Data Sets and Examples

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
where DataSetName is one of the files in this table.

Data Set NameDescription
Data_CanadaCanadian inflation and interest rates, 1954–1994
Data_ConsumptionU.S. food consumption, 1927–1962
Data_CreditDefaultsInvestment-grade corporate bond defaults and four predictors, 1984–2004
Data_DanishDanish stock returns, bond yields, 1922–1999
Data_DieboldLiU.S. Treasury unsmoothed Fama-Bliss zero-coupon yields, 1972–2000
Data_ElectricityPricesSimulated daily electricity spot prices, 2010–2013
Data_EquityIdxU.S. equity indices, 1990–2001
Data_FXRatesCurrency exchange rates, 1979–1998
Data_GDPU.S. Gross Domestic Product, 1947–2005
Data_GlobalIdx1Global large-cap equity indices, 1993–2003
Data_GNPU.S. Gross National Product, 1947–2005
Data_Income1Simulated data on income and education
Data_Income2Average annual earnings by educational attainment in eight workforce age categories
Data_JAustralianJohansen's Australian data, 1972–1991
Data_JDanishJohansen's Danish data, 1974–1987
Data_MarkPoundDeutschmark/British Pound foreign-exchange rate, 1984–1991
Data_NelsonPlosserMacroeconomic series of Nelson and Plosser, 1860–1970
Data_RecessionsU.S. recession start and end dates, 1857–2011
Data_SchwertMacroMacroeconomic series of Schwert, 1947–1985
Data_SchwertStockIndices of U.S. stock prices, 1871–2008
Data_TBillThree-month U.S. treasury bill secondary market rates, 1947–2005
Data_USEconModelU.S. macroeconomic series, 1947–2009
Data_USEconVECModelU.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 a featured example, at the command line, enter

edit ExampleName
where ExampleName is the name of a featured example in this table.

Example NameDescription
Demo_ClassicalTestsPerforming classical model misspecification tests
Demo_DieboldLiModelUsing 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
Demo_RiskFHSUsing bootstrapping and filtered historical simulation to evaluate market risk
Demo_RiskEVTUsing extreme value theory and copulas to evaluate market risk
Demo_TSReg1Introducing basic assumptions behind multiple linear regression models
Demo_TSReg2Detecting correlation among predictors and accommodating problems of large estimator variance
Demo_TSReg3Detecting influential observations in time series data and accommodating their effect on multiple linear regression models
Demo_TSReg4Investigating trending variables, spurious regression, and methods of accommodation in multiple linear regression models
Demo_TSReg5Selecting a parsimonious set of predictors with high statistical significance for multiple linear regression models
Demo_TSReg6Evaluating model assumptions and investigating respecification opportunities by examining the series of residuals
Demo_TSReg7Presenting the basic setup for producing conditional and unconditional forecasts from multiple linear regression models
Demo_TSReg8Examining how lagged predictors affect least-squares estimation of multiple linear regression models
Demo_TSReg9Illustrating predictor history selection for multiple linear regression models
Demo_TSReg10Estimating multiple linear regression models of time series data in the presence of heteroscedastic or autocorrelated innovations
Demo_USEconModelModeling the U.S. economy using a VEC model as a linear alternative to the Smets-Wouters DSGE macroeconomic model
ModelAndSimulateElectricitySpotPricesUsingSkewNormalExampleSimulating 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.

Alternatively, you can simply run the example by entering ExampleName at the command line.

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