{smcl} {hline} {cmd:help: {helpb almon1}}{space 50} {cmd:dialog:} {bf:{dialog almon1}} {hline} {bf:{err:{dlgtab:Title}}} {bf: almon1: Shirley Almon Polynomial Distributed Lag Model} {marker 00}{bf:{err:{dlgtab:Table of Contents}}} {p 4 8 2} {p 5}{helpb almon1##01:Syntax}{p_end} {p 5}{helpb almon1##02:Description}{p_end} {p 5}{helpb almon1##03:Model}{p_end} {p 5}{helpb almon1##04:End Point Polynomial Restrictions Options}{p_end} {p 5}{helpb almon1##05:Other Options}{p_end} {p 5}{helpb almon1##06:Model Selection Diagnostic Criteria}{p_end} {p 5}{helpb almon1##07:Autocorrelation Tests}{p_end} {p 5}{helpb almon1##08:Heteroscedasticity Tests}{p_end} {p 5}{helpb almon1##09:Non Normality Tests}{p_end} {p 5}{helpb almon1##10:Saved Results}{p_end} {p 5}{helpb almon1##11:References}{p_end} {p 1}*** {helpb almon1##12:Examples}{p_end} {p 5}{helpb almon1##13:Author}{p_end} {marker 01}{bf:{err:{dlgtab:Syntax}}} {p 5 5 6} {cmd:almon1} {depvar} {it:{help varname:indepvar}} {ifin} , {err:[} {opt model(ols, als, gls, arch)}{p_end} {p 5 5 6} {opt lag(#)} {opt pdl(#)} {opt end:pr(0,1,2,3)} {opt ord:er(#)} {opt nocons:tant} {opt nol:ag} {opt mfx(lin, log)} {opt tolog} {opt wvar(name)} {p_end} {p 5 5 6} {opt iter:ate(#)} {opt tech:nique(name)} {opt diag dn test ominv} {opt pred:ict(new_var)} {opt res:id(new_var)} {opt l:evel(#)} {err:]}{p_end} {marker 02}{bf:{err:{dlgtab:Description}}} {pstd} {cmd:almon1} estimates Shirley Almon Polynomial Distributed Lag Model for many variables with the same lag order, endpoint restrictions, and polynomial degree order via (OLS - ALS - GLS - ARCH) Regression models. {pstd} {cmd:almon1} used Allen McDowell (2004, p183) for polynomial order with some modifications to allow inclusion or exclusion intercept, and also for estimation (ALS) and (ARCH) models. {pstd} {cmd:almon1} can compute: {p 2 5 5}1- Autocorrelation, Heteroscedasticity, and Non Normality Tests.{p_end} {p 2 5 5}2- Model Selection Diagnostic Criteria.{p_end} {p 2 5 5}3- Marginal effects and elasticities in both short and long run.{p_end} {p 2 5 5}4- Impact or short run multiplier, and long run or total distributed lag multiplier is the sum of lag coefficients for each variable. SUM(Coefs.) is called Long Run Dynamic Multiplier{p_end} {p 2 5 5}5- Mean lag gives a measure of the speed of adjustment,it has a useful interpretation if lag coefficients are all positive, so the full time period to response is (Full Lag = Mean Lag +1){p_end} {p 2 5 5}6- Joint-F or Joint Chi2-Test to test the null hypothesis that all coefficients associated with the distributed lag variable are simultaneously equal to zero.{p_end} {p 2 5 5}7- lag(#) order always starts from (0).{p_end} {p 2 5 5}8- If pdl(0) and endpr(0) then unrestricted lag model is estimated.{p_end} {p 2 5 5}9- Almon Polynomial Distributed Lag Model is useful in estimating supply response function for durable goods or perennial crops. {p 3 4 2} R2, R2 Adjusted, and F-Test, are obtained from 4 ways:{p_end} {p 5 4 2} 1- (Buse 1973) R2.{p_end} {p 5 4 2} 2- Raw Moments R2.{p_end} {p 5 4 2} 3- squared correlation between predicted (Yh) and observed dependent variable (Y).{p_end} {p 5 4 2} 4- Ratio of variance between predicted (Yh) and observed dependent variable (Y).