"""Score, lagrange multiplier and conditional moment tests
robust to misspecification or without specification of higher moments

Created on Thu Oct 30 00:42:38 2014

Author: Josef Perktold
License: BSD-3

Notes
-----

This module is a mixture of very general and very specific functions for
hypothesis testing in general models, targeted mainly to non-normal models.

Some of the options or versions of these tests are mainly intented for
cross-checking and to replicate different examples in references.

We need clean versions with good defaults for those functions that are
intended for the user.



References
----------

The following references are collected after my intitial implementation and is
most likely not exactly what I used.

The main articles on which the functions are directly based upon, are Boos 1992,
Tauchen 1985 and Whitney 1985a. Wooldrige artificial regression is
based on several articles and his text book.
Background reading are the textbooks by Cameron and Trivedi, Wooldridge and
Davidson and MacKinnon.
Newey and MacFadden 1994 provide some of the theoretical background.

Poisson dispersion tests are based on Dean 1992 and articles and text books by
Cameron and Trivedi.

The references currently do not include the literature on LM-test for
specification and diagnostic testing, like Pagan, Bera, Bera and Yoon and
many others, except those for the Poisson excess dispersion case and Pagan
and Vella.


Boos, Dennis D. 1992. “On Generalized Score Tests.” The American Statistician 46
(4): 327-33. https://doi.org/10.2307/2685328.

Breslow, Norman. 1989. “Score Tests in Overdispersed GLM`s.” In Statistical
Modelling, edited by Adriano Decarli, Brian J. Francis, Robert Gilchrist, and
Gilg U. H. Seeber, 64-74. Lecture Notes in Statistics 57. Springer New York.
http://link.springer.com/chapter/10.1007/978-1-4612-3680-1_8.

Breslow, Norman. 1990. “Tests of Hypotheses in Overdispersed Poisson Regression
and Other Quasi- Likelihood Models.” Journal of the American Statistical
Association 85 (410): 565-71. https://doi.org/10.2307/2289799.

Cameron, A. Colin, and Pravin K. Trivedi. 1986. “Econometric Models Based on
Count Data. Comparisons and Applications of Some Estimators and Tests.” Journal
of Applied Econometrics 1 (1): 29-53. https://doi.org/10.1002/jae.3950010104.

Cameron, A. Colin, and Pravin K. Trivedi. 1990a. “Conditional Moment Tests and
Orthogonal Polynomials.” Indiana University, Department of Economics, Working
Paper, 90-051.

Cameron, A. Colin, and Pravin K. Trivedi. 1990b. “Regression-Based Tests for
Overdispersion in the Poisson Model.” Journal of Econometrics 46 (3): 347-64.
https://doi.org/10.1016/0304-4076(90)90014-K.

Cameron, A. Colin, and Pravin K. Trivedi. Microeconometrics: methods and
applications. Cambridge university press, 2005.

Cameron, A. Colin, and Pravin K. Trivedi. Regression analysis of count data.
Vol. 53. Cambridge university press, 2013.

Davidson, Russell, and James G. MacKinnon. 1981. “Several Tests for Model
Specification in the Presence of Alternative Hypotheses.” Econometrica 49 (3):
781-93. https://doi.org/10.2307/1911522.

Davidson, Russell, and James G. MacKinnon. 1990. “Specification Tests Based on
Artificial Regressions.” Journal of the American Statistical Association 85
(409): 220-27. https://doi.org/10.2307/2289548.

Davidson, Russell, and James G. MacKinnon. 1991. “Artificial Regressions and C
(α) Tests.” Economics Letters 35 (2): 149-53.
https://doi.org/10.1016/0165-1765(91)90162-E.

Davidson, Russell, and James G. MacKinnon. Econometric theory and methods. Vol.
5. New York: Oxford University Press, 2004.

Dean, C. B. 1992. “Testing for Overdispersion in Poisson and Binomial Regression
Models.” Journal of the American Statistical Association 87 (418): 451-57.
https://doi.org/10.2307/2290276.

Dean, C., and J. F. Lawless. 1989. “Tests for Detecting Overdispersion in
Poisson Regression Models.” Journal of the American Statistical Association 84
(406): 467-72. https://doi.org/10.2307/2289931.

Newey, Whitney K. 1985a. “Generalized Method of Moments Specification Testing.”
Journal of Econometrics 29 (3): 229-56.
https://doi.org/10.1016/0304-4076(85)90154-X.

