SHAZAM Examples

Manual Examples: Jeffrey M. Wooldridge, 2009
Contains examples and data to accompany Introductory Econometrics: A Modern Approach 4e, Fourth Edition, South-Western College Publishing.

Manual Examples: Damodar Gujarati, Dawn Porter, 2008
Contains examples and data to accompany Basic Econometrics, Fifth Edition, Mcgraw Hill.

Manual Examples: R. Carter Hill, William E. Griffiths and Guay C. Lim, 2008.
Contains examples and data to accompany Principles of Econometrics, Third Edition, Wiley.

Manual Examples: R. Carter Hill, William E. Griffiths and George G. Judge, 2001.
Contains examples and data to accompany Undergraduate Econometrics, Second Edition, Wiley.

Manual Examples: William Greene, 2000
Contains examples and data to accompany Econometric Analysis, Fourth Edition, Prentice-Hall.

Chapter 3. Data Input and Output
rdata.sha	Reading character data with the `FORMAT` command (p. 36).
readchar.sha	Reading character data with the `CHARVARS=` option.
Chapter 4. Descriptive Statistics
anova.sha	A two-way ANOVA table (p. 51).
teststat.sha	t-test statistic for differences in population mean and an F-test statistic for different population variances (p. 52). Data file : urate.txt Data file description: urate.html
stemleaf.sha	Stem-and-Leaf Display
Chapter 5. Plots and Graphs
graph.sha	Graph of monthly time series data with dates on the x-axis. (p. 56). Data file : urate.txt Data file description: urate.html
Chapter 6. Generating Variables
log10.sha	Working with logarithms to the base 10 (pp. 65-66).
wreplace.sha	Sampling Without Replacement (p. 77).
Chapter 7. Ordinary Least Squares
homeown.sha	Weighted Least Squares - Analysis of Proportions Data
Chapter 8. Hypothesis Testing and Confidence Intervals
hyptest.sha	Linear and non-linear hypothesis tests (pp. 104-110).
confid.sha	Interval estimation for a population mean (pp. 112-113).
confid2.sha	Confidence ellipse for 2 regression coefficients from 2SLS estimation. (pp. 115-116). Also see system.sha. Data file : klein.txt
Chapter 9. Inequality Restrictions
bayes.sha	Linear regression with inequality restrictions (pp. 119-121).
sureq.sha	SURE with inequality restrictions (pp. 121-122).
Chapter 10. ARIMA Models
pacf.sha	Calculation of the partial autocorrelation function (pp. 127-128).
arima.sha	ARIMA estimation - an example from Enders.
arseas.sha	Seasonal ARIMA models - examples with the Box and Jenkins airline passenger data set and the Enders Spanish tourism data set.
Chapter 11. Autocorrelation Models
ar1.sha	Estimation and forecasting for a model with AR(1) errors. The commands show how to replicate the estimation results of the `AUTO` command by using OLS on transformed observations.
Chapter 13. Cointegration and Unit Root Tests
unitroot.sha	Tests for unit roots using the Perron test applied to the Nelson-Plosser data set. Data file : nelplos.txt Data file description: nelplos.html
johansen.prc	Johansen trace test procedure for cointegration (pp. 174-177). Command file : johan.sha Data file : macro.txt Data file description: macro.html
Chapter 14. Diagnostic Tests
diagnos.sha	Examples of programming test statistics in SHAZAM by illustrating some of the computations implemented by the `DIAGNOS` command. Calculations for tests for autocorrelation and tests for heteroskedasticity are shown (pp. 182-185).
recur.sha	More examples of programming test statistics. Calculations for recursive coefficient estimates and the Hansen test of model stability are shown (pp. 185-188).
reset.sha	More examples of programming test statistics. Calculations for RESET tests are shown (pp. 190-191).
Chapter 15. Distributed-Lag Models
dlag.sha	Estimation of distributed lag models including Almon lags (pp. 194-199).
gcause.sha	Testing for Granger causality (pp. 199-200). Data file : judge18.txt
Chapter 16. Forecasting
poolfc.sha	Forecasting with time-series cross-section data.
Chapter 17. Fuzzy Set Models
fuzzy.sha	Measuring the underground economy using the methodology of Giles and Draeseke (pp. 215-216).