{p_end} {p 5 4 2} - Adjusted R2: R2_a=1-(1-R2)*(N-1)/(N-K-1).{p_end} {p 5 4 2} - F-Test=R2/(1-R2)*(N-K-1)/(K).{p_end} {marker 03}{bf:{err:{dlgtab:Model}}} {synoptset 16}{...} {p2coldent:{it:model}}description{p_end} {synopt:{opt ols}}Ordinary Least Squares (OLS){p_end} {synopt:{opt als}}Autoregressive Least Squares (ALS){p_end} {synopt:{opt gls}}Generalized Least Squares (GLS){p_end} {synopt:{opt arch}}Autoregressive Conditional Heteroskedasticity (ARCH){p_end} {marker 04}{bf:{err:{dlgtab:End Point Restriction Options}}} {synoptset 16}{...} {p2coldent:{it:endpr Options}}Description{p_end} {col 3}{opt endp:pr(0)}{col 20}No Endpoint Polynomial Restrictions; the default {col 3}{opt endp:pr(1)}{col 20}Left Side Endpoint Polynomial Restrictions {col 3}{opt endp:pr(2)}{col 20}Right Side Endpoint Polynomial Restrictions {col 3}{opt endp:pr(3)}{col 20}Left & Right Side Endpoint Polynomial Restrictions {pstd} Lag Length {cmd:lag(#)} must be greater than Polynomial Degree {cmd:pdl(#)}} by at lesat (1) {pstd} Endpoint restrictions may be specified for any polynomial. The use of Endpoint restrictions increases the number of restrictions imposed in the model. {marker 05}{bf:{err:{dlgtab:Other Options}}} {synoptset 16}{...} {col 3}{opt lag(#)}{col 20}Lag Length Order of Polynomial Distributed Lag Model; default (3) {col 3}{opt pdl(#)}{col 20}Polynomial Degree Order (Pascal Triangle); default (2) {pstd} {almon1} estimates Almon polynomial distributed lag model at the same order with respect to: lag length, order of polynomial and Endpoint restriction on any independent variable in the model. {col 3}{opt nocons:tant}{col 20}Exclude Constant Term from Equation {col 3}{opt nol:ag}{col 20}Use Independent Variables in current period X(t), instead of lag X(t-1) {col 3}{opt pred:ict(new_var)}{col 20}Predicted values variable {col 3}{opt res:id(new_var)}{col 20}Residuals values variable {col 3}{opt dn}{col 20}Use (N) divisor instead of (N-K) for Degrees of Freedom (DF) in {cmd:(diag)} {col 3}{opt ord:er(#)}{col 20}Lag Order for (ALS) and (ARCH) Models; default (1) {col 3}{opt wvar(var)}{col 20}required variable name for Omega or Omega Inverse in {cmd:(GLS)} model {col 3}{opt ominv}{col 20}Use Omega Inverse instead of Omega in {cmd:(GLS)} model {col 3}{opt mfx(lin, log)}{col 20}functional form: Linear model {cmd:(lin)}, or Log-Log model {cmd:(log)}, {col 20}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). - {opt mfx(log)} and {opt tolog} options must be combined, to transform variables to log form. {col 3}{opt tolog}{col 20}Convert dependent and independent variables {col 20}to LOG Form in the memory for Log-Log regression. {col 20}{opt tolog} Transforms {depvar} and {indepvars} {col 20}to Log Form without lost the original data variables {col 3}{opt iter:ate(#)}{col 20}number of iterations; Default is iter(300) {col 3}{opt tech:nique(name)}{col 20}specifies how the likelihood function is to be maximized {col 3}{opt technique(nr)}{col 20}Newton-Raphson (NR) algorithm ; The default. {col 3}{opt technique(bhhh)}{col 20}Berndt-Hall-Hall-Hausman (BHHH) algorithm. {col 3}{opt technique(dfp)}{col 20} Davidon-Fletcher-Powell (DFP) algorithm. {col 3}{opt technique(bfgs)}{col 20} Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. {p2colreset}{...