This page provides links to selected example programs (see list below) and sample data sets for Basic Econometrics, by Damodar N. Gujarati, 4th Edition, (2003, McGraw-Hill).
The Zip file linked below includes all the sample programs, data files, and several useful procedures. If you want to actually run any of the examples, you should download the Zip file.
Below is a list of the available example programs. You can view (and save) any of these by simply clicking on the example name. The files are referenced by page number (e.g., DIEBP050.PRG refers to an example beginning on page 50). Again, if you actually want to run these, you should download the zip file listed above to get the required data and procedure files.
| GUJAP168.PRG | Regression through the origin |
| GUJAP172.PRG | Effects of rescaling variables |
| GUJAP177.PRG | Log-log regression |
| GUJAP180.PRG | Log vs linear trends |
| GUJAP182.PRG | Lin-log model, computing elasticities |
| GUJAP187.PRG | Phillips' Curve, estimating NAIRU |
| GUJAP220.PRG | Effects of transformations on R**2 |
| GUJAP223.PRG | Cobb-Douglas production function |
| GUJAP227.PRG | Polynomial regression |
| GUJAP269.PRG | Testing coefficient restrictions |
| GUJAP272.PRG | Testing exclusion restrictions |
| GUJAP275.PRG | Chow test, test for equal variances |
| GUJAP281.PRG | MacKinnon-White-Davidson (MWD) test for functional form |
| GUJAP291.PRG | Dummy variables |
| GUJAP309.PRG | Sample split dummies |
| GUJAP370.PRG | Longley data, multicollinearity |
| GUJAP404.PRG | Heteroscedasticity tests |
| GUJAP407.PRG | Spearman's Rank Correlation test |
| GUJAP409.PRG | Goldfeld-Quandt, Breusch-Pagan-Godfrey tests |
| GUJAP416.PRG | Weighted least squares |
| GUJAP422.PRG | Heteroscedasticity tests |
| GUJAP423.PRG | Heteroscedasticity tests |
| GUJAP462.PRG | Serial correlation tests |
| GUJAP475.PRG | Serial correlation induced by misspecification |
| GUJAP478.PRG | Serially correlated errors; estimation |
| GUJAP511.PRG | Effects of omitted variables |
| GUJAP519.PRG | Simple specification tests |
| GUJAP570.PRG | Non-linear least squares |
| GUJAP571.PRG | Non-linear least squares |
| GUJAP572.PRG | Non-linear least squares |
| GUJAP587.PRG | Linear probability model |
| GUJAP600.PRG | Grouped logit/probit |
| GUJAP604.PRG | Logit/probit |
| GUJAP641.PRG | Panel data: fixed/random effects |
| GUJAP669.PRG | Koyck distributed lag |
| GUJAP681.PRG | Stock adjustment model |
| GUJAP686.PRG | Dynamic regression model |
| GUJAP692.PRG | Polynomial distributed lag |
| GUJAP699.PRG | Granger causality tests |
| GUJAP767.PRG | Indirect least squares |
| GUJAP775.PRG | Two stage least squares |
| GUJAP779.PRG | Klein's Model I |
| GUJAP795.PRG | Graphs of macro data |
| GUJAP801.PRG | Examples of random walks |
| GUJAP804.PRG | Examples of non-stationary vs trend stationary |
| GUJAP809.PRG | Autocorrelations of non-stationary series |
| GUJAP811.PRG | Autocorrelations of non-stationary series |
| GUJAP816.PRG | Unit root tests |
| GUJAP823.PRG | Simple cointegration tests |
| GUJAP825.PRG | Error correction model |
| GUJAP826.PRG | Unit root tests |
| GUJAP827.PRG | Unit root tests |
| GUJAP828.PRG | Unit root tests |
| GUJAP829.PRG | Cointegration tests |
| GUJAP842.PRG | ARIMA model |
| GUJAP848.PRG | Vector autoregression |
[此贴子已经被作者于2005-2-6 14:57:53编辑过]
calendar 1960 1 12 allocate 1999:9
open data basics.wks data(format=wks,org=cols) / rate
graph(key=upleft) # rate
* Compute and graph autocorrelations: correlate(partial=rpcorrs,number=35) rate / rcorrs graph(key=below,style=bar,nodates,min=-1.0,max=1.0,number=1) 2 # rcorrs 2 36 # rpcorrs 2 36