Newey, Whitney K. 1985b. “Maximum Likelihood Specification Testing and
Conditional Moment Tests.” Econometrica 53 (5): 1047-70.
https://doi.org/10.2307/1911011.

Newey, Whitney K. and Kenneth D. West. 1987. “Hypothesis Testing with Efficient
Method of Moments Estimation.” International Economic Review 28 (3): 777-87.
https://doi.org/10.2307/2526578.

Newey, Whitney K. and Daniel McFadden. 1994 "Large sample estimation and
hypothesis testing." Handbook of econometrics 4: 2111-2245.

Pagan, Adrian, and Frank Vella. 1989. “Diagnostic Tests for Models Based on
Individual Data: A Survey.” Journal of Applied Econometrics 4 (S1): S29-59.
https://doi.org/10.1002/jae.3950040504.

Tauchen, George. 1985. “Diagnostic Testing and Evaluation of Maximum Likelihood
Models.” Journal of Econometrics 30 (1-2): 415-43.
https://doi.org/10.1016/0304-4076(85)90149-6.

White, Halbert. 1981. “Consequences and Detection of Misspecified Nonlinear
Regression Models.” Journal of the American Statistical Association 76 (374):
419-33. https://doi.org/10.2307/2287845.

White, Halbert. 1983. “Maximum Likelihood Estimation of Misspecified Models.”
Econometrica 51 (2): 513. https://doi.org/10.2307/1912004.

White, Halbert. 1994. Estimation, Inference and Specification Analysis.
Cambridge: Cambridge University Press. https://doi.org/10.1017/CCOL0521252806.

Wooldridge, Jeffrey M. 1991. “Specification Testing and Quasi-Maximum-
Likelihood Estimation.” Journal of Econometrics 48 (1-2): 29-55.
https://doi.org/10.1016/0304-4076(91)90031-8.

Wooldridge, Jeffrey M. 1990. “A Unified Approach to Robust, Regression-Based
Specification Tests.” Econometric Theory 6 (1): 17-43.

Wooldridge, Jeffrey M. 1991a. “On the Application of Robust, Regression- Based
Diagnostics to Models of Conditional Means and Conditional Variances.” Journal
of Econometrics 47 (1): 5-46. https://doi.org/10.1016/0304-4076(91)90076-P.

Wooldridge, Jeffrey M. 1991b. “On the Application of Robust, Regression- Based
Diagnostics to Models of Conditional Means and Conditional Variances.” Journal
of Econometrics 47 (1): 5-46. https://doi.org/10.1016/0304-4076(91)90076-P.

Wooldridge, Jeffrey M. 1991c. “Specification Testing and Quasi-Maximum-
Likelihood Estimation.” Journal of Econometrics 48 (1-2): 29-55.
https://doi.org/10.1016/0304-4076(91)90031-8.

Wooldridge, Jeffrey M. 1994. “On the Limits of GLM for Specification Testing: A
Comment on Gurmu and Trivedi.” Econometric Theory 10 (2): 409-18.
https://doi.org/10.2307/3532875.

Wooldridge, Jeffrey M. 1997. “Quasi-Likelihood Methods for Count Data.” Handbook
of Applied Econometrics 2: 352-406.

Wooldridge, Jeffrey M. Econometric analysis of cross section and panel data. MIT
press, 2010.

"""

import warnings

import numpy as np
from scipy import stats

from statsmodels.regression.linear_model import OLS
from statsmodels.tools.decorators import cache_readonly

# deprecated dispersion functions, moved to discrete._diagnostic_count


def dispersion_poisson(results):
    """Score/LM type tests for Poisson variance assumptions

    .. deprecated:: 0.14

       dispersion_poisson moved to discrete._diagnostic_count

    Null Hypothesis is

    H0: var(y) = E(y) and assuming E(y) is correctly specified
    H1: var(y) ~= E(y)

    The tests are based on the constrained model, i.e. the Poisson model.
    The tests differ in their assumed alternatives, and in their maintained
    assumptions.

    Parameters
    ----------
    results : Poisson results instance
        This can be a results instance for either a discrete Poisson or a GLM
        with family Poisson.