} {marker 06}{bf:{err:{dlgtab: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 {p2colreset}{...} {marker 07}{bf:{err:{dlgtab: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 {p2colreset}{...} {marker 08}{bf:{err:{dlgtab: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 {p2colreset}{...} {marker 09}{bf:{err:{dlgtab:Non Normality Tests}}} Ho: Normality - Ha: Non Normality - Jarque-Bera LM Test {marker 10}{bf:{err:{dlgtab:Saved Results}}} {pstd} {cmd:almon1} saves the following in {cmd:e()}: {err:*** Model Selection Diagnostic Criteria:} {col 4}{cmd:e(N)}{col 20}number of observations {col 4}{cmd:e(r2bu)}{col 20}R-squared (Buse 1973) {col 4}{cmd:e(r2bu_a)}{col 20}R-squared Adj (Buse 1973) {col 4}{cmd:e(r2raw)}{col 20}Raw Moments R2 {col 4}{cmd:e(r2raw_a)}{col 20}Raw Moments R2 Adj {col 4}{cmd:e(f)}{col 20}F-test {col 4}{cmd:e(fp)}{col 20}F-test P-Value {col 4}{cmd:e(wald)}{col 20}Wald-test {col 4}{cmd:e(waldp)}{col 20}Wald-test P-Value {col 4}{cmd:e(r2h)}{col 20}R2 Between Predicted (Yh) and Observed DepVar (Y) {col 4}{cmd:e(r2h_a)}{col 20}Adjusted r2h {col 4}{cmd:e(fh)}{col 20}F-test due to r2h {col 4}{cmd:e(fhp)}{col 20}F-test due to r2h P-Value {col 4}{cmd:e(r2v)}{col 20}R2 Variance Ratio Between Predicted (Yh) and Observed DepVar (Y) {col 4}{cmd:e(r2v_a)}{col 20}Adjusted r2v {col 4}{cmd:e(fv)}{col 20}F-test due to r2v {col 4}{cmd:e(fvp)}{col 20}F-test due to r2v P-Value {col 4}{cmd:e(sig)}{col 20}Root MSE (Sigma) {col 4}{cmd:e(llf)}{col 20}Log Likelihood Function{col 62}LLF {col 4}{cmd:e(aic)}{col 20}Akaike Information Criterion{col 62}(1974) AIC {col 4}{cmd:e(laic)}{col 20}Akaike Information Criterion{col 62}(1973) Log AIC {col 4}{cmd:e(sc)}{col 20}Schwarz Criterion{col 62}(1978) SC {col 4}{cmd:e(lsc)}{col 20}Schwarz Criterion{col 62}(1978) Log SC {col 4}{cmd:e(fpe)}{col 20}Amemiya Prediction Criterion{col 62}(1969) FPE {col 4}{cmd:e(hq)}{col 20}Hannan-Quinn Criterion{col 62}(1979) HQ {col 4}{cmd:e(rice)}{col 20}Rice Criterion{col 62}(1984) Rice {col 4}{cmd:e(shibata)}{col 20}Shibata Criterion{col 62}(1981) Shibata {col 4}{cmd:e(gcv)}{col 20}Craven-Wahba Generalized Cross Validation (1979) GCV {err:*** Autocorrelation Tests:} {col 4}{cmd:e(lmabgd#)}{col 20}Breusch-Godfrey LM Test (drop i obs) {col 4}{cmd:e(lmabgdp#)}{col 20}Breusch-Godfrey LM Test (drop i obs) P-Value {col 4}{cmd:e(lmabgk#)}{col 20}Breusch-Godfrey LM Test (keep i obs) {col 4}{cmd:e(lmabgkp#)}{col 20}Breusch-Godfrey LM Test (keep i obs) P-Value {col 4}{cmd:e(lmabpg#)}{col 20}Breusch-Pagan-Godfrey LM Test AR(i) {col 4}{cmd:e(lmabpgp#)}{col 20}Breusch-Pagan-Godfrey LM Test AR(i) P-Value {err:*** Heteroscedasticity Tests:} {col 4}{cmd:e(lmharch)}{col 20}Engle LM ARCH Test AR(i) {col 4}{cmd:e(lmharchp)}{col 20}Engle LM ARCH Test AR(i) P-Value {col 4}{cmd:e(lmhhp1)}{col 20}Hall-Pagan LM Test {col 4}{cmd:e(lmhhp1p)}{col 20}Hall-Pagan LM Test P-Value {col 4}{cmd:e(lmhhp2)}{col 20}Hall-Pagan LM Test {col 4}{cmd:e(lmhhp2p)}{col 20}Hall-Pagan LM Test P-Value {col 4}{cmd:e(lmhhp3)}{col 20}Hall-Pagan LM Test {col 4}{cmd:e(lmhhp3p)}{col 20}Hall-Pagan LM Test P-Value {err:*** Non Normality Tests:} {col 4}{cmd:e(lmnjb)}{col 20}Jarque-Bera LM Test {col 4}{cmd:e(lmnjbp)}{col 20}Jarque-Bera LM Test P-Value Matrixes {col 4}{cmd:e(b)}{col 