help: dlagif dialog:

Title

dlagif: Irving Fisher Arithmetic Distributed Lag Model

Table of Contents

Syntax

Description

Model

GMM Options

Ridge Options

Weight Options

Weighted Variable Type Options

Other Options

Model Selection Diagnostic Criteria

Autocorrelation Tests

Heteroscedasticity Tests

Non Normality Tests

Saved Results

References

*** Examples

Author

Syntax

dlagif depvar indepvars [if] [in] , [ model(als, arch, bcox, gls, gmm, ols, qreg, rreg)

weights(yh|yh2|abse|e2|x|xi|x2|xi2) wvar(varname) techn(str) condition

ridge(orr|grr1|grr2|grr3) kr(#) hetcov(type) nolag list

lmauto lmhet lmnorm tune(#) diag dn tolog lad test

mfx(lin, log) noconstant lag(#) ar(#) hetcov(gmm_type)

predict(new_var) resid(new_var) twostep quantile(#) level(#) ]

Description

dlagif estimates Irving Fisher Distributed Lag Model via
(ALS - ARCH - Box-Cox - GLS - GMM - OLS - QREG - RREG - Ridge) Regression models.

dlagif can compute:

1- Autocorrelation, Heteroscedasticity, and Non Normality Tests.

2- Model Selection Diagnostic Criteria.

3- Marginal effects and elasticities in long run.

R2, R2 Adjusted, and F-Test, are obtained from 4 ways:

1- (Buse 1973) R2.

2- Raw Moments R2.

3- squared correlation between predicted (Yh) and observed dependent variable (Y).

4- Ratio of variance between predicted (Yh) and observed dependent variable (Y).

- Adjusted R2: R2_a=1-(1-R2)*(N-1)/(N-K-1).

- F-Test=R2/(1-R2)*(N-K-1)/(K).

Model

model description

als Autoregressive Least Squares (ALS)

arch Autoregressive Conditional Heteroskedasticity (ARCH)

bcox Box-Cox Regression Model (Box-Cox)

gls Generalized Least Squares (GLS)

gmm Generalized Method of Moments (GMM)

ols Ordinary Least Squares (OLS)

qreg Quantile Regression (QREG)

rreg Robust Regression (RREG)

GMM Options

hetcov Options Description

hetcov(white) White Method

hetcov(bart) Bartlett Method

hetcov(crag) Cragg (1983) Auxiliary Variables Method

hetcov(dan) Daniell Method

hetcov(hdun) Horn-Duncan (1975) Method

hetcov(hink) Hinkley (1977) Method

hetcov(jack) Jackknife Mackinnon-White (1985) Method

hetcov(nwest) Newey-West Method

hetcov(parzen) Parzen Method

hetcov(quad) Quadratic Spectral Method

hetcov(tent) Tent Method

hetcov(trunc) Truncated Method

hetcov(tukey) Tukey Method

hetcov(tukeym) Tukey-Hamming Method

hetcov(tukeyn) Tukey-Hanning Method

Ridge Options

kr(#) Ridge k value, must be in the range (0 < k < 1).

IF kr(0) in ridge(orr, grr1, grr2, grr3), the model will be an OLS regression.

ridge(orr) : Ordinary Ridge Regression [Judge,et al(1988,p.878) eq.21.4.2].
ridge(grr1): Generalized Ridge Regression [Judge,et al(1988,p.881) eq.21.4.12].
ridge(grr2): Iterative Generalized Ridge [Judge,et al(1988,p.881) eq.21.4.12].
ridge(grr3): Adaptive Generalized Ridge [Strawderman(1978)].

dlagif estimates Ordinary Ridge regression as a multicollinearity remediation method.