    Returns
    -------
    res : ndarray, shape (7, 2)
       each row contains the test statistic and p-value for one of the 7 tests
       computed here.
    description : 2-D list of strings
       Each test has two strings a descriptive name and a string for the
       alternative hypothesis.
    """
    msg = (
        "dispersion_poisson here is deprecated, use the version in "
        "discrete._diagnostic_count"
    )
    warnings.warn(msg, FutureWarning, stacklevel=2)

    from statsmodels.discrete._diagnostics_count import test_poisson_dispersion

    return test_poisson_dispersion(results, _old=True)


def dispersion_poisson_generic(
    results,
    exog_new_test,
    exog_new_control=None,
    include_score=False,
    use_endog=True,
    cov_type="HC3",
    cov_kwds=None,
    use_t=False,
):
    """A variable addition test for the variance function

    .. deprecated:: 0.14

       dispersion_poisson_generic moved to discrete._diagnostic_count

    This uses an artificial regression to calculate a variant of an LM or
    generalized score test for the specification of the variance assumption
    in a Poisson model. The performed test is a Wald test on the coefficients
    of the `exog_new_test`.

    Warning: insufficiently tested, especially for options
    """
    msg = (
        "dispersion_poisson_generic here is deprecated, use the version in "
        "discrete._diagnostic_count"
    )
    warnings.warn(msg, FutureWarning, stacklevel=2)

    from statsmodels.discrete._diagnostics_count import (
        _test_poisson_dispersion_generic,
    )

    res_test = _test_poisson_dispersion_generic(
        results,
        exog_new_test,
        exog_new_control=exog_new_control,
        include_score=include_score,
        use_endog=use_endog,
        cov_type=cov_type,
        cov_kwds=cov_kwds,
        use_t=use_t,
    )
    return res_test


class ResultsGeneric:

    def __init__(self, **kwds):
        self.__dict__.update(kwds)


class TestResults(ResultsGeneric):

    def summary(self):
        txt = "Specification Test (LM, score)\n"
        stat = [self.c1, self.c2, self.c3]
        pval = [self.pval1, self.pval2, self.pval3]
        description = ["nonrobust", "dispersed", "HC"]

        for row in zip(description, stat, pval):
            txt += "%-12s  statistic = %6.4f  pvalue = %6.5f\n" % row

        txt += "\nAssumptions:\n"
        txt += "nonrobust: variance is correctly specified\n"
        txt += "dispersed: variance correctly specified up to scale factor\n"
        txt += "HC       : robust to any heteroscedasticity\n"
        txt += "test is not robust to correlation across observations"

        return txt


def lm_test_glm(result, exog_extra, mean_deriv=None):
    """score/lagrange multiplier test for GLM

    Wooldridge procedure for test of mean function in GLM

    Parameters
    ----------
    results : GLMResults instance
        results instance with the constrained model
    exog_extra : ndarray or None
        additional exogenous variables for variable addition test
        This can be set to None if mean_deriv is provided.
    mean_deriv : None or ndarray
        Extra moment condition that correspond to the partial derivative of
        a mean function with respect to some parameters.

    Returns
    -------
    test_results : Results instance
        The results instance has the following attributes which are score
        statistic and p-value for 3 versions of the score test.

        c1, pval1 : nonrobust score_test results
        c2, pval2 : score test results robust to over or under dispersion
        c3, pval3 : score test results fully robust to any heteroscedasticity

        The test results instance also has a simple summary method.

    Notes
    -----
    TODO: add `df` to results and make df detection more robust

    This implements the auxiliary regression procedure of Wooldridge,
    implemented based on the presentation in chapter 8 in Handbook of
    Applied Econometrics 2.

    References
    ----------
    Wooldridge, Jeffrey M. 1997. “Quasi-Likelihood Methods for Count Data.”
    Handbook of Applied Econometrics 2: 352-406.

    and other articles and text book by Wooldridge

    """

    if hasattr(result, "_result"):
        res = result._result
    else:
        res = result

    mod = result.model
    nobs = mod.endog.shape[0]