20}coefficient vector {col 4}{cmd:e(V)}{col 20}variance-covariance matrix of the estimators {marker 11}{bf:{err:{dlgtab:References}}} {p 4 8 2}Shirley Montag Almon (1935–1975) {browse "http://en.wikipedia.org/wiki/Shirley_Montag_Almon"} {p 4 8 2} Almon, Shirley (1965) {cmd:The Distributed Lag Between Capital Appropriations and Expenditures,} {it:Econometrica, Vol. 33, No. 1, Jan.;} 178-196. {p 4 8 2} Almon, Shirley (1968) {cmd:Lags Between Investment Decisions and Their Causes,} {it:Rev. Econ. Stat., Vol. 50;} 193-206. {p 4 8 2}Breusch, Trevor (1978) {cmd: "Testing for Autocorrelation in Dynamic Linear Models",} {it:Aust. Econ. Papers, Vol. 17}; 334-355. {p 4 8 2}Breusch, Trevor & Adrian Pagan (1980) {cmd: "The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics",} {it:Review of Economic Studies 47}; 239-253. {p 4 8 2}C.M. Jarque & A.K. Bera (1987) {cmd: "A Test for Normality of Observations and Regression Residuals"} {it:International Statistical Review}, Vol. 55; 163-172. {p 4 8 2}Damodar Gujarati (2004) {cmd: "Basic Econometrics"} {it:4th Edition, McGraw Hill, New York, USA}; 687. {p 4 8 2}Damodar Gujarati & Dawn C. Porter (2009) {cmd: "Basic Econometrics"} {it:5th Edition, McGraw Hill, New York, USA}; 645. {p 4 8 2}Engle, Robert (1982) {cmd: "Autoregressive Conditional Heteroscedasticity with Estimates of Variance of United Kingdom Inflation"} {it:Econometrica, 50(4), July, 1982}; 987-1007. {p 4 8 2} Frost, P.A. (1975) {cmd:"Some Properties of the Almon Lag Technique When One Searches for Degree of Polynomial and Lag,} {it:J. Amer. Stat. Assoc., Vol. 70, March}; 606-612. {p 4 8 2}Godfrey, L. (1978) {cmd: "Testing for Higher Order Serial Correlation in Regression Equations when the Regressors Include Lagged Dependent Variables",} {it:Econometrica, Vol., 46}; 1303-1310. {p 4 8 2}Greene, William (2007) {cmd: "Econometric Analysis",} {it:6th ed., Upper Saddle River, NJ: Prentice-Hall}; 387-388. {p 4 8 2}Griffiths, W., R. Carter Hill & George Judge (1993) {cmd: "Learning and Practicing Econometrics",} {it:John Wiley & Sons, Inc., New York, USA}; 602-606. {p 4 8 2}Harvey, Andrew (1990) {cmd: "The Econometric Analysis of Time Series",} {it:2nd edition, MIT Press, Cambridge, Massachusetts}. {p 4 8 2}McDowell, Allen (2004) {cmd: "From the Help Desk: Polynomial Distributed Lag Models",} {it:Stata Journal, 4(2)}; 180–189. {p 4 8 2}Judge, Georege, R. Carter Hill, William . E. Griffiths, Helmut Lutkepohl, & Tsoung-Chao Lee (1988) {cmd: "Introduction To The Theory And Practice Of Econometrics",} {it:2nd ed., John Wiley & Sons, Inc., New York, USA}. {p 4 8 2}Judge, Georege, W. E. Griffiths, R. Carter Hill, Helmut Lutkepohl, & Tsoung-Chao Lee(1985) {cmd: "The Theory and Practice of Econometrics",} {it:2nd ed., John Wiley & Sons, Inc., New York, USA}; 615. {p 4 8 2}Kmenta, Jan (1986) {cmd: "Elements of Econometrics",} {it: 2nd ed., Macmillan Publishing Company, Inc., New York, USA}; 718. {p 4 8 2}Maddala, G. (1992) {cmd: "Introduction to Econometrics",} {it:2nd ed., Macmillan Publishing Company, New York, USA}; 358-366. {p 4 8 2} Shehata, Emad Abd Elmessih (1996) {cmd:"Supply Response for Some Field Crops",} {it:Cairo University - Faculty of Agriculture - Department of Economics - Egypt}. {p 4 8 2}William