General form of Ridge Coefficients and Covariance Matrix are:

Br = inv[X'X + kI] X'Y

Cov=Sig^2 * inv[X'X + kI] (X'X) inv[X'X + kI]

where:
Br = Ridge Coefficients Vector (k x 1).
Cov = Ridge Covariance Matrix (k x k).
Y = Dependent Variable Vector (N x 1).
X = Independent Variables Matrix (N x k).
k = Ridge Value (0 < k < 1).
I = Diagonal Matrix of Cross Product Matrix (Xs'Xs).
Xs = Standardized Variables Matrix in Deviation from Mean.
Sig2 = (Y-X*Br)'(Y-X*Br)/DF

Weight Options

wvar(varname) Weighted Variable Name

Weighted Variable Type Options

weights 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(x) (x) Variable

weights(xi) (1/x) Inverse Variable

weights(x2) (x^2) Squared Variable

weights(xi2) (1/x^2) Inverse Squared Variable

Other Options

lamp( ) Transformation Type in model(bcox)

lamp(lhs) Power Transformations on (LHS) Left Hand Side Only; default

lamp(rhs) Power Transformations on (RHS) Right Hand Side Only

lamp(alls) Power Transformations on both (LHS) & (RHS) are the Same

lamp(alld) Power Transformations on both (LHS) & (RHS) are Different

lag(#) Lag Length Order of Arithmetic Distributed Lag Model; default (2)

ar(#) Lag Order for Model Tests; default (1)

condition use conditional MLE instead of full MLE with model(als)

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

quantile(#) Quantile Value

tune(#) use # as the biweight tuning constant;
default is tune(7) with model(qreg)

nolag Use Independent Variables in current period X(t), instead of lag X(t-1)

list Print Converted Variables according to Arithmetic Distributed Lag

twostep Two-Step estimation, stop after first iteration, same as iter(1)

iter(#) number of iterations; Default is iter(50)

tolerance(#) tolerance for coefficient vector; Default is tol(0.00001)

test Display ALL lma, lmh, lmn tests

noconstant Exclude Constant Term from Equation

predict(new_var)}Predicted values variable

resid(new_var)}Residuals values variable

mfx(lin, log) functional form: Linear model (lin), or Log-Log model (log),
to compute Marginal Effects and Elasticities
- In Linear model: marginal effects are the coefficients (Bm),
and elasticities are (Es = Bm X/Y).
- In Log-Log model: elasticities are the coefficients (Es),
and the marginal effects are (Bm = Es Y/X).
- mfx(log) and tolog options must be combined, to transform variables to log form.

tolog Convert dependent and independent variables
to LOG Form in the memory for Log-Log regression.
tolog Transforms depvar and indepvars
to Log Form without lost the original data variables

techn(name) Maximization of Log Likelihood Function (LLF) Technique Algorithm

techn(nr) Newton-Raphson (NR); default

techn(bhhh) Berndt-Hall-Hall-Hausman (BHHH)

techn(dfp) Davidon-Fletcher-Powell (DFP)

techn(bfgs) Broyden-Fletcher-Goldfarb-Shanno (BFGS)

Model Selection Diagnostic Criteria

* Model Selection Diagnostic Criteria
- Log Likelihood Function LLF
- Akaike Information Criterion (1974) AIC
- Akaike Information Criterion (1973) Log AIC
- Schwarz Criterion (1978) SC
- Schwarz Criterion (1978) Log SC
- Amemiya Prediction Criterion (1969) FPE
- Hannan-Quinn Criterion (1979) HQ
- Rice Criterion (1984) Rice
- Shibata Criterion (1981) Shibata
- Craven-Wahba Generalized Cross Validation (1979) GCV

Autocorrelation Tests

Ho: No Autocorrelation - Ha: Autocorrelation
- Breusch-Godfrey LM Test (drop 1 obs)
- Breusch-Godfrey LM Test (keep 1 obs)
- Breusch-Pagan-Godfrey LM Test

Heteroscedasticity Tests

Ho: Homoscedasticity - Ha: Heteroscedasticity
- Engle LM ARCH Test
- Hall-Pagan LM Test: E2 = Yh
- Hall-Pagan LM Test: E2 = Yh2
- Hall-Pagan LM Test: E2 = LYh2

Non Normality Tests

Ho: Normality - Ha: Non Normality
- Jarque-Bera LM Test
- White IM Test
- Geary LM Test

Saved Results

dlagif saves the following in e():

