Download
1 / 26
260 likes | 399 Vues
This guide provides an overview of empirical methods applicable in microeconomics, focusing on descriptive tools, regression analysis, and panel data. It details the command format used for operations, including variable creation and model specification for multiple regression types such as PROBIT and LOGIT. The guide also outlines data manipulation techniques, interaction terms, and nonlinearities, along with how to compute key statistics from estimators. It's designed for students and practitioners looking to enhance their skills in microeconomic data analysis.
E N D
Empirical Methods for Microeconomic Applications William Greene Department of Economics Stern School of Business
Contents of the Project File Open folder Name Sample size Variables
Generic Command Format Verb ; specification ; specification ; … $ Every command ends with $ Use as many lines as desired. Use spaces wherever desired. Capital or lower case – no matter. Example: Create ; x = z*y + log(Income) $ Example; PROBIT ; Lhs = doctor ; Rhs = one,X $
Important Commands: SAMPLE ; first - last $ • Sample ; 1 – 1000 $ • Sample ; All $ CREATE ; Variable = transformation $ • Create ; loginc = Log(income) $ • Create ; logincF = .5*loginc*female $ • Create ; … any algebraic transformation $
Name Conventions CREATE ; name = any result desired $ Name is the name of a new variable • No more than 8 characters in a name • The first character must be a letter or _ • May not contain -,+,*,/,^,%,!,@,#, etc. • May contain _
Model Command Model ; Lhs = dependent variable ; Rhs = list of independent variables $ • Regress ; Lhs = income ; Rhs = ONE,age,educ,female $ • ONE requests the constant term Models are REGRESS, PROBIT, POISSON, LOGIT, TOBIT, … and over 100 others. All have the same form.
“Submitting” Commands One line command • Place cursor on that line • Press “Go” button More than one line command and/ormore than one command. • Highlight all lines (like any text editor) • Press “Go” button
Describe the data DSTAT ; Rhs = * $ Kernel Estimator KERNEL ; Rhs = income $ or KERNEL ; If[female = 1 & married = 1] ; Rhs = income ; Title=Kernel Estimator for Married Women$
Compute a Regression Use the whole sample REGRESS ; Lhs = income ; Rhs = One,age educ,female $ Use part of the sample REGRESS ; If [ married = 1] ; Lhs = income ; Rhs = one,age,educ,female $ The constant term in the model
Interactions and Nonlinearities Regress ; Lhs = loginc ; Rhs = One,age,age^2,educ, female, female*educ $
Standard Three Window Operation Commands typed in editing window Project window shows variables Results appear in output window
Model Results Regress ; Lhs = income ; Rhs =One,age,educ,female ; Res = e $ (Regression with residuals saved) Produces results: Displayed results in output Variables added to data set Matrices Named Scalars
New Variable Regress;Lhs=income ;Rhs=One,age,educ,female ; Res = e $ ? We can now manipulate the new ? variable created by the regression. Namelist ; z = married,hhkids $ Create;esq = e*e / (sumsqdev/nreg) – 1 $ Regress; Lhs = esq ; Rhs=One,z $ Calc ; List ; LMTstHet = nreg*Rsqrd $
Saved Matrices B=estimated coefficients and VARB=estimated asymptotic covariance matrix are saved by every model command. Different model estimators save other results as well. Here, we manipulate B and VARB to compute a restricted least squares estimator the hard way. REGRESS ; Lhs = income ; Rhs=One,age,educ,female $ NAMELIST ; X = One, age,educ,female $ MATRIX ; R = [0,1,1,1] ; q = [1] ; XXI=<X'X> ; m = R*B - q ; C=R*XXI*R' ; bstar = B - XXI*R'*<C>*m ; Vbstar=VARB - ssqrd*XXI*R'*<C>*R*XXI $ DISPLAY ; Parameters = bstar ; Labels = x ; Covariance = vbstar $
Saved Scalars Model estimates include named scalars. Linear regressions save numerous scalars. Others usually save 3 or 4, such as LOGL, and others. The program on the previous page used SSQRD saved by the regression. The LM test two pages back used NREG (the number of observations used), RSQRD (the R2 in the most recent regression), and sumsqdev.