E. Griffiths, R. Carter Hill and George G. Judge (1993) {cmd: "Learning and Practicing Econometrics",} {it:John Wiley & Sons, Inc., New York, USA}; 687. {marker 12}{bf:{err:{dlgtab:Examples}}} {stata clear all} {stata db almon1} {stata sysuse almon1.dta , clear} {bf:{err:* (1) Ordinary Least Squares (OLS)}} {stata almon1 y x , model(ols) lag(3) pdl(2) mfx(lin)} {stata almon1 y x} {stata almon1 y x , model(ols) lag(3) pdl(0) end(0) mfx(lin)} {stata almon1 y x z , model(ols) lag(3) pdl(2) end(0) mfx(lin)} {stata almon1 y x z , model(ols) lag(4) pdl(3) end(0) mfx(lin)} {hline} {stata tsset t} {stata reg y l(0/3).x} {stata almon1 y x , model(ols) lag(3) pdl(0) end(0)} {hline} {stata almon1 y x , model(ols) lag(3) pdl(2) end(0)} {stata almon1 y x , model(ols) lag(3) pdl(2) end(1)} {stata almon1 y x , model(ols) lag(3) pdl(2) end(2)} {stata almon1 y x , model(ols) lag(3) pdl(2) end(3)} {stata almon1 y x , model(ols) lag(3) pdl(2) end(0) test mfx(lin)} {stata almon1 y x , model(ols) lag(3) pdl(2) end(0) test mfx(log) tolog} {stata almon1 y x , model(ols) lag(3) pdl(2) end(0) test mfx(lin) predict(Yh) resid(Ue)} {hline} {bf:{err:* (2) Autoregressive Least Squares (ALS)}} {stata almon1 y x , model(als) lag(3) pdl(2) end(0) test mfx(lin)} {stata almon1 y x , model(als) lag(3) pdl(2) end(0) test mfx(lin) order(1)} {stata almon1 y x , model(als) lag(3) pdl(2) end(0) test mfx(lin) order(2)} {hline} {bf:{err:* (3) Generalized Least Squares (GLS)}} {stata almon1 y x , model(gls) lag(3) pdl(2) wvar(x)} {stata almon1 y x , model(gls) lag(3) pdl(2) wvar(x) ominv} {hline} {bf:{err:* (4) Autoregressive Conditional Heteroskedasticity (ARCH)}} {stata almon1 y x , model(arch) lag(4) pdl(2) end(0) test mfx(lin)} {stata almon1 y x , model(arch) lag(4) pdl(2) end(0) test mfx(lin) order(1)} {stata almon1 y x , model(arch) lag(4) pdl(2) end(0) test mfx(lin) order(2)} {hline} * Example from Damodar [2009, p. 651]. {stata clear all} {stata sysuse almon2.dta , clear} {stata almon1 y x , model(ols) lag(3) pdl(2) end(0)} {hline} * Example from Griffiths, Hill and Judge [1993, p. 687]. {stata clear all} {stata sysuse almon3.dta , clear} {stata almon1 y x , model(ols) lag(8) pdl(2) end(0)} {hline} {stata clear all} {stata sysuse almon1.dta , clear} {stata almon1 y x , model(ols) lag(3) pdl(2) end(0) test mfx(lin) predict(Yh) resid(Ue)} . clear all . sysuse almon1.dta , clear . almon1 y x , model(ols) lag(3) pdl(2) end(0) test mfx(lin) predict(Yh) resid(Ue) ============================================================================== *** Shirley Almon Polynomial Distributed Lag Model *** ============================================================================== *** Ordinary Least Squares (OLS) *** *** No Endpoint Polynomial Restrictions *** ------------------------------------------------------------------------------ - Lag Length: Lag(3) - Polynomial Degree PDL(2) - Endpoint Restriction End(0) ------------------------------------------------------------------------------ Sample Size = 32 | Sample Range = 4 - 36 Wald Test = 1848.5961 | P-Value > Chi2(3) = 0.0000 F-Test = 616.1987 | P-Value > F(3 , 28) = 0.0000 R2 (R-Squared) = 0.9851 | Raw Moments R2 = 0.9999 R2a (Adjusted R2) = 0.9835 | Raw Moments R2 Adj. = 0.9999 Root MSE (Sigma) = 2.8448 | Log Likelihood Function = -76.7249 ------------------------------------------------------------------------------ - R2h= 0.9851 R2h Adj= 0.9835 F-Test = 616.20 P-Value > F(3 , 28) 0.0000 - R2v= 0.9851 R2v Adj= 0.9835 F-Test = 616.20 P-Value > F(3 , 28) 0.0000 ------------------------------------------------------------------------------ Akaike Criterion AIC = 161.4498 | Schwarz Criterion SC = 167.3128 ------------------------------------------------------------------------------ - Joint F-Test Restriction x = 616.199 P > F(3, 28) 0.0000 ------------------------------------------------------------------------------ y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | --. | 1.202501 .2993337 4.02 0.000 .5893435 1.815658 L1. | .6681351 .27373 2.44 0.021 .1074245 1.228846 L2. | .0904505 .2717045 0.33 0.742 -.4661109 .6470119 L3. | -.530553 .3083609 -1.72 0.096 -1.162202 .1010957 | _cons | 68.57335 6.809186 10.07 0.000 54.62537 82.52134 ------------------------------------------------------------------------------ ============================================================================== *** Model Selection Diagnostic Criteria ------------------------------------------------------------------------------ * Stata Method - Akaike Information Criterion (1974) AIC = 161.4498 - Schwarz Criterion (1978) SC = 167.3128 ------------------------------------------------------------------------- - Log Likelihood Function (LLF) = -76.7249 --------------------------------------------------------------------------- - Akaike Information Criterion (1974) AIC = 9.0923 - Akaike Information Criterion (1973) Log AIC = 2.2074 - Corrected Akaike Information Criterion AICC = 10.5738 --------------------------------------------------------------------------- - Schwarz Criterion (1978) SC = 10.9206 - Schwarz Criterion (1978) Log SC = 2.3906 --------------------------------------------------------------------------- - Final Prediction Criterion (1969) FPE = 9.1043 - Hannan-Quinn Criterion (1979) HQ = 9.6616 - Rice Criterion (1984) Rice = 9.4415 - Shibata Criterion (1981) Shibata = 8.8514 - Craven-Wahba Generalized Cross Validation (1979) GCV = 9.2488 ------------------------------------------------------------------------------ ============================================================================== *** Autocorrelation Tests - (Model= ols) ------------------------------------------------------------------------------ Ho: No Autocorrelation - Ha: Autocorrelation ------------------------------------------------------------------------------ - Rho Value for Order(1) AR(1)= 0.5062 - Breusch-Godfrey LM Test (drop 1 obs) AR(1)= 8.4650 P-Value >Chi2(1) 0.0036 - Breusch-Godfrey LM Test (keep 1 obs) AR(1)= 8.7436 P-Value >Chi2(1) 0.0031 - Breusch-Pagan-Godfrey LM Test AR(1)= 2.9570 P-Value >Chi2(1) 0.0855 ------------------------------------------------------------------------------ ============================================================================== *** Heteroscedasticity Tests - (Model= ols) ------------------------------------------------------------------------------ Ho: Homoscedasticity - Ha: Heteroscedasticity ------------------------------------------------------------------------------ - Engle LM ARCH Test