*** Model Selection Diagnostic Criteria:
e(N) number of observations
e(r2bu) R-squared (Buse 1973)
e(r2bu_a) R-squared Adj (Buse 1973)
e(r2raw) Raw Moments R2
e(r2raw_a) Raw Moments R2 Adj
e(f) F-test
e(fp) F-test P-Value
e(wald) Wald-test
e(waldp) Wald-test P-Value
e(r2h) R2 Between Predicted (Yh) and Observed DepVar (Y)
e(r2h_a) Adjusted r2h
e(fh) F-test due to r2h
e(fhp) F-test due to r2h P-Value
e(r2v) R2 Variance Ratio Between Predicted (Yh) and Observed DepVar (Y)
e(r2v_a) Adjusted r2v
e(fv) F-test due to r2v
e(fvp) F-test due to r2v P-Value
e(sig) Root MSE (Sigma)
e(llf) Log Likelihood Function LLF
e(aic) Akaike Information Criterion (1974) AIC
e(laic) Akaike Information Criterion (1973) Log AIC
e(sc) Schwarz Criterion (1978) SC
e(lsc) Schwarz Criterion (1978) Log SC
e(fpe) Amemiya Prediction Criterion (1969) FPE
e(hq) Hannan-Quinn Criterion (1979) HQ
e(rice) Rice Criterion (1984) Rice
e(shibata) Shibata Criterion (1981) Shibata
e(gcv) Craven-Wahba Generalized Cross Validation (1979) GCV

*** Autocorrelation Tests:
e(lmabgd#) Breusch-Godfrey LM Test (drop i obs)
e(lmabgdp#) Breusch-Godfrey LM Test (drop i obs) P-Value
e(lmabgk#) Breusch-Godfrey LM Test (keep i obs)
e(lmabgkp#) Breusch-Godfrey LM Test (keep i obs) P-Value
e(lmabpg#) Breusch-Pagan-Godfrey LM Test AR(i)
e(lmabpgp#) Breusch-Pagan-Godfrey LM Test AR(i) P-Value

*** Heteroscedasticity Tests:
e(lmharch) Engle LM ARCH Test AR(i)
e(lmharchp) Engle LM ARCH Test AR(i) P-Value
e(lmhhp1) Hall-Pagan LM Test
e(lmhhp1p) Hall-Pagan LM Test P-Value
e(lmhhp2) Hall-Pagan LM Test
e(lmhhp2p) Hall-Pagan LM Test P-Value
e(lmhhp3) Hall-Pagan LM Test
e(lmhhp3p) Hall-Pagan LM Test P-Value

*** Non Normality Tests:
e(lmnjb) Jarque-Bera LM Test
e(lmnjbp) Jarque-Bera LM Test P-Value
e(lmnw) White IM Test
e(lmnwp) White IM Test P-Value
e(lmng) Geary LM Test
e(lmngp) Geary LM Test P-Value

References

Breusch, Trevor (1978)
"Testing for Autocorrelation in Dynamic Linear Models",
Aust. Econ. Papers, Vol. 17; 334-355.

Breusch, Trevor & Adrian Pagan (1980)
"The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics",
Review of Economic Studies 47; 239-253.

C.M. Jarque & A.K. Bera (1987)
"A Test for Normality of Observations and Regression Residuals"
International Statistical Review, Vol. 55; 163-172.

Durbin, James (1970a)
"Testing for Serial Correlation in Least-Squares Regression When Some of the Regressors are Lagged Dependent Variables",
Econometrica, vol.38, no.3, May; 410-421.

Durbin, James (1970b)
"An Alternative to the Bounds Test for Testing for Serial Correlation in Least Square Regression",
Econometrica, Vol. 38, No. 2, May; 422-429.

Engle, Robert (1982)
"Autoregressive Conditional Heteroscedasticity with Estimates of Variance of United Kingdom Inflation"
Econometrica, 50(4), July, 1982; 987-1007.

Evagelia, Mitsaki (2011)
"Ridge Regression Analysis of Collinear Data",
http://www.stat-athens.aueb.gr/~jpan/diatrives/Mitsaki/chapter2.pdf

Geary R.C. (1947)
"Testing for Normality" Biometrika, Vol. 34; 209-242.

Geary R.C. (1970)
"Relative Efficiency of Count of Sign Changes for Assessing Residuals Autoregression in Least Squares Regression"
Biometrika, Vol. 57; 123-127.

Godfrey, L. (1978)
"Testing for Higher Order Serial Correlation in Regression Equations when the Regressors Include Lagged Dependent Variables",
Econometrica, Vol., 46; 1303-1310.