* Now do same with first differences: diff rate / rdiff
Output
calendar 1960 1 12 allocate 1999:9
open data basics.wks data(format=wks,org=cols) / rate
graph(key=upleft) # rate
* Compute and graph autocorrelations: correlate(partial=rpcorrs,number=35) rate / rcorrs graph(key=below,style=bar,nodates,min=-1.0,max=1.0,number=1) 2 # rcorrs 2 36 # rpcorrs 2 36
* Now do same with first differences: diff rate / rdiff ## SR12. We recommend File-Clear Program or END xxx before ALLOCATE Correlations of Series RATE Monthly Data From 1960:01 To 1996:02 Autocorrelations 1: 0.98055431 0.95051044 0.92498083 0.90342788 0.88350434 0.86142382 7: 0.84639269 0.83887000 0.82737070 0.80777678 0.78555010 0.76355072 13: 0.74567072 0.72647154 0.70060134 0.67871385 0.65647312 0.63040020 19: 0.60039965 0.57116934 0.55121731 0.53758753 0.52416712 0.51106778 25: 0.49991597 0.48940033 0.47633568 0.45808047 0.43788511 0.41997145 31: 0.40399840 0.39009508 0.37722978 0.36677371 0.35817541
Partial Autocorrelations 1: 0.9805543 -0.2850007 0.1917198 -0.0024184 0.0221206 -0.0760231 7: 0.2521125 0.0412822 -0.1553906 -0.1035789 0.0350462 -0.0812726 13: 0.1119073 -0.0743396 -0.1632285 0.1274379 -0.1850215 -0.0635427 19: -0.0452570 0.1183718 0.0620811 0.0609881 0.0335901 0.0139284 25: 0.0149623 0.0443839 -0.0423606 0.0045004 -0.0248163 -0.0789235 31: 0.0475247 0.0205280 0.0118070 0.0077386 -0.0209824
[此贴子已经被作者于2005-2-15 10:00:43编辑过]
The Basics of WinRATS
This is the first textbook to teach introductory econometrics to finance majors. The text is data- and problem-driven, giving students the skills to estimate and interpret models, whilst having an intuitive grasp of the underlying theoretical concepts. The approach of Dr Brooks, based on the successful course he teaches at the ISMA Centre, one of Europe's leading finance schools, ensures that the text focuses squarely on the needs of finance students, including advice on planning and executing a project in empirical finance. The book assumes no prior knowledge of econometrics, and covers important modern topics such as time-series forecasting, volatility modelling, switching models and simulation methods. It includes detailed examples and case studies from the finance literature. Sample instructions and output from two popular and widely available computer packages (EViews and WinRATS) are presented as an integral part of the text. Extensive web-based supporting materials are available free of charge.
Contents
1. Introduction;
2. Econometric packages for modelling financial data;
3. A brief overview of the classical linear regression model;
4. Further issues with the classical linear regression model;
5. Univariate time series modelling and forecasting;
6. Multivariate modelling;
7. Modelling long-run relationships in finance;
8. Modelling volatility and correlation;
9. Modelling regime shifts;
10. Simulation methods;
11. Conducting empirical research in finance;
12. Conclusions: recent and future developments in the modelling of financial time series; References;
Appendix. Review of matrix algebra, calculus, and probability theory; Statistical tables
Reviews
‘‘New’ finance studies will like this book. It’s clear and easy to follow and the RATs code is integrated with the algebra and provides value added … the material is very applied and ‘hands on’ and it should have wide usage in the myriad of finance courses around.’ International Journal of Finance & Economics
[此贴子已经被作者于2005-2-15 10:25:33编辑过]
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