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Data Sets and Examples

Econometrics Toolbox™ includes the sample data sets and examples in the following tables.

Data sets contain individual data variables, description variables with references, and dataset arrays encapsulating the data set and its description, as appropriate. To load a data set into the workspace, type

load Data_Filename,

where Data_Filename is one of the files listed in the table.

Data Set NameDescription
Data_CanadaCanadian inflation and interest rates, 1954–1994
Data_DanishDanish stock returns, bond yields, 1922–1999
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_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_USEconModelUS Macroeconomic series
Data_VARMA22Two-dimensional VARMA(2,2) specification
Data_CreditDefaultsInvestment-grade corporate bond defaults and four predictors, 1984–2004
Data_RecessionsU.S. recession start and end dates, 1857–2011

Example NameDescription
Demo_ClassicalTestsPerforming classical model misspecification tests
Demo_RiskFHSUsing bootstrapping and filtered historical simulation to evaluate market risk
Demo_RiskEVTUsing extreme value theory and copulas to evaluate market risk
Demo_HPFilterUsing the Hodrick-Prescott filter to reproduce their original result
Demo_USEconModelModeling the United States economy
Demo_TSReg1Introducing basic assumptions behind multiple linear regression models
Demo_TSReg2Detecting correlation among predictors and accommodate problems of large estimator variance
Demo_TSReg3Detecting influential observations in time series data and accommodate 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 investigate 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

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