Greene, William (1993)
"Econometric Analysis",
2nd ed., Macmillan Publishing Company Inc., New York, USA; 616-618.

Greene, William (2007)
"Econometric Analysis",
6th ed., Upper Saddle River, NJ: Prentice-Hall; 387-388.

Griffiths, W., R. Carter Hill & George Judge (1993)
"Learning and Practicing Econometrics",
John Wiley & Sons, Inc., New York, USA; 602-606.

Harvey, Andrew (1990)
"The Econometric Analysis of Time Series",
2nd edition, MIT Press, Cambridge, Massachusetts.

Judge, Georege, R. Carter Hill, William . E. Griffiths, Helmut Lutkepohl, & Tsoung-Chao Lee (1988)
"Introduction To The Theory And Practice Of Econometrics",
2nd ed., John Wiley & Sons, Inc., New York, USA.

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.

Kmenta, Jan (1986)
"Elements of Econometrics",
2nd ed., Macmillan Publishing Company, Inc., New York, USA; 718.

Maddala, G. (1992)
"Introduction to Econometrics",
2nd ed., Macmillan Publishing Company, New York, USA; 358-366.

Marquardt D.W. (1970)
"Generalized Inverses, Ridge Regression, Biased Linear Estimation, and Nonlinear Estimation",
Technometrics, 12; 591-612.

Marquardt D.W. & R. Snee (1975)
"Ridge Regression in Practice",
The American Statistician, 29; 3-19.

Shehata, Emad Abd Elmessih (1996)
"Supply Response for Some Field Crops",
Cairo University - Faculty of Agriculture - Department of Economics - Egypt.

Strawderman, W. E. (1978)
"Minimax Adaptive Generalized Ridge Regression Estimators",
Journal American Statistical Association, 73; 623-627.

Theil, Henri (1971)
"Principles of Econometrics",
John Wiley & Sons, Inc., New York, USA.

White, Halbert (1980)
"A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity",
Econometrica, 48; 817-838.

William E. Griffiths, R. Carter Hill and George G. Judge (1993)
"Learning and Practicing Econometrics",
John Wiley & Sons, Inc., New York, USA.

Examples

clear all

sysuse dlagif.dta , clear

db dlagif

* (1) Ordinary Least Squares (OLS)
dlagif y x z , model(ols) mfx(lin) test
dlagif y x z , model(ols) mfx(log) test tolog
dlagif y x z , model(ols) lag(1) diag
dlagif y x z , model(ols) lag(2) diag
dlagif y x z , model(ols) lag(3) diag

* (2) Autoregressive Least Squares (ALS)
dlagif y x z , model(als) mfx(lin) test
dlagif y x z , model(als) mfx(lin) test ar(2)
dlagif y x z , model(als) mfx(log) test tolog
dlagif y x z , model(als) mfx(lin) test twostep

* (3) Autoregressive Conditional Heteroskedasticity (ARCH)
dlagif y x z , model(arch) mfx(lin) test
dlagif y x z , model(arch) mfx(lin) test ar(2)
dlagif y x z , model(arch) mfx(log) test tolog

* (4) Box-Cox Regression Model (Box-Cox)
dlagif y x z , model(bcox) mfx(lin) test

* (5) Generalized Least Squares (GLS)
dlagif y x z , model(gls) wvar(x) mfx(lin) test
dlagif y x z , model(gls) wvar(x) mfx(log) test tolog

* (6) Quantile Regression (QREG)
dlagif y x z , model(qreg) mfx(lin) test
dlagif y x z , model(qreg) mfx(log) test tolog

* (7) Robust Regression (RREG)
dlagif y x z , model(qreg) mfx(lin) test
dlagif y x z , model(qreg) mfx(log) test tolog

