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+-------+ ----+ Title +------------------------------------------------------------

meloreg2: Minimum Expected Loss (MELO) Instrumental Variables Regression

+-------------------+ ----+ Table of Contents +------------------------------------------------

Syntax Options Weight Options Weighted Variable Type Options Description Saved Results References

*** Examples

Author

+--------+ ----+ Syntax +-----------------------------------------------------------

meloreg2 depvar indepvars (endog = inst) [if] [in] , [ noconstant noconexog dn weights(type) wvar(varname) predict(new_var) resid(new_var) ]

+---------+ ----+ Options +----------------------------------------------------------

noconstant Exclude Constant Term from RHS Equation only

noconexog Exclude Constant Term from all Equations (both RHS and Instrumental Equations). Results of using noconexog option are identical to Stata ivregress and ivreg2. Including Constant Term in both RHS and Instrumental Equations is default in meloreg2

dn Use (N) divisor instead of (N-K) for Degrees of Freedom (DF)

predict(new_var) Predicted values variable

resid(new_var) Residuals values variable

+----------------+ ----+ Weight Options +---------------------------------------------------

wvar(varname) Weighted Variable Name

+--------------------------------+ ----+ Weighted Variable Type Options +-----------------------------------

Type Options Description

weights(yh) Yh - Predicted Value weights(yh2) Yh^2 - Predicted Value Squared weights(abse) abs(E) - Absolute Value of Residual weights(e2) E^2 - Residual Squared weights(le2) log(E^2) - Log Residual Squared weights(x) (x) Variable weights(xi) (1/x) Inverse Variable weights(x2) (x^2) Squared Variable weights(xi2) (1/x^2) Inverse Squared Variable

+-------------+ ----+ Description +------------------------------------------------------

meloreg2 estimate Minimum Expected Loss (MELO) Instrumental Variables Regression. meloreg2 dont deal with Missing values (.) in variables

+---------------+ ----+ Saved Results +----------------------------------------------------

meloreg2 saves the following in e():

e(f) F Test e(fp) F Test P-Value e(N) Number of obs e(r2c) R2 e(r2c_a) adj R2 e(r2cc) Corrected R2 e(r2cc_a) adj Corrected R2 e(r2u) Raw R2 e(r2u_a) Adj Raw R2 e(sig) Sigma (MSE) e(wald) Wald Test e(waldp) Wald Test P-Value

Matrixes e(b) coefficient vector e(V) variance-covariance matrix of the estimators

+------------+ ----+ References +-------------------------------------------------------

Judge, Georege, W. E. Griffiths, R. Carter Hill, Helmut Lutkepohl, & Tsoung-Chao Lee(1985) "The Theory and Practice of Econometrics", 2nd ed., John Wiley & Sons, Inc., New York, USA; 615.

Park, S. (1982) "Some Sampling Properties of Minimum Expected Loss (MELO) Estimators of Structural Coefficients", J. Econometrics, Vol. 18, No. 2, April,; 295-311.

Zellner, Arnold (1978) "Estimation of Functions of Population Means and Regression Coefficients Including Structural Coefficients: A Minimum Expected Loss (MELO) Approach", J. Econometrics, Vol. 8,; 127-158.

Zellner, Arnold & S. Park (1979) "Minimum Expected Loss (MELO) Estimators for Functions of Parameters and Structural Coefficients of Econometric Models", J. Am. Stat. Assoc., Vol. 74; 185-193.

+----------+ ----+ Examples +---------------------------------------------------------

clear all

sysuse meloreg2.dta , clear

db meloreg2

meloreg2 y1 x1 x2 (y2 = x1 x2 x3 x4)

ereturn list

tsset t

gen y1_1=L.y1

*** replace missing value in first observation with (0). replace y1_1= 0 if y1_1 == .

meloreg2 y1 y1_1 x1 x2 (y2 = y1_1 x1 x2 x3 x4)

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* Weighted MELO (WMELO)

meloreg2 y1 x1 x2 (y2 = x1 x2 x3 x4) , weights(yh)

meloreg2 y1 x1 x2 (y2 = x1 x2 x3 x4) , weights(yh2)

meloreg2 y1 x1 x2 (y2 = x1 x2 x3 x4) , weights(abse)

meloreg2 y1 x1 x2 (y2 = x1 x2 x3 x4) , weights(e2)

meloreg2 y1 x1 x2 (y2 = x1 x2 x3 x4) , weights(le2)

meloreg2 y1 x1 x2 (y2 = x1 x2 x3 x4) , weights(x) wvar(x1)

meloreg2 y1 x1 x2 (y2 = x1 x2 x3 x4) , weights(xi) wvar(x1)

meloreg2 y1 x1 x2 (y2 = x1 x2 x3 x4) , weights(x2) wvar(x1)

meloreg2 y1 x1 x2 (y2 = x1 x2 x3 x4) , weights(xi2) wvar(x1) -------------------------------------------------------------------------------

. clear all . sysuse meloreg2.dta , clear . meloreg2 y1 x1 x2 (y2 = x1 x2 x3 x4) ============================================================================== * Minimum Expected Loss (MELO) Instrumental Variables Regression) ============================================================================== y1 = y2 + x1 + x2 ------------------------------------------------------------------------------ K - Class (MELO) Value = 0.72727 Number of Obs = 17 Wald Test = 81.2132 P-Value > Chi2(3) = 0.0000 F Test = 27.0711 P-Value > F(3 , 13) = 0.0000 R-squared = 0.8616 Raw R2 = 0.9955 R-squared Adj = 0.8297 Raw R2 Adj = 0.9945 Root MSE (Sigma) = 10.1364 Log Likelihood Function = -61.2160 ------------------------------------------------------------------------------ y1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- y2 | .1951374 .2125644 0.92 0.375 -.2640801 .6543549 x1 | .3229772 .5276595 0.61 0.551 -.8169618 1.462916 x2 | -1.101973 .3260175 -3.38 0.005 -1.806291 -.3976546 _cons | 152.1958 58.82774 2.59 0.023 25.10624 279.2854 ------------------------------------------------------------------------------

+--------+ ----+ Author +-----------------------------------------------------------

Emad Abd Elmessih Shehata Assistant Professor Agricultural Research Center - Agricultural Economics Research Institute - Eg > ypt Email: emadstat@hotmail.com WebPage: http://emadstat.110mb.com/stata.htm WebPage at IDEAS: http://ideas.repec.org/f/psh494.html WebPage at EconPapers: http://econpapers.repec.org/RAS/psh494.htm

+-------------------+ ----+ meloreg2 Citation +------------------------------------------------

Shehata, Emad Abd Elmessih (2012) MELOREG2: "Stata Module to Estimate Minimum Expected Loss (MELO) Instrumental Variables Regression"

Online Help:

ivregress, diagreg2, meloreg2, (if installed)}.