    # mean_func = mod.family.link.inverse
    dlinkinv = mod.family.link.inverse_deriv

    # derivative of mean function w.r.t. beta (linear params)
    def dm(x, linpred):
        return dlinkinv(linpred)[:, None] * x

    var_func = mod.family.variance

    x = result.model.exog
    x2 = exog_extra

    # test omitted
    try:
        lin_pred = res.predict(which="linear")
    except TypeError:
        # TODO: Standardized to which="linear" and remove linear kwarg
        lin_pred = res.predict(linear=True)
    dm_incl = dm(x, lin_pred)
    if x2 is not None:
        dm_excl = dm(x2, lin_pred)
        if mean_deriv is not None:
            # allow both and stack
            dm_excl = np.column_stack((dm_excl, mean_deriv))
    elif mean_deriv is not None:
        dm_excl = mean_deriv
    else:
        raise ValueError("either exog_extra or mean_deriv have to be provided")

    # TODO check for rank or redundant, note OLS calculates the rank
    k_constraint = dm_excl.shape[1]
    fittedvalues = res.predict()  # discrete has linpred instead of mean
    v = var_func(fittedvalues)
    std = np.sqrt(v)
    res_ols1 = OLS(
        res.resid_response / std, np.column_stack((dm_incl, dm_excl)) / std[:, None]
    ).fit()

    # case: nonrobust assumes variance implied by distribution is correct
    c1 = res_ols1.ess
    pval1 = stats.chi2.sf(c1, k_constraint)
    # print c1, stats.chi2.sf(c1, 2)

    # case: robust to dispersion
    c2 = nobs * res_ols1.rsquared
    pval2 = stats.chi2.sf(c2, k_constraint)
    # print c2, stats.chi2.sf(c2, 2)

    # case: robust to heteroscedasticity
    from statsmodels.stats.multivariate_tools import partial_project

    pp = partial_project(dm_excl / std[:, None], dm_incl / std[:, None])
    resid_p = res.resid_response / std
    res_ols3 = OLS(np.ones(nobs), pp.resid * resid_p[:, None]).fit()
    # c3 = nobs * res_ols3.rsquared   # this is Wooldridge
    c3b = res_ols3.ess  # simpler if endog is ones
    pval3 = stats.chi2.sf(c3b, k_constraint)

    tres = TestResults(c1=c1, pval1=pval1, c2=c2, pval2=pval2, c3=c3b, pval3=pval3)

    return tres


def cm_test_robust(resid, resid_deriv, instruments, weights=1):
    """score/lagrange multiplier of Wooldridge

    generic version of Wooldridge procedure for test of conditional moments

    Limitation: This version allows only for one unconditional moment
    restriction, i.e. resid is scalar for each observation.
    Another limitation is that it assumes independent observations, no
    correlation in residuals and weights cannot be replaced by cross-observation
    whitening.

    Parameters
    ----------
    resid : ndarray, (nobs, )
        conditional moment restriction, E(r | x, params) = 0
    resid_deriv : ndarray, (nobs, k_params)
        derivative of conditional moment restriction with respect to parameters
    instruments : ndarray, (nobs, k_instruments)
        indicator variables of Wooldridge, multiplies the conditional momen
        restriction
    weights : ndarray
        This is a weights function as used in WLS. The moment
        restrictions are multiplied by weights. This corresponds to the
        inverse of the variance in a heteroskedastic model.

    Returns
    -------
    test_results : Results instance
        ???  TODO

    Notes
    -----
    This implements the auxiliary regression procedure of Wooldridge,
    implemented based on procedure 2.1 in Wooldridge 1990.

    Wooldridge allows for multivariate conditional moments (`resid`)
    TODO: check dimensions for multivariate case for extension

    References
    ----------
    Wooldridge
    Wooldridge
    and more Wooldridge

    """
    # notation: Wooldridge uses too mamny Greek letters
    # instruments is capital lambda
    # resid is small phi
    # resid_deriv is capital phi
    # weights is C

    nobs = resid.shape[0]

    from statsmodels.stats.multivariate_tools import partial_project

    w_sqrt = np.sqrt(weights)
    if np.size(weights) > 1:
        w_sqrt = w_sqrt[:, None]
    pp = partial_project(instruments * w_sqrt, resid_deriv * w_sqrt)
    mom_resid = pp.resid

    moms_test = mom_resid * resid[:, None] * w_sqrt

    # we get this here in case we extend resid to be more than 1-D
    k_constraint = moms_test.shape[1]

    # use OPG variance as in Wooldridge 1990. This might generalize
    cov = moms_test.T.dot(moms_test)
    diff = moms_test.sum(0)

    # see Wooldridge last page in appendix
    stat = diff.dot(np.linalg.solve(cov, diff))

    # for checking, this corresponds to nobs * rsquared of auxiliary regression
    stat2 = OLS(np.ones(nobs), moms_test).fit().ess
    pval = stats.chi2.sf(stat, k_constraint)

    return stat, pval, stat2


def lm_robust(score, constraint_matrix, score_deriv_inv, cov_score, cov_params=None):
    """general formula for score/LM test

    generalized score or lagrange multiplier test for implicit constraints

    `r(params) = 0`, with gradient `R = d r / d params`

    linear constraints are given by `R params - q = 0`

    It is assumed that all arrays are evaluated at the constrained estimates.

