Econometrics Toolbox™ includes the sample data sets and featured examples in the following tables.
Generally, the data sets contain individual data variables, description variables with references, and tables or timetables encapsulating the data set and its description, as appropriate. To load a data set into the workspace, at the command line, enter
load DataSetName
DataSetName| Data Set Name | Description |
|---|---|
Data_Canada | Canadian inflation and interest rates, 1954–1994 |
Data_Consumption | U.S. food consumption, 1927–1962 |
Data_CreditDefaults | Investment-grade corporate bond defaults and four predictors, 1984–2004 |
Data_Danish | Danish stock returns, bond yields, 1922–1999 |
Data_DieboldLi | U.S. Treasury unsmoothed Fama-Bliss zero-coupon yields, 1972–2000 |
Data_ElectricityPrices | Simulated daily electricity spot prices, 2010–2013 |
Data_EquityIdx | U.S. equity indices, 1990–2001 |
Data_FXRates | Currency exchange rates, 1979–1998 |
Data_GDP | U.S. Gross Domestic Product, 1947–2005 |
Data_GlobalIdx1 | Global large-cap equity indices, 1993–2003 |
Data_GNP | U.S. Gross National Product, 1947–2005 |
Data_Income1 | Simulated data on income and education |
Data_Income2 | Average annual earnings by educational attainment in eight workforce age categories |
Data_JAustralian | Johansen's Australian data, 1972–1991 |
Data_JDanish | Johansen's Danish data, 1974–1987 |
Data_MarkPound | Deutschmark/British Pound foreign-exchange rate, 1984–1991 |
Data_NelsonPlosser | Macroeconomic series of Nelson and Plosser, 1860–1970 |
Data_Recessions | U.S. recession start and end dates, 1857–2011 |
Data_SchwertMacro | Macroeconomic series of Schwert, 1947–1985 |
Data_SchwertStock | Indices of U.S. stock prices, 1871–2008 |
Data_TBill | Three-month U.S. treasury bill secondary market rates, 1947–2005 |
Data_USEconModel | U.S. macroeconomic series, 1947–2009 |
Data_USEconVECModel | U.S. macroeconomic series 1957–2016 and projections for the following 10 years from the Congressional Budget Office |
After loading the data set, you can display information about the data set, e.g., the meanings of the variables, by entering Description at the command line.
To open the script of an Econometrics Toolbox featured example, at the command line, enter
openExample('econ/ExampleName')
ExampleName is the name of a featured example in this table.| Example Name | Title | Description |
|---|---|---|
Demo_ClassicalTests | Classical Model Misspecification Tests | Performing classical model misspecification tests |
Demo_DieboldLiModel | Using the Kalman Filter to Estimate and Forecast the Diebold-Li Model | Using the State-Space Model (SSM) and Kalman filter to fit the Diebold-Li yields-only model to yield curves derived from government bond data |
Demo_HPFilter
| Using the Hodrick-Prescott Filter to Reproduce Their Original Result | Using the Hodrick-Prescott filter to reproduce their original result |
Demo_RiskFHS | Using Bootstrapping and Filtered Historical Simulation to Evaluate Market Risk | Using bootstrapping and filtered historical simulation to evaluate market risk |
Demo_RiskEVT | Using Extreme Value Theory and Copulas to Evaluate Market Risk | Using extreme value theory and copulas to evaluate market risk |
Demo_TSReg1 | Time Series Regression I: Linear Models | Introducing basic assumptions behind multiple linear regression models |
Demo_TSReg2 | Time Series Regression II: Collinearity and Estimator Variance | Detecting correlation among predictors and accommodating problems of large estimator variance |
Demo_TSReg3 | Time Series Regression III: Influential Observations | Detecting influential observations in time series data and accommodating their effect on multiple linear regression models |
Demo_TSReg4 | Time Series Regression IV: Spurious Regression | Investigating trending variables, spurious regression, and methods of accommodation in multiple linear regression models |
Demo_TSReg5 | Time Series Regression V: Predictor Selection | Selecting a parsimonious set of predictors with high statistical significance for multiple linear regression models |
Demo_TSReg6 | Time Series Regression VI: Residual Diagnostics | Evaluating model assumptions and investigating respecification opportunities by examining the series of residuals |
Demo_TSReg7 | Time Series Regression VII: Forecasting | Presenting the basic setup for producing conditional and unconditional forecasts from multiple linear regression models |
Demo_TSReg8 | Time Series Regression VIII: Lagged Variables and Estimator Bias | Examining how lagged predictors affect least-squares estimation of multiple linear regression models |
Demo_TSReg9 | Time Series Regression IX: Lag Order Selection | Illustrating predictor history selection for multiple linear regression models |
Demo_TSReg10 | Time Series Regression X: Generalized Least Squares and HAC Estimators | Estimating multiple linear regression models of time series data in the presence of heteroscedastic or autocorrelated innovations |
Demo_USEconModel | Modeling the United States Economy | Modeling the U.S. economy using a VEC model as a linear alternative to the Smets-Wouters DSGE macroeconomic model |
ModelAndSimulateElectricitySpotPricesUsingSkewNormalExample | Model and Simulate Electricity Spot Prices Using the Skew-Normal Distribution | Simulating the future behavior of electricity spot prices from a time series model fitted to historical data, and using the skew normal distribution to model the innovations process. |