-1 (X'Miy) Mi = I - i(i'i)-1i' = M0 MiX = deviation from the mean of X Miy = deviation from the mean of y
ols estimate of a partitioned regression | Frisch-Waugh Theorem | Econometrics | U/Grad | MPhil| PhD
มุมมอง 1.6Kปีที่แล้ว
* Partitioned Regression y = X1b1 X2b2 e * b1 = (X1'M2X1)-1 X1'M2y ; b2 = (X2'M1X2)-1 X2'M1y * Frisch-Waugh Theorem * Individual Regression Coefficients c = (z*'z*)-1 (z*'y*) z*=Mz ; y* = My
fitted value (y^) as projection of y on col space of X | Matrix | Econometrics | U/Grad | MPhil| PhD
มุมมอง 153ปีที่แล้ว
* Fitted values (Xb) or y^ is the projection of y on col space of X * P = X(X'X)-1X' ; M=I-P Py = Xb^= y^; PX= X; Pe=0 My=e; MX=0; Me=e; PM=MP=0 * e'e = y'y - y^'y^ = e'y = y'e y^'y^ = y^'y = y'y^ * P and M are Idempotent Matrix * y = Py My = Xb e
ordinary least square (OLS) beta estimate derivation | Matrix | Econometrics | U/Grad | MPhil | PhD
มุมมอง 206ปีที่แล้ว
ordinary least square (OLS) beta estimate derivation | Matrix | Econometrics | U/Grad | MPhil | PhD
derivative of lin n qua expression involving matrix| product rule| Econometrics |U/Grad| MPhil| PhD
มุมมอง 113ปีที่แล้ว
derivative of lin n qua expression involving matrix| product rule| Econometrics |U/Grad| MPhil| PhD
matrix notation of linear regression equations | C Lin Reg Modl | Econometrics | U/Grad| MPhil| PhD
มุมมอง 284ปีที่แล้ว
matrix notation of linear regression equations | C Lin Reg Modl | Econometrics | U/Grad| MPhil| PhD
hypothesis testing theory | p-value | z and t test | Probability and Statistics | U/Grad| MPhil| PhD
มุมมอง 52ปีที่แล้ว
hypothesis testing theory | p-value | z and t test | Probability and Statistics | U/Grad| MPhil| PhD
CI of (µ1-µ2) when population variance is unknown | Probability and Statistics | U/Grad| MPhil | PhD
มุมมอง 42ปีที่แล้ว
CI of (µ1-µ2) when population variance is unknown | Probability and Statistics | U/Grad| MPhil | PhD
interval estimates of σ2 | parameter estimation | Probability and Statistics | U/Grad| MPhil | PhD
มุมมอง 73ปีที่แล้ว
interval estimates of σ2 | parameter estimation | Probability and Statistics | U/Grad| MPhil | PhD
interval estimates of mu (µ) | parameter estimation | Probability and Statistics| U/Grad| MPhil| PhD
มุมมอง 56ปีที่แล้ว
interval estimates of mu (µ) | parameter estimation | Probability and Statistics| U/Grad| MPhil| PhD
maximum likelihood estimation | parameter estimation | Probability and Statistics| U/Grad|MPhil| PhD
มุมมอง 76ปีที่แล้ว
maximum likelihood estimation | parameter estimation | Probability and Statistics| U/Grad|MPhil| PhD
distribution of sample variance | divide by n-1 | Probability and Statistics| U/Graduate|MPhil | PhD
มุมมอง 31ปีที่แล้ว
distribution of sample variance | divide by n-1 | Probability and Statistics| U/Graduate|MPhil | PhD
central limit theorem | distri of sample mean | Probability and Statistics | U/Graduate| MPhil | PhD
มุมมอง 68ปีที่แล้ว
central limit theorem | distri of sample mean | Probability and Statistics | U/Graduate| MPhil | PhD
mean and variance of sample mean (statistics) | Probability and Statistics | U/Graduate| MPhil | PhD
มุมมอง 98ปีที่แล้ว
mean and variance of sample mean (statistics) | Probability and Statistics | U/Graduate| MPhil | PhD
special random variable| Bernoulli RVs | Probability and Statistics | U/Graduate| MPhil | PhD
มุมมอง 93ปีที่แล้ว
special random variable| Bernoulli RVs | Probability and Statistics | U/Graduate| MPhil | PhD
moment generating function of random variable | Probability and Statistics | U/Graduate| MPhil | PhD
มุมมอง 98ปีที่แล้ว
moment generating function of random variable | Probability and Statistics | U/Graduate| MPhil | PhD
lin combins of RVs || var(Ax)= A var(x) A' || Probability and Statistics | U/Graduate || MPhil | PhD
มุมมอง 55ปีที่แล้ว
lin combins of RVs || var(Ax)= A var(x) A' || Probability and Statistics | U/Graduate || MPhil | PhD
correlation of random variables || Probability and Statistics || U/Graduate || MPhil || PhD
มุมมอง 108ปีที่แล้ว
correlation of random variables || Probability and Statistics || U/Graduate || MPhil || PhD
Hello I have a question. I have one string variable which have 4 characters the first two are the houres a the two second are the minutes How can I Split that variable into to variable one for the houres and other for the minutes?