Chapter 18. Generalized Entropy
gme.sha	Example of generalized entropy estimation (pp. 219-222).
Chapter 19. Generalized Least Squares
glsar1.sha	Example of generalized least squares estimation for the model with AR(1) errors (pp. 226-228).
Chapter 20. Heteroskedastic Models
hetreg.sha	Multiplicative Heteroskedasticity Data file : credit.txt
Chapter 21. Maximum Likelihood Estimation of Non-Normal Models
poisson.sha	Poisson regression.
mlebeta.sha	Models with Beta-Distributed Dependent Variables Data file : soss.txt
Chapter 22. Nonlinear Regression
nlces.sha	Estimation of a CES production function and testing for autocorrelated errors (p. 257).
maxfunc.sha	Maximizing a function of a single variable (p. 257).
nlsure.sha	Nonlinear seemingly unrelated regression applied to the estimation of a linear expenditure system (p. 258).
sysnl.sha	N2SLS, N3SLS and GMM estimation applied to Klein's Model I (pp. 260-268). Also see system.sha. Data file : klein.txt
Examples of the LOGDEN option:
mhet.sha	Estimation of the multiplicative heteroskedastic error model.
boxhet.sha	Maximum likelihood estimation of Box-Cox models with heteroskedasticity.
poisnl.sha	Poisson regression. Note that Poisson regression is implemented with the `MLE` command as shown in the command file poisson.sha.
tobithet.sha	Tobit model with heteroskedasticity. Data file : mroz.txt
homeown.sha	Analysis of Proportions Data
Chapter 23. Nonparametric Methods
nonpar.sha	Nonparametric regression of a nonlinear function (pp. 279-280).
semipar.sha	Robinson's semiparametric regression.
Chapter 24. Pooled Cross-Section Time-Series
pool.sha	Estimation methods available with the `POOL` command (pp. 289-292).
poolfc.sha	Forecasting with time-series cross-section data.
poolec.sha	Pooling with error components - an example of programming in SHAZAM.
Chapter 25. Probit and Logit Regression
logit.sha	Logit model estimation - comparisons with the probit model are also shown (pp. 300-301). Data file : school.txt
probit.sha	Probit model estimation and Heckit procedure (pp. 302-304). Data file : mroz.txt
logitw.sha	Weighted Logit estimation
Chapter 26. Robust Estimation
lad.sha	Least Absolute Error estimation. Calculation of bootstrap standard errors is also shown. Data file : industry.txt
Chapter 27. Time-Varying Linear Regression
fls.sha	Flexible least squares simulation experiment from Kalaba and Tesfatsion (pp. 313-314).
Chapter 28. Tobit Regression
tobit.sha	Tobit regression (pp. 321-322). Data file : judge19.txt
tobitm.sha	Calculating marginal effects for Tobit models including the McDonald and Moffitt (1980) decomposition. Data file : judge19.txt
Chapter 29. Two-Stage Least Squares and Systems of Equations
system.sha	Estimation of Klein's Model I by 2SLS and 3SLS (pp. 325-326 and pp. 335-337). Data file : klein.txt
hetcov.sha	Computation of heteroskedasticity-consistent standard errors for 2SLS estimation. Data file : klein.txt
Chapter 30. Data Smoothing, Moving Averages and Seasonal Adjustment
smooth.sha	Seasonal adjustment (p. 342).
expsmth.sha	Moving averages and exponential smoothing.
Chapter 31. Financial Time Series
stock.sha	Chart of stock market prices (pp. 349-350). Data file : spy.txt
portfol.sha	Portfolio selection problem (pp. 353-355). Data file : p.txt
eurocall.sha	Pricing European call options (pp. 358-359).
bsprice.sha	Black-Scholes formula for a call option price, put option price and implied volatility.
Chapter 32. Linear Programming
lp.sha	Linear Programming (pp. 362-364).
Chapter 33. Matrix Manipulation
matrix.sha	Matrix operations (p. 366).
matols.sha	OLS estimation with the `MATRIX` command (p. 370).
Chapter 34. Price Indexes
prindex.sha	Computing price indexes (p. 376).
Chapter 35. Principal Components and Factor Analysis
pcomp.sha	Example of multicollinearity diagnostics and principal components regression (pp. 381-382).
Chapter 36. Probability Distributions
pvalue.sha	Calculating p-values for test statistics (pp. 394-395).
