コード例 #1
0
ファイル: cond_moment.py プロジェクト: jstac/lae_test
def cm_test(X):
    """
    Conditional moment test.  X is a flat numpy array.
    """
    betahat, alphahat, shat = ar1_functions.fit(X)
    n = len(X)
    xL = X[:(n-1)]  #  All but the last one
    xF = X[1:]      #  All but the first one
    Z = (xF - betahat - alphahat * xL)**2 
    XX = sm.add_constant(xL)
    out = sm.OLS(Z, XX).fit()
    return np.abs(out.tvalues[0]) > 1.96
コード例 #2
0
ファイル: power.py プロジェクト: jstac/lae_test
def compute_power(alt_mod=None, ts_length=500, replications=1000, cv_reps=1000):
    """
    Computes the rejection frequency for a given null and alternative.
    """
    outcomes = []
    for m in range(replications):
        # Generate time series from alternative
        X = alt_mod.ts(ts_length)
        # Estimate parameters under the null
        betahat, alphahat, shat = ar1_functions.fit(X)
        cvs = v_test.compute_critical(betahat, alphahat, shat, 
                num_reps=cv_reps, ts_length=ts_length)
        T = v_test.test_stat(betahat, alphahat, shat, X)
        outcomes.append(T > cvs)
    return sum(outcomes) / replications
コード例 #3
0
ファイル: ar1_null.py プロジェクト: jstac/lae_test
 def compute_critical(self, beta, alpha, s, ep=True, num_reps=500, ts_length=264):
     """ 
     Compute the critical value of the test stat associated with parameters
     (beta, alpha, s) and AR1 null by Monte Carlo.  The flag ep determines
     whether or not the test is with estimated parameters.  (Note that the
     test statistic has a different asymptotic distribution when parameters
     are estimated---see the paper.)
     """
     # Step 1: Compute num_reps observations of the test statistic 
     T = np.empty(num_reps)        
     a1 = ar1_functions.AR1(beta=beta, alpha=alpha, s=s)
     data = a1.sim_data(num_reps, ts_length)  # Simulate under null
     for k in range(num_reps):
         if ep:
             betahat, alphahat, shat = ar1_functions.fit(data[k,:])
             T[k] = self.test_stat(betahat, alphahat, shat, data[k,:])
         else:
             T[k] = self.test_stat(beta, alpha, s, data[k,:])
     # Step 2: Compute and return (1 - size) quantile of test stat observations
     T.sort()
     return T[int((1 - self.test_size) * num_reps)]