* (8) Generalized Method of Moments (GMM)
White: dlagif y x z , mfx(lin) test model(gmm) hetcov(white)
Bartlett: dlagif y x z , mfx(lin) test model(gmm) hetcov(bart)
Cragg: dlagif y x z , mfx(lin) test model(gmm) hetcov(crag)
Daniell: dlagif y x z , mfx(lin) test model(gmm) hetcov(dan)
Horn-Duncan: dlagif y x z , mfx(lin) test model(gmm) hetcov(hdun)
Hinkley: dlagif y x z , mfx(lin) test model(gmm) hetcov(hink)
Jackknife: dlagif y x z , mfx(lin) test model(gmm) hetcov(jack)
Newey-West: dlagif y x z , mfx(lin) test model(gmm) hetcov(nwest)
Parzen: dlagif y x z , mfx(lin) test model(gmm) hetcov(parzen)
Quadratic Spectral: dlagif y x z , mfx(lin) test model(gmm) hetcov(quad)
Tent: dlagif y x z , mfx(lin) test model(gmm) hetcov(tent)
Truncated: dlagif y x z , mfx(lin) test model(gmm) hetcov(trunc)
Tukey: dlagif y x z , mfx(lin) test model(gmm) hetcov(tukey)
Tukey-Hamming: dlagif y x z , mfx(lin) test model(gmm) hetcov(tukeym)
Tukey-Hanning: dlagif y x z , mfx(lin) test model(gmm) hetcov(tukeyn)

* (9) Weighted OLS & GMM Regression
dlagif y x z , mfx(lin) test model(ols) weights(yh)
dlagif y x z , mfx(lin) test model(ols) weights(yh2)
dlagif y x z , mfx(lin) test model(ols) weights(abse)
dlagif y x z , mfx(lin) test model(ols) weights(e2)
dlagif y x z , mfx(lin) test model(ols) weights(x) wvar(x)
dlagif y x z , mfx(lin) test model(ols) weights(xi) wvar(x)
dlagif y x z , mfx(lin) test model(ols) weights(x2) wvar(x)
dlagif y x z , mfx(lin) test model(ols) weights(xi2) wvar(x)

dlagif y x z , mfx(lin) test model(gmm) hetcov(white) weights(yh)
dlagif y x z , mfx(lin) test model(gmm) hetcov(white) weights(x) wvar(x)

* (10) Ridge Regression
dlagif y x z , mfx(lin) test model(ols) ridge(orr) kr(0.5)
dlagif y x z , mfx(lin) test model(ols) ridge(orr) kr(0.5) weights(x) wvar(x)
dlagif y x z , mfx(lin) test model(ols) ridge(grr1)
dlagif y x z , mfx(lin) test model(ols) ridge(grr2)
dlagif y x z , mfx(lin) test model(ols) ridge(grr3)

dlagif y x z , mfx(lin) test model(gmm) hetcov(white) ridge(orr) kr(0.5)
dlagif y x z , mfx(lin) test model(gmm) hetcov(white) ridge(orr) kr(0.5) weights(x) wvar(x)
dlagif y x z , mfx(lin) test model(gmm) hetcov(white) ridge(grr1)
dlagif y x z , mfx(lin) test model(gmm) hetcov(white) ridge(grr2)
dlagif y x z , mfx(lin) test model(gmm) hetcov(white) ridge(grr3)

. clear all
. sysuse dlagif.dta , clear
. dlagif y x z , model(ols) test mfx(lin)

==============================================================================
*** Irving Fisher Arithmetic Distributed Lag Model ***
==============================================================================
*** Ordinary Least Squares (OLS) ***

y = [+ 2*L1.x + 1*L2.x + 2*L1.z + 1*L2.z]/3

------------------------------------------------------------------------------
Sample Size = 33 | Sample Range = 4 - 36
Wald Test = 1024.1447 | P-Value > Chi2(2) = 0.0000
F-Test = 512.0724 | P-Value > F(2 , 30) = 0.0000
R2 (R-Squared) = 0.9715 | Raw Moments R2 = 0.9999
R2a (Adjusted R2) = 0.9696 | Raw Moments R2 Adj = 0.9999
Root MSE (Sigma) = 4.0297 | Log Likelihood Function = -91.2443
------------------------------------------------------------------------------
- R2h= 0.9715 R2h Adj= 0.9696 F-Test = 512.07 P-Value > F(2 , 30) 0.0000
- R2v= 0.9715 R2v Adj= 0.9696 F-Test = 512.07 P-Value > F(2 , 30) 0.0000
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x | 2.037683 .2810455 7.25 0.000 1.463712 2.611655
z | -1.043649 .5333626 -1.96 0.060 -2.132921 .045623
_cons | 70.79627 10.57861 6.69 0.000 49.19185 92.40068
------------------------------------------------------------------------------