    Parameters
    ----------
    score : ndarray, 1-D
        derivative of objective function at estimated parameters
        of constrained model
    constraint_matrix R : ndarray
        Linear restriction matrix or Jacobian of nonlinear constraints
    hessian_inv, Ainv : ndarray, symmetric, square
        inverse of second derivative of objective function
        TODO: could be OPG or any other estimator if information matrix
        equality holds
    cov_score B :  ndarray, symmetric, square
        covariance matrix of the score. This is the inner part of a sandwich
        estimator.
    cov_params V :  ndarray, symmetric, square
        covariance of full parameter vector evaluated at constrained parameter
        estimate. This can be specified instead of cov_score B.

    Returns
    -------
    lm_stat : float
        score/lagrange multiplier statistic

    Notes
    -----

    """
    # shorthand alias
    R, Ainv, B, V = constraint_matrix, score_deriv_inv, cov_score, cov_params

    tmp = R.dot(Ainv)
    wscore = tmp.dot(score)  # C Ainv score

    if B is None and V is None:
        # only Ainv is given, so we assume information matrix identity holds
        # computational short cut, should be same if Ainv == inv(B)
        lm_stat = score.dot(Ainv.dot(score))
    else:
        # information matrix identity does not hold
        if V is None:
            inner = tmp.dot(B).dot(tmp.T)
        else:
            inner = R.dot(V).dot(R.T)

        # lm_stat2 = wscore.dot(np.linalg.pinv(inner).dot(wscore))
        # Let's assume inner is invertible, TODO: check if usecase for pinv exists
        lm_stat = wscore.dot(np.linalg.solve(inner, wscore))

    return lm_stat  # , lm_stat2


def lm_robust_subset(score, k_constraints, score_deriv_inv, cov_score):
    """general formula for score/LM test

    generalized score or lagrange multiplier test for constraints on a subset
    of parameters

    `params_1 = value`, where params_1 is a subset of the unconstrained
    parameter vector.

    It is assumed that all arrays are evaluated at the constrained estimates.

    Parameters
    ----------
    score : ndarray, 1-D
        derivative of objective function at estimated parameters
        of constrained model
    k_constraint : int
        number of constraints
    score_deriv_inv : ndarray, symmetric, square
        inverse of second derivative of objective function
        TODO: could be OPG or any other estimator if information matrix
        equality holds
    cov_score B :  ndarray, symmetric, square
        covariance matrix of the score. This is the inner part of a sandwich
        estimator.
    not cov_params V :  ndarray, symmetric, square
        covariance of full parameter vector evaluated at constrained parameter
        estimate. This can be specified instead of cov_score B.

    Returns
    -------
    lm_stat : float
        score/lagrange multiplier statistic
    p-value : float
        p-value of the LM test based on chisquare distribution

    Notes
    -----
    The implementation is based on Boos 1992 section 4.1. The same derivation
    is also in other articles and in text books.

    """

    # Notation in Boos
    # score `S = sum (s_i)
    # score_obs `s_i`
    # score_deriv `I` is derivative of score (hessian)
    # `D` is covariance matrix of score, OPG product given independent observations

    # k_params = len(score)

    # Note: I reverse order between constraint and unconstrained compared to Boos

    # submatrices of score_deriv/hessian
    # these are I22 and I12 in Boos
    # h_uu = score_deriv[-k_constraints:, -k_constraints:]
    h_uu = score_deriv_inv[:-k_constraints, :-k_constraints]
    h_cu = score_deriv_inv[-k_constraints:, :-k_constraints]

    # TODO: pinv or solve ?
    tmp_proj = h_cu.dot(np.linalg.inv(h_uu))
    tmp = np.column_stack((-tmp_proj, np.eye(k_constraints)))  # , tmp_proj))

    cov_score_constraints = tmp.dot(cov_score.dot(tmp.T))

