statsmodels.tsa.vector_ar.var_model.VARProcess class statsmodels.tsa.vector_ar.var_model.VARProcess(coefs, intercept, sigma_u, names=None) [source] Class represents a known VAR(p) process Parameters: coefs : ndarray (p x k x k) intercept : ndarray (length k) sigma_u : ndarray (k x k) names : sequence (length k) Returns: **Attributes:** : Methods acf([nlags]) Compute theoretical autocovariance function acorr([nlags]) Compute theoretical autocorrelation function forecast(y, steps) Produ

statsmodels.tsa.vector_ar.var_model.VAR class statsmodels.tsa.vector_ar.var_model.VAR(endog, dates=None, freq=None, missing='none') [source] Fit VAR(p) process and do lag order selection Parameters: endog : array-like 2-d endogenous response variable. The independent variable. dates : array-like must match number of rows of endog References Lutkepohl (2005) New Introduction to Multiple Time Series Analysis Methods fit([maxlags, method, ic, trend, verbose]) Fit the VAR model from_f

statsmodels.tsa.vector_ar.var_model.FEVD class statsmodels.tsa.vector_ar.var_model.FEVD(model, P=None, periods=None) [source] Compute and plot Forecast error variance decomposition and asymptotic standard errors Methods cov() Compute asymptotic standard errors plot([periods, figsize]) Plot graphical display of FEVD summary()

statsmodels.tsa.vector_ar.irf.IRAnalysis class statsmodels.tsa.vector_ar.irf.IRAnalysis(model, P=None, periods=10, order=None, svar=False) [source] Impulse response analysis class. Computes impulse responses, asymptotic standard errors, and produces relevant plots Parameters: model : VAR instance Notes Using Lutkepohl (2005) notation Methods G() H() cov([orth]) Compute asymptotic standard errors for impulse response coefficients cum_effect_cov([orth]) Compute asymptotic standard error

statsmodels.tsa.vector_ar.dynamic.DynamicVAR class statsmodels.tsa.vector_ar.dynamic.DynamicVAR(data, lag_order=1, window=None, window_type='expanding', trend='c', min_periods=None) [source] Estimates time-varying vector autoregression (VAR(p)) using equation-by-equation least squares Parameters: data : pandas.DataFrame lag_order : int, default 1 window : int window_type : {?expanding?, ?rolling?} min_periods : int or None Minimum number of observations to require in window, defaults to wi

statsmodels.tsa.varma_process.VarmaPoly class statsmodels.tsa.varma_process.VarmaPoly(ar, ma=None) [source] class to keep track of Varma polynomial format Examples ar23 = np.array([[[ 1. , 0. ], [ 0. , 1. ]], [[-0.6, 0. ], [ 0.2, -0.6]], [[-0.1, 0. ], [ 0.1, -0.1]]]) ma22 = np.array([[[ 1. , 0. ], [ 0. , 1. ]], [[ 0.4, 0. ], [ 0.2, 0.3]]]) Methods getisinvertible([a]) check whether the auto-regressive lag-polynomial is stationary getisstationary([a]) check whether the auto-regressive l

statsmodels.tsa.tsatools.lagmat2ds statsmodels.tsa.tsatools.lagmat2ds(x, maxlag0, maxlagex=None, dropex=0, trim='forward') [source] generate lagmatrix for 2d array, columns arranged by variables Parameters: x : array_like, 2d 2d data, observation in rows and variables in columns maxlag0 : int for first variable all lags from zero to maxlag are included maxlagex : None or int max lag for all other variables all lags from zero to maxlag are included dropex : int (default is 0) exclude

statsmodels.tsa.tsatools.lagmat statsmodels.tsa.tsatools.lagmat(x, maxlag, trim='forward', original='ex') [source] create 2d array of lags Parameters: x : array_like, 1d or 2d data; if 2d, observation in rows and variables in columns maxlag : int or sequence of ints all lags from zero to maxlag are included trim : str {?forward?, ?backward?, ?both?, ?none?} or None ?forward? : trim invalid observations in front ?backward? : trim invalid initial observations ?both? : trim invalid observ

statsmodels.tsa.tsatools.detrend statsmodels.tsa.tsatools.detrend(x, order=1, axis=0) [source] detrend an array with a trend of given order along axis 0 or 1 Parameters: x : array_like, 1d or 2d data, if 2d, then each row or column is independently detrended with the same trendorder, but independent trend estimates order : int specifies the polynomial order of the trend, zero is constant, one is linear trend, two is quadratic trend axis : int for detrending with order > 0, axis can

statsmodels.tsa.tsatools.add_trend statsmodels.tsa.tsatools.add_trend(X, trend='c', prepend=False, has_constant='skip') [source] Adds a trend and/or constant to an array. Parameters: X : array-like Original array of data. trend : str {?c?,?t?,?ct?,?ctt?} ?c? add constant only ?t? add trend only ?ct? add constant and linear trend ?ctt? add constant and linear and quadratic trend. prepend : bool If True, prepends the new data to the columns of X. has_constant : str {?raise?, ?add?, ?ski