==============================================================================
*** Model Selection Diagnostic Criteria - (Model= OLS)
==============================================================================
- Log Likelihood Function LLF = -91.2443
---------------------------------------------------------------------------
- Akaike Information Criterion (1974) AIC = 17.7059
- Akaike Information Criterion (1973) Log AIC = 2.8739
---------------------------------------------------------------------------
- Schwarz Criterion (1978) SC = 20.2863
- Schwarz Criterion (1978) Log SC = 3.0099
---------------------------------------------------------------------------
- Amemiya Prediction Criterion (1969) FPE = 17.7148
- Hannan-Quinn Criterion (1979) HQ = 18.5353
- Rice Criterion (1984) Rice = 18.0429
- Shibata Criterion (1981) Shibata = 17.4464
- Craven-Wahba Generalized Cross Validation (1979) GCV = 17.8625
------------------------------------------------------------------------------

==============================================================================
*** Autocorrelation Tests - (Model= OLS)
------------------------------------------------------------------------------
Ho: No Autocorrelation - Ha: Autocorrelation
------------------------------------------------------------------------------
- Rho Value for Order(1) AR(1)= 0.4993
- Breusch-Godfrey LM Test (drop 1 obs) AR(1)= 10.2208 P-Value >Chi2(1) 0.0014
- Breusch-Godfrey LM Test (keep 1 obs) AR(1)= 8.7690 P-Value >Chi2(1) 0.0031
- Breusch-Pagan-Godfrey LM Test AR(1)= 2.9613 P-Value >Chi2(1) 0.0853
------------------------------------------------------------------------------

==============================================================================
*** Heteroscedasticity Tests - (Model= OLS)
------------------------------------------------------------------------------
Ho: Homoscedasticity - Ha: Heteroscedasticity
------------------------------------------------------------------------------
- Engle LM ARCH Test AR(1) E2=E2_1-E2_1= 0.2307 P-Value > Chi2(1) 0.6310
------------------------------------------------------------------------------
- Hall-Pagan LM Test: E2 = Yh = 0.6349 P-Value > Chi2(1) 0.4256
- Hall-Pagan LM Test: E2 = Yh2 = 0.6864 P-Value > Chi2(1) 0.4074
- Hall-Pagan LM Test: E2 = LYh2 = 0.5835 P-Value > Chi2(1) 0.4449
------------------------------------------------------------------------------
- Harvey LM Test: LogE2 = X = 6.0460 P-Value > Chi2(2) 0.0487
- White Test -Koenker(R2): E2 = X = 1.0200 P-Value > Chi2(2) 0.6005
- White Test -B-P-G (SSR): E2 = X = 1.6479 P-Value > Chi2(2) 0.4387
------------------------------------------------------------------------------

==============================================================================
*** Non Normality Tests - (Model= OLS)
------------------------------------------------------------------------------
Ho: Normality - Ha: Non Normality
------------------------------------------------------------------------------
- Jarque-Bera LM Test = 5.3428 P-Value > Chi2(2) 0.0692
- White IM Test = 6.0750 P-Value > Chi2(2) 0.0480
- Geary LM Test = -3.0042 P-Value > Chi2(2) 0.2227
------------------------------------------------------------------------------

* Marginal Effect - Elasticity - Standardized Beta (Model= OLS): Linear *

+--------------------------------------------------------------------+
| Variable | Margin | Elasticity | St_Beta | Mean |
|----------------+------------+------------+------------+------------|
| x | 2.0377 | 1.1280 | 1.3406 | 193.6451 |
| z | -1.0436 | -0.3304 | -0.3618 | 110.7479 |
+--------------------------------------------------------------------+
Mean of Dependent Variable = 349.8016

Author

Emad Abd Elmessih Shehata
Professor (PhD Economics)
Agricultural Research Center - Agricultural Economics Research Institute - Egypt
Email: emadstat@hotmail.com
WebPage at IDEAS: http://ideas.repec.org/f/psh494.html
WebPage at EconPapers: http://econpapers.repec.org/RAS/psh494.htm

DLAGIF Citation

Shehata, Emad Abd Elmessih (2016)

