ó áp7]c@sÕdZddlmZmZddlZddlmZddlm Z ddl m Z m Z ddl mZmZmZmZdd d gZd efd „ƒYZde fd „ƒYZd e fd „ƒYZdS(sY Multivariate Conditional and Unconditional Kernel Density Estimation with Mixed Data Types References ---------- [1] Racine, J., Li, Q. Nonparametric econometrics: theory and practice. Princeton University Press. (2007) [2] Racine, Jeff. "Nonparametric Econometrics: A Primer," Foundation and Trends in Econometrics: Vol 3: No 1, pp1-88. (2008) http://dx.doi.org/10.1561/0800000009 [3] Racine, J., Li, Q. "Nonparametric Estimation of Distributions with Categorical and Continuous Data." Working Paper. (2000) [4] Racine, J. Li, Q. "Kernel Estimation of Multivariate Conditional Distributions Annals of Economics and Finance 5, 211-235 (2004) [5] Liu, R., Yang, L. "Kernel estimation of multivariate cumulative distribution function." Journal of Nonparametric Statistics (2008) [6] Li, R., Ju, G. "Nonparametric Estimation of Multivariate CDF with Categorical and Continuous Data." Working Paper [7] Li, Q., Racine, J. "Cross-validated local linear nonparametric regression" Statistica Sinica 14(2004), pp. 485-512 [8] Racine, J.: "Consistent Significance Testing for Nonparametric Regression" Journal of Business & Economics Statistics [9] Racine, J., Hart, J., Li, Q., "Testing the Significance of Categorical Predictor Variables in Nonparametric Regression Models", 2006, Econometric Reviews 25, 523-544 iÿÿÿÿ(trangetnextN(toptimize(t mquantiles(tKDEMultivariatet KernelReg(tgpket LeaveOneOutt _get_type_post _adjust_shapetSingleIndexModelt SemiLineart TestFFormcBs,eZdZdd„Zd„Zd„ZRS(sW Nonparametric test for functional form. Parameters ---------- endog: list Dependent variable (training set) exog: list of array_like objects The independent (right-hand-side) variables bw: array_like, str Bandwidths for exog or specify method for bandwidth selection fform: function The functional form ``y = g(b, x)`` to be tested. Takes as inputs the RHS variables `exog` and the coefficients ``b`` (betas) and returns a fitted ``y_hat``. var_type: str The type of the independent `exog` variables: - c: continuous - o: ordered - u: unordered estimator: function Must return the estimated coefficients b (betas). Takes as inputs ``(endog, exog)``. E.g. least square estimator:: lambda (x,y): np.dot(np.pinv(np.dot(x.T, x)), np.dot(x.T, y)) References ---------- See Racine, J.: "Consistent Significance Testing for Nonparametric Regression" Journal of Business & Economics Statistics. See chapter 12 in [1] pp. 355-357. idcCsg||_||_||_||_||_||_t|d|d|ƒj|_|jƒ|_ dS(Ntbwtvar_type( tendogtexogRtfformt estimatortnbootRR t _compute_sigtsig(tselfRRR RRRR((sNlib/python2.7/site-packages/statsmodels/sandbox/nonparametric/kernel_extras.pyt__init__Rs      cCsø|j}|j}|j||ƒ}|j||ƒ}tj|ƒd}||}|tj|ƒ}|j|ƒ|_tj dƒ}d|d}d|d} ||} | |} | |} tj |j dfƒ} x¦t |j ƒD]•}| j ƒ}tjjddd|fƒ}|| k}| |||<||}|j||ƒ}|j||ƒ}||}|j|ƒ| |eZdZd„Zd„Zd„Zdd„Zd„ZRS(s¿ Single index semiparametric model ``y = g(X * b) + e``. Parameters ---------- endog: array_like The dependent variable exog: array_like The independent variable(s) var_type: str The type of variables in X: - c: continuous - o: ordered - u: unordered Attributes ---------- b: array_like The linear coefficients b (betas) bw: array_like Bandwidths Methods ------- fit(): Computes the fitted values ``E[Y|X] = g(X * b)`` and the marginal effects ``dY/dX``. References ---------- See chapter on semiparametric models in [1] Notes ----- This model resembles the binary choice models. The user knows that X and b interact linearly, but ``g(X * b)`` is unknown. In the parametric binary choice models the user usually assumes some distribution of g() such as normal or logistic. cCsœ||_t|ƒ|_|jd|_t|dƒ|_t||jƒ|_tj|jƒd|_|j|_ |j |_ |j ƒ\|_ |_dS(Nii(RtlenRMR RRRRtnobst data_typet_est_loc_lineartfunct _est_b_bwR'R (RRRR((sNlib/python2.7/site-packages/statsmodels/sandbox/nonparametric/kernel_extras.pyRÆs   cCsptjjd|jdfƒ}tj|j|ddƒ}|d|j!}||j}|j|ƒ}||fS(NRitdispi(RR"R#RMRtfmintcv_loot_set_bw_bounds(Rtparams0tb_bwR'R ((sNlib/python2.7/site-packages/statsmodels/sandbox/nonparametric/kernel_extras.pyRZÒs  c Cstj|ƒ}|d|j!}||j}t|jƒ}t|jƒjƒ}d}x«t|ƒD]\}}t|ƒ} |j |d| dtj ||ƒdd…df dtj |j||d…dd…f|ƒ ƒd} ||j|| d7}qcW||j S(NiRRR;ii( RtasarrayRMRRRR>R?RRYtdotR=RV( RtparamsR'R tLOO_XtLOO_YtLRJRKR%tG((sNlib/python2.7/site-packages/statsmodels/sandbox/nonparametric/kernel_extras.pyR]Ús  57c Cs+|dkr|j}nt||jƒ}tj|ƒd}tj|fƒ}tj||jfƒ}x·t|ƒD]©}|j|j |j tj |j|j ƒdd…dfdtj |||d…dd…f|j ƒƒ}|d||RpRbR=R?RRY(RRcR'R RdRetLOO_ZtXbRftiiRKR%tZtXb_jtYxRgtlt((sNlib/python2.7/site-packages/statsmodels/sandbox/nonparametric/kernel_extras.pyR]Ts$ %  " !!c CsF|dkr|j}nt||jƒ}|dkrB|j}nt||jƒ}tj|ƒd}tj|fƒ}tj||jfƒ}|j tj ||j ƒdd…df}x|t |ƒD]n}|j |j||jd||dd…fƒ}|d||s "oo