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specification testing -凯发k8网页登录

identify the parametric form of a model

apps

econometric modeleranalyze and model econometric time series

functions

augmented dickey-fuller test
kpss test for stationarity
leybourne-mccabe stationarity test
phillips-perron test for one unit root
variance ratio test for random walk
paired integration and stationarity tests
sample autocorrelation
sample partial autocorrelation
sample cross-correlation
plot variable correlations
ljung-box q-test for residual autocorrelation
belsley collinearity diagnostics
block-wise granger causality and block exogeneity tests
engle test for residual heteroscedasticity
chow test for structural change
cusum test for structural change
recursive linear regression
belsley collinearity diagnostics
engle-granger cointegration test
johansen cointegration test
johansen constraint test

topics

stationarity


  • learn how to model a unit root process or test for one.

  • interactively assess whether a time series is a unit root process using statistical hypothesis tests.

  • conduct unit root tests on time series data.

  • check whether a linear time series is a unit root process.

correlation


  • interactively implement the box-jenkins methodology to select the appropriate number of lags for a univariate conditional mean model. then, fit the model to data and export the estimated model to the command line to generate forecasts.

  • apply box-jenkins methodology to select an arima model for the quarterly australian consumer price index.

  • interactively assess serial correlation for model specification or box-jenkins model selection by plotting the autocorrelation and partial autocorrelation functions (acf and pacf) and by conducting ljung-box q-tests.

  • estimate the acf and pacf, or conduct the ljung-box q-test.

  • autocorrelation and partial autocorrelation measure is the linear dependence of a variable with itself at two points in time.

  • the ljung-box q-test is a quantitative way to test for autocorrelation at multiple lags jointly.

heteroscedasticity


  • interactively assess whether a series has volatility clustering by inspecting correlograms of the squared residuals and by testing for significant arch lags.

  • test for autocorrelation in the squared residuals, or conduct engle’s arch test.

  • engle’s arch test is a lagrange multiplier test to assess the significance of arch effects.

structural change


  • check the model assumptions for a chow test.

  • estimate the power of a chow test using a monte carlo simulation.

collinearity


  • interactively assess the strengths and sources of collinearity among multiple series by using belsley collinearity diagnostics.

cointegration


  • interactively test series for cointegration by using the engle-granger cointegration test and the johansen cointegration test.

  • learn about cointegrated time series and error correction models.

  • the engle-granger test for cointegration and its limitations.

  • learn about the johansen test for cointegration.

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