    # lm_stat2 = wscore.dot(np.linalg.pinv(inner).dot(wscore))
    # Let's assume inner is invertible, TODO: check if usecase for pinv exists
    lm_stat = score.dot(np.linalg.solve(cov_score_constraints, score))
    pval = stats.chi2.sf(lm_stat, k_constraints)

    #     # check second calculation Boos referencing Kent 1982 and Engle 1984
    #     # we can use this when robust_cov_params of full model is available
    #     # h_inv = np.linalg.inv(score_deriv)
    #     hinv = score_deriv_inv
    #     v = h_inv.dot(cov_score.dot(h_inv)) # this is robust cov_params
    #     v_cc = v[:k_constraints, :k_constraints]
    #     h_cc = score_deriv[:k_constraints, :k_constraints]
    #     # brute force calculation:
    #     h_resid_cu = h_cc - h_cu.dot(np.linalg.solve(h_uu, h_cu))
    #     cov_s_c = h_resid_cu.dot(v_cc.dot(h_resid_cu))
    #     diff = np.max(np.abs(cov_s_c - cov_score_constraints))
    return lm_stat, pval  # , lm_stat2


def lm_robust_subset_parts(
    score,
    k_constraints,
    score_deriv_uu,
    score_deriv_cu,
    cov_score_cc,
    cov_score_cu,
    cov_score_uu,
):
    """robust generalized score tests on subset of parameters

    This is the same as lm_robust_subset with arguments in parts of
    partitioned matrices.
    This can be useful, when we have the parts based on different estimation
    procedures, i.e. when we do not have the full unconstrained model.

    Calculates mainly the covariance of the constraint part of the score.

    Parameters
    ----------
    score : ndarray, 1-D
        derivative of objective function at estimated parameters
        of constrained model. These is the score component for the restricted
        part under hypothesis. The unconstrained part of the score is assumed
        to be zero.
    k_constraint : int
        number of constraints
    score_deriv_uu : ndarray, symmetric, square
        first derivative of moment equation or second derivative of objective
        function for the unconstrained part
        TODO: could be OPG or any other estimator if information matrix
        equality holds
    score_deriv_cu : ndarray
        first cross derivative of moment equation or second cross
        derivative of objective function between.
    cov_score_cc :  ndarray
        covariance matrix of the score for the unconstrained part.
        This is the inner part of a sandwich estimator.
    cov_score_cu :  ndarray
        covariance matrix of the score for the off-diagonal block, i.e.
        covariance between constrained and unconstrained part.
    cov_score_uu :  ndarray
        covariance matrix of the score for the unconstrained part.

    Returns
    -------
    lm_stat : float
        score/lagrange multiplier statistic
    p-value : float
        p-value of the LM test based on chisquare distribution

    Notes
    -----
    TODO: these function should just return the covariance of the score
    instead of calculating the score/lm test.

    Implementation similar to lm_robust_subset and is based on Boos 1992,
    section 4.1 in the form attributed to Breslow (1990). It does not use the
    computation attributed to Kent (1982) and Engle (1984).
    """

    tmp_proj = np.linalg.solve(score_deriv_uu, score_deriv_cu.T).T
    tmp = tmp_proj.dot(cov_score_cu.T)

    # this needs to make a copy of cov_score_cc for further inplace modification
    cov = cov_score_cc - tmp
    cov -= tmp.T
    cov += tmp_proj.dot(cov_score_uu).dot(tmp_proj.T)

    lm_stat = score.dot(np.linalg.solve(cov, score))
    pval = stats.chi2.sf(lm_stat, k_constraints)
    return lm_stat, pval


def lm_robust_reparameterized(score, params_deriv, score_deriv, cov_score):
    """robust generalized score test for transformed parameters

    The parameters are given by a nonlinear transformation of the estimated
    reduced parameters

    `params = g(params_reduced)`  with jacobian `G = d g / d params_reduced`

    score and other arrays are for full parameter space `params`

    Parameters
    ----------
    score : ndarray, 1-D
        derivative of objective function at estimated parameters
        of constrained model
    params_deriv : ndarray
        Jacobian G of the parameter trasnformation
    score_deriv : ndarray, symmetric, square
        second derivative of objective function
        TODO: could be OPG or any other estimator if information matrix
        equality holds
    cov_score B :  ndarray, symmetric, square
        covariance matrix of the score. This is the inner part of a sandwich
        estimator.