distchi.sha	Calculating probabilities for chi-square (p. 395).
distf.sha	Calculating probabilities for non-central F (pp. 395-396).
Chapter 37. Sorting Data
wreplace.sha	Sampling Without Replacement (p. 77).
Chapter 40. Programming in SHAZAM
splice.sha	Splicing price index series (p. 417).
power.sha	Computing the power of a test (pp. 418-420).
ridge.sha	Ridge Regression. (pp. 420-422).
nlsroc.sha	Nonlinear least squares by the rank one correction method (pp. 426-427).
mcarlo.sha	Monte Carlo experiments (pp. 428-430).
boot.sha	Bootstrapping regression coefficients (pp. 430-432).
olscov.sha	Estimating the variance of the OLS estimator in the presence of heteroskedastic errors or autocorrelated errors (pp. 432-434).
hausman.sha	Hausman specification test for errors in variables (pp. 434-435).
nonnest.sha	Non-Nested model testing (pp. 435-438).
solve.sha	Solving nonlinear sets of equations (pp. 438-439).
mnlogit.sha	Multinomial logit estimation (pp. 440-442).
More Examples
window.sha	Performing a regression with a moving window.
cor.sha	Computing p-values for correlation coefficients.
archprog.sha	ARCH estimation using Engle's algorithm.
vif.sha	Computation of Variance Inflation Factors as an indicator of the severity of multicollinearity.
probelas.sha	Computing elasticities from probit estimation when variables have been log-transformed. Also see logit.sha. Data file : school.txt
bvprob.sha	Bivariate Probit models - Testing for zero error correlation by computing a Lagrange multiplier test statistic. Data file : school.txt
fiml.sha	Full information maximum likelihood - Klein Model I. Data file : klein.txt
Chapter 41. SHAZAM Procedures
sqrta.prc sqrtm.prc	Square root of a matrix (pp. 448-449) using: - an eigenvalue-vector decomposition - the Golub-Van Loan procedure Command file : sqrtm.sha
bs.prc impvol.prc	Black-Scholes option pricing and implied volatility (pp. 451-454). Command file : bsvol.sha Note that Black-Scholes option pricing is implemented with the `CALL` and `PUT` commands as shown in the command file bsprice.sha.
liml.prc	Limited information maximum likelihood (pp. 454-457). Command file : liml.sha Data file : klein.txt
multi.prc	Generating multivariate random numbers (pp. 457-459). Command file : multi.sha
More Procedures
dwpvalue.prc	Calculation of a p-value for the Durbin-Watson statistic Command file : dwpvalue.sha
gauss.prc	Nonlinear equation estimation by the Gauss-Newton method Command file : gauss.sha
granger.prc	Testing for Granger causality Command file : granger.sha Data file : judge18.txt Granger causality tests are also available using the commands shown in gcause.sha.
huf.prc	Robinson's heteroskedasticity of unknown form estimator Command file : huf.sha
qr.prc hh.prc	Solving OLS with the Householder transformation Command file : qr.sha
randcoef.prc	Random coefficients models - pooled time-series cross-section data. Command file : rand.sha
seasroot.prc	Tests for seasonal unit roots Command file : seas.sha Data file : gdpcan.txt
stest.prc	Stationarity tests proposed by Leybourne and McCabe Command file : stest.sha Data file : citibase.txt Data file description: citibase.html
ols.prc	Replication of the SHAZAM `OLS` command. Command file : ols.sha

A Note on using SHAZAM Procedures

SHAZAM command files that use SHAZAM procedures may need a revision to the FILE PROC command. This is required to ensure that the procedure file can be located. Further details are in the chapter SHAZAM PROCEDURES.

Run SHAZAM over the Internet

The SHAZAM examples can be run over the internet from the link on the SHAZAM homepage.

Data files can be loaded with the READ command:

READ (data/filename) variable_list / OPTIONS

where filename is the name of the data file and variable_list is the list of variable names.

SHAZAM procedures can be located by using:

FILE PROC procs/proc_name

where proc_name is the name of the procedure file.

Upper and lower case are not interchangeable for filenames.