AR(1) E2=E2_1-E2_1= 0.7476 P-Value > Chi2(1) 0.3872 ------------------------------------------------------------------------------ - Hall-Pagan LM Test: E2 = Yh = 0.1063 P-Value > Chi2(1) 0.7444 - Hall-Pagan LM Test: E2 = Yh2 = 0.0904 P-Value > Chi2(1) 0.7637 - Hall-Pagan LM Test: E2 = LYh2 = 0.1239 P-Value > Chi2(1) 0.7248 ------------------------------------------------------------------------------ - Harvey LM Test: LogE2 = X = 0.0362 P-Value > Chi2(2) 0.9821 - White Test -Koenker(R2): E2 = X = 0.0758 P-Value > Chi2(1) 0.7831 - White Test -B-P-G (SSR): E2 = X = 0.0647 P-Value > Chi2(1) 0.7993 ------------------------------------------------------------------------------ ============================================================================== *** Non Normality Tests - (Model= ols) ------------------------------------------------------------------------------ Ho: Normality - Ha: Non Normality ------------------------------------------------------------------------------ - Jarque-Bera LM Test = 0.2503 P-Value > Chi2(2) 0.8824 - White IM Test = 1.6090 P-Value > Chi2(2) 0.4473 - Geary LM Test = -3.0347 P-Value > Chi2(2) 0.2193 ------------------------------------------------------------------------------ *** Marginal Effect - Elasticity (Model= OLS): Linear * +----------------------------------------------------------------------------+ | Variable | Marginal Effect(B) | Elasticity(Es) | Mean | |-------------+--------------------+--------------------+--------------------| | x | 1.2025 | 0.6735 | 196.6850 | | L.x | 0.6681 | 0.3710 | 194.9896 | | L2.x | 0.0905 | 0.0498 | 193.2746 | | L3.x | -0.5306 | -0.2896 | 191.6763 | +----------------------------------------------------------------------------+ Mean of Dependent Variable = 351.1539 ------------------------------------------------------------------------------ * Variable Mean Lag Full Lag SUM(Coefs.) Std. Err. T-Test P>|t| ------------------------------------------------------------------------------ x -0.5191 0.4809 1.4305 0.0356 40.1586 0.0000 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ * Variable Marginal Effect (B) | Elasticity (Es) Short Run Long Run | Short Run Long Run ------------------------------------------------------------------------------ x 1.2025 1.4305 | 0.6735 0.8047 ------------------------------------------------------------------------------ {marker 13}{bf:{err:{dlgtab:Author}}} {hi:Emad Abd Elmessih Shehata} {hi:Professor (PhD Economics)} {hi:Agricultural Research Center - Agricultural Economics Research Institute - Egypt} {hi:Email: {browse "mailto:emadstat@hotmail.com":emadstat@hotmail.com}} {hi:WebPage at IDEAS:{col 27}{browse "http://ideas.repec.org/f/psh494.html"}} {hi:WebPage at EconPapers:{col 27}{browse "http://econpapers.repec.org/RAS/psh494.htm"}} {bf:{err:{dlgtab:ALMON1 Citation}}} {p 1}{cmd:Shehata, Emad Abd Elmessih (2014)}{p_end} {p 1 10 1}{cmd:ALMON1: "Stata Module to Estimate Shirley Almon Polynomial Distributed Lag Model"}{p_end} {title:Online Help:} {bf:*** Distributed Lag Models} {helpb almon1}{col 12}Shirley Almon Polynomial Distributed Lag Model{col 75}(ALMON1) {helpb almon}{col 12}Shirley Almon Generalized Polynomial Distributed Lag Model{col 75}(ALMON) {helpb dlagaj}{col 12}Alt France-Jan Tinbergen