DLAGIF: "Stata Module to Estimate Irving Fisher Arithmetic Distributed Lag Model"

Online Help:

*** Distributed Lag Models:
almon1 Shirley Almon Polynomial Distributed Lag Model (ALMON1)
almon Shirley Almon Generalized Polynomial Distributed Lag Model (ALMON)
dlagaj Alt France-Jan Tinbergen Distributed Lag Model (DLAGAJ)
dlagdj Dale Jorgenson Rational Distributed Lag Model (DLAGDJ)
dlagfd Frank De Leeuw Inverted V Distributed Lag Model (DLAGFD)
dlagif Irving Fisher Arithmetic Distributed Lag Model (DLAGIF)
dlagmf Milton Fridman Partial Adjustment-Adaptive Expectations
Distributed Lag Model (DLAGMF)
dlagmn Marc Nerlove Partial Adjustment Distributed Lag Model (DLAGMN)
dlagrs Robert Solow Pascal Triangle Distributed Lag Model (DLAGRS)
dlagrw Rudolf Wolffram Segmenting Partial Adjustment Distributed Lag (DLAGRW)
dlagtq Tweeten-Quance Partial Adjustment Distributed Lag Model (DLAGTQ)

*** Demand System Models:
dles Linear Expenditure System (LES) (DLES)
deles Extended Linear Expenditure System (ELES) (DELES)
dqes Quadratic Expenditure System (QES) (DQES)
drot Rotterdam Demand System (DROT)
droti Inverse Rotterdam Demand System (DROTI)
daidsla Linear Approximation Almost Ideal Demand System (AIDS-LA) (DAIDSLA)
daidsfd First Difference Almost Ideal Demand System (AIDS-FD) (DAIDSFD)
daidsi Inverse Almost Ideal Demand System(AIDS-I) (DAIDSI)
darmin Primal Armington Demand System (DARMIN)
dengel Engel Demand System (DENGEL)
dgads Generalized AddiLog Demand System (GADS) (DGADS)
dtlog Transcendental Logarithmic Demand System (DTLOG)
dwork Working Demand System (DWORK)

pfm Production Function Models (PFM)
pfmnl Non-Linear Production Function Models (PFMNL)

cfm Cost Function Models (CFM)
costreg Quadratic and Cubic Cost Functions (COSTREG)

ffm Profit Function Models (FFM)

iic Investment Indicators Criteria (IIC)

index Index Numbers (INDEX)

iot Leontief Input - Output Table (IOT)

mef Marketing Efficiency Models (MEF)

pam Policy Analysis Matrix (PAM)
pem Partial Equilibrium Model (PEM)

*** Financial Analysis Models:
fam Financial Analysis Models (FAM)
fbep Financial Break-Even Point Analysis (BEP) (FBEP)
fxbcr Benefit-Cost Ratio (BCR) (FXBCR)
fxirr Internal Rate of Return (IRR-XIRR) (FXIRR)
fxmirr Modified Internal Rate of Return (MIRR-XMIRR) (FXMIRR)
fxnfv Net Future Value (NFV-XNFV) (FXNFV)
fxnpv Net Present Value (NPV-XNPV) (FXNPV)
fxpp Payback Period (PP) (FXPP)

*** Black-Scholes European Option Pricing:
bsopm Black-Scholes European Option Pricing Model (BSOPM)
imvol Implied Volatility Black-Scholes European Option Pricing Model (IMVOL)

*** Trade Models:
wtm World Trade Models (WTM)
wtic World Trade Indicators Criteria (WTIC)
wtrca World Trade Revealed Comparative Advantage (WTRCA)
wtrgc World Trade Regional Geographical Concentration (WTRGC)
wtsgc World Trade Sectoral Geographical Concentration (WTSGC)

*** Forecasting Models:
arfimax Autoregressive Fractionally Integrated Moving Average Models (ARFIMAX)
arimax Autoregressive Integrated Moving Average Models (ARIMAX)
varx Vector Autoregressive Models (VARX)
vecx Vector Error Correction Models (VECX)

*** Spatial Econometrics Regression Models:
spregcs Spatial Cross Section Regression Econometric Models (SPREGCS)
spregxt Spatial Panel Regression Econometric Models (SPREGXT)