Thank you for your question. I hope the following example clarifies your query: clear set obs 100 gen time = runiformint(1000, 2400) tostring time, replace gen hour = substr(time, 1, 2) // Extract the first two characters for the hour gen min = substr(time, 3, 2) // Extract the third and fourth characters for the minutes // Convert hour and min to numeric destring hour, replace destring min, replace
thank you for your amazing videos. I want to ask you how to make lapgap xlabel on twoway line graph? in your case, the label is "good", "better", and "north", while in my case, the xlabel is a continuous year. Thank you
Thank you for reaching out to us. Replace the labels "good", "better", and "north" in `relabel()' with the desired year values. For example, say: "" graph bar inc (max) exp, bargap(-30) /// over(prov,relabel(1 "2021" 2 "2022")) I hope this answers your question.
Dear @andrespurmalino9653 , Please find the content of the log file below: clear *** Input -- end input a b c d 12 13 14 14 12 234 13 132 123 132 153 134 45 23 123 2435 12 13 14 14 12 234 13 132 123 132 153 134 45 23 123 2435 12 13 14 14 12 234 13 132 123 132 153 134 45 23 123 2435 12 13 14 14 12 234 13 132 123 132 153 134 45 23 123 2435 end rename a inc rename b exp rename c inf rename d age *** gen clear set obs 30 gen a = 300 in f // f for first replace a = 1200 in 2/15 replace a = 5 in 16/20 replace a = 30 in l // l for last replace a = 700 if a==. rename a inc gen b = 900 *** Data Editor clear
thank you for your kind...
can we get the log file?
clear * Input -- end input a b c d 12 13 14 14 12 234 13 132 123 132 153 134 45 23 123 2435 12 13 14 14 12 234 13 132 123 132 153 134 45 23 123 2435 12 13 14 14 12 234 13 132 123 132 153 134 45 23 123 2435 12 13 14 14 12 234 13 132 123 132 153 134 45 23 123 2435 end rename a inc rename b exp rename c inf rename d age * gen clear set obs 30 gen a = 300 in f // f for first replace a = 1200 in 2/15 replace a = 5 in 16/20 replace a = 30 in l // l for last replace a = 700 if a==. rename a inc gen b = 900 * Data Editor clear
good thanks
Please remove the music in the background, it's annoying....😢😢😢
Thank you for your comment. The latest videos are free from any background music.
Very good job! Thanks
"Promo sm"
Very good summary of key graph commands in such short time. I was looking for this for quite awhile. Thank you.
dhanyabaad.
04:51 Events (AB) and (BA``) are mutually exclusive events so p(AB n BA``) = 0. p(AB U BA``) = p(AB) + p(BA``) - p(AB n BA``) = p(AB) + p(BA``) - 0 Therefore, p(AB U BA``) = p(AB) + p(BA``)
How to get variable labels, if there are multiple variable in tabstat command
cls clear set obs 3000 * creating categorical variables a, b, and c gen a = round(runiform()*1) gen b = round(runiform()*3) gen c = round(runiform()*4) * creating discrete or continuous variables d, e, and f gen d = round(runiform()*100) gen e = round(runiform()*500) gen f = round(runiform()*1000) * Labeling categorical variables a, b, and c label define a 0 "Male" 1 "Female" label values a a label define b 0 "Spring" 1 "Autumn" 2 "Winter" 3 "Summer" label values b b label define c 0 "Go" 1 "Sit" 2 "Stand" 3 "Relax" 4 "Move" label values c c ** operating tabstat command ## labels of categorical variable "a" is displayed tabstat d e f, by(a) statist(n mean sum p10 p50) /// col(stat) longstub ## labels of categorical variable "a" is displayed, across labels of categorical variable b bysort b : tabstat d e f, by(a) /// statist(n mean sum p10 p50) /// col(stat) longstub ## labels of categorical variable "a" is displayed, across labels of categorical variable b and c bysort b c : tabstat d e f, by(a) /// statist(n mean sum p10 p50) /// col(stat) longstub
@@EconometricsMelody thanks for reply, with this example, suppose variable d is weight, variable e is height. I want tabstat table to provide label of variables in table, i.e. weight and height to appear in table instead of d and e
@@varunmiglani110 Before you run the tabstat command, make sure you first label the variables, eg. label variable d "Weight" label variable e "height" Now you can run the tabstat command and get the desired results tabstat d e f, by(a) statist(n mean sum p10 p50) /// col(stat) longstub Look at this video th-cam.com/video/dz4CoQ7lWB4/w-d-xo.html for a detailed understanding of labeling the variables and values.