Distributed Lag Model{col 75}(DLAGAJ) {helpb dlagdj}{col 12}Dale Jorgenson Rational Distributed Lag Model{col 75}(DLAGDJ) {helpb dlagfd}{col 12}Frank De Leeuw Inverted V Distributed Lag Model{col 75}(DLAGFD) {helpb dlagif}{col 12}Irving Fisher Arithmetic Distributed Lag Model{col 75}(DLAGIF) {helpb dlagmf}{col 12}Milton Fridman Partial Adjustment-Adaptive Expectations {col 12}Distributed Lag Model{col 75}(DLAGMF) {helpb dlagmn}{col 12}Marc Nerlove Partial Adjustment Distributed Lag Model{col 75}(DLAGMN) {helpb dlagrs}{col 12}Robert Solow Pascal Triangle Distributed Lag Model{col 75}(DLAGRS) {helpb dlagrw}{col 12}Rudolf Wolffram Segmenting Partial Adjustment Distributed Lag{col 75}(DLAGRW) {helpb dlagtq}{col 12}Tweeten-Quance Partial Adjustment Distributed Lag Model{col 75}(DLAGTQ) {hline 83} {bf:*** Demand System Models} {helpb dsles}{col 12}Linear Expenditure System (LES){col 75}(DSLES) {helpb dseles}{col 12}Extended Linear Expenditure System (ELES){col 75}(DSELES) {helpb dsqes}{col 12}Quadratic Expenditure System (QES){col 75}(DSQES) {helpb dsrot}{col 12}Rotterdam Demand System{col 75}(DSROT) {helpb dsroti}{col 12}Inverse Rotterdam Demand System{col 75}(DSROTI) {helpb dsaidsla}{col 12}Linear Approximation Almost Ideal Demand System (AIDS-LA){col 75}(DSAIDSLA) {helpb dsaidsfd}{col 12}First Difference Almost Ideal Demand System (AIDS-FD){col 75}(DSAIDSFD) {helpb dsaidsi}{col 12}Inverse Almost Ideal Demand System(AIDS-I) {col 75}(DSAIDSI) {helpb dsarm}{col 12}Primal Armington Demand System{col 75}(DSARM) {helpb dsengel}{col 12}Engel Demand System{col 75}(DSENGEL) {helpb dsgads}{col 12}Generalized AddiLog Demand System (GADS){col 75}(DSGADS) {helpb dstlog}{col 12}Transcendental Logarithmic Demand System{col 75}(DSTLOG) {helpb dsw}{col 12}Working Demand System{col 75}(DSW) {hline 83} {helpb pfm}{col 12}Production Function Models{col 75}(PFM) {hline 83} {helpb ffm}{col 12}Profit Function Models{col 75}(FFM) {hline 83} {helpb cfm}{col 12}Cost Function Models{col 75}(CFM) {hline 83} {helpb iic}{col 12}Investment Indicators Criteria{col 75}(IIC) {hline 83} {helpb iot}{col 12}Leontief Input - Output Table{col 75}(IOT) {hline 83} {helpb index}{col 12}Index Numbers{col 75}(INDEX) {hline 83} {helpb mef}{col 12}Marketing Efficiency Models{col 75}(MEF) {hline 83} {helpb pam}{col 12}Policy Analysis Matrix{col 75}(PAM) {helpb pem}{col 12}Partial Equilibrium Model{col 75}(PEM) {hline 83} {bf:*** Financial Analysis Models} {helpb fam}{col 12}Financial Analysis Models{col 75}(FAM) {helpb xbcr}{col 12}Benefit-Cost Ratio{col 75}(XBCR) {helpb xirr}{col 12}Internal Rate of Return{col 75}(XIRR) {helpb xmirr}{col 12}Modified Internal Rate of Return{col 75}(XMIRR) {helpb xnfv}{col 12}Net Future Value{col 75}(XNFV) {helpb xnpv}{col 12}Net Present Value{col 75}(XNPV) {helpb xpp}{col 12}Payback Period{col 75}(XPP) {hline 83} {bf:*** Trade Models} {helpb wtm}{col 12}World Trade Models{col 75}(WTM) {helpb wtic}{col 12}World Trade Indicators Criteria{col 75}(WTIC) {helpb wtrgc}{col 12}World Trade Regional Geographical Concentration{col 75}(WTRGC) {helpb wtsgc}{col 12}World Trade Sectoral Geographical Concentration{col 75}(WTSGC) {helpb wtrca}{col 12}World Trade Revealed Comparative Advantage{col 75}(WTRCA) {hline 83} {psee} {p_end}