@@EconometricsMelody the problem still persists , tried these commands, still variable names are displayed in table and not label of variable
@@varunmiglani110 With reference to Stata version 15, unlike the commands "table" and "tabulate," tabstat does not display the label of a variable. However, you can manually edit the variable name when preparing the table from the tabstat outputs, or you can rename the variable without any spaces. Here are some suggested variable namings: 1) Rename variable "d" to "weight" and rename variable "e" to "height": rename d weight rename e height Then you can use tabstat with the renamed variables: tabstat weight height f, by(a) 2) Alternatively, if you want to rename the variables with longer names, you can use the following approach: rename d weight_of_the_children_under_18 rename e height_of_the_children_under_18 Then you can use tabstat with the renamed variables: tabstat weight_of_the_children_under_18 height_of_the_children_under_18, by(a) Both of these approaches will allow you to manually edit the variable name or use renamed variables to display meaningful labels in the tabstat output.
Good explanation
love this thank you so much for this. literally helped me to transform a set of variables into observations which i couldnt find anywhere else
The music behind the video is not needed
Great explanation. Thanks!
Hey my friend, please post a video on how to export regression tables from Stata to Excel, especially using the "outwrite" command (available from SSC). This would be great and extremely heplful, I guess that more people would also like to watch such video because your didactics is great! It could also be a separate playlist. Thank you!
Great video! Thank you
This is great. Saves so much time. Is it possible to share the do files
Great! Thanks for one more great class
Great video!!
AWESOME content! Thank you so much. Great teacher at engaging presentation speed. I had some trouble understanding some of the audio quality.
You saved me so much time, thank you very much sir
Thank you very much. This is super helpful.
Great work, thanks for producing this public good!
@2:35 gen c = a if a==b [This is the easiest way to generate a variable "c" with values equal to variables "a" and "b". We used forvalues loop only to reinforce our understanding of referencing nth value of any variable.]
Great job!
Sir how to write "doitnowok".
How to remove extra space from the column?
clear set obs 10 gen a = " do it now ok " split a, gen(m) gen b = m1+m2+m3+m4 order a b
clear set obs 10 gen a = " do it now ok " split a, gen(m) egen b = concat(m*) order a b
th-cam.com/video/np_ZY_JG_7g/w-d-xo.html
th-cam.com/video/atEHrLmPhKU/w-d-xo.html
thank u!!!
if we want to numeric variable and string variable can we do that? like in d variable instead of do we want to add any any variable with numeric value given in our data set ? example 23adf
clear set obs 12 gen a =23 gen b = "adf" gen c = string(a)+b
egen d = concat(a b), punct("")
?1,:.
Great tutorial, as always! Congrats again!!!!
Thank you very much. It means a lot to us.
Congratulations for these great classes. Thank you for producing these public goods!!! It would be great If you could launch classes about time series in Stata
This is very useful! Thank you so much
Congrats for your great job! Keep on doing that and thanks for producing a valuable public good!
Than you very much. It means a lot to us.
Very nice! Congrats!
Thank you very much
Congratulations! Very didactic, thank you for providing this public good! Keep on making the videos!
Thank you very much ...
Nice keep it up!!
Thank you very much Chitwambi Makungu
thank you so much
Thank you Beny smart
Really useful !! Thank you so much
Thank you very much Ghofran Ghofran
5:42 cross-sectional variable or data
1:15 the seed of mango should produce mango every time no matter where you sow it