    Returns
    -------
    lm_stat : float
        score/lagrange multiplier statistic
    p-value : float
        p-value of the LM test based on chisquare distribution

    Notes
    -----
    Boos 1992, section 4.3, expression for T_{GS} just before example 6
    """
    # Boos notation
    # params_deriv G

    k_params, k_reduced = params_deriv.shape
    k_constraints = k_params - k_reduced

    G = params_deriv  # shortcut alias

    tmp_c0 = np.linalg.pinv(G.T.dot(score_deriv.dot(G)))
    tmp_c1 = score_deriv.dot(G.dot(tmp_c0.dot(G.T)))
    tmp_c = np.eye(k_params) - tmp_c1

    cov = tmp_c.dot(cov_score.dot(tmp_c.T))  # warning: reduced rank

    lm_stat = score.dot(np.linalg.pinv(cov).dot(score))
    pval = stats.chi2.sf(lm_stat, k_constraints)
    return lm_stat, pval


def conditional_moment_test_generic(
    mom_test,
    mom_test_deriv,
    mom_incl,
    mom_incl_deriv,
    var_mom_all=None,
    cov_type="OPG",
    cov_kwds=None,
):
    """generic conditional moment test

    This is mainly intended as internal function in support of diagnostic
    and specification tests. It has no conversion and checking of correct
    arguments.

    Parameters
    ----------
    mom_test : ndarray, 2-D (nobs, k_constraints)
        moment conditions that will be tested to be zero
    mom_test_deriv : ndarray, 2-D, square (k_constraints, k_constraints)
        derivative of moment conditions under test with respect to the
        parameters of the model summed over observations.
    mom_incl : ndarray, 2-D (nobs, k_params)
        moment conditions that where use in estimation, assumed to be zero
        This is score_obs in the case of (Q)MLE
    mom_incl_deriv : ndarray, 2-D, square (k_params, k_params)
        derivative of moment conditions of estimator summed over observations
        This is the information matrix or Hessian in the case of (Q)MLE.
    var_mom_all : None, or ndarray, 2-D, (k, k) with k = k_constraints + k_params
        Expected product or variance of the joint (column_stacked) moment
        conditions. The stacking should have the variance of the moment
        conditions under test in the first k_constraint rows and columns.
        If it is not None, then it will be estimated based on cov_type.
        I think: This is the Hessian of the extended or alternative model
        under full MLE and score test assuming information matrix identity
        holds.

    Returns
    -------
    results

    Notes
    -----
    TODO: cov_type other than OPG is missing
    initial implementation based on Cameron Trived countbook 1998 p.48, p.56

    also included: mom_incl can be None if expected mom_test_deriv is zero.

    References
    ----------
    Cameron and Trivedi 1998 count book
    Wooldridge ???
    Pagan and Vella 1989
    """
    if cov_type != "OPG":
        raise NotImplementedError

    k_constraints = mom_test.shape[1]

    if mom_incl is None:
        # assume mom_test_deriv is zero, do not include effect of mom_incl
        if var_mom_all is None:
            var_cm = mom_test.T.dot(mom_test)
        else:
            var_cm = var_mom_all

    else:
        # take into account he effect of parameter estimates on mom_test
        if var_mom_all is None:
            mom_all = np.column_stack((mom_test, mom_incl))
            # TODO: replace with inner sandwich covariance estimator
            var_mom_all = mom_all.T.dot(mom_all)

        tmp = mom_test_deriv.dot(np.linalg.pinv(mom_incl_deriv))
        h = np.column_stack((np.eye(k_constraints), -tmp))

        var_cm = h.dot(var_mom_all.dot(h.T))

    # calculate test results with chisquare
    var_cm_inv = np.linalg.pinv(var_cm)
    mom_test_sum = mom_test.sum(0)
    statistic = mom_test_sum.dot(var_cm_inv.dot(mom_test_sum))
    pval = stats.chi2.sf(statistic, k_constraints)

    # normal test of individual components
    se = np.sqrt(np.diag(var_cm))
    tvalues = mom_test_sum / se
    pvalues = stats.norm.sf(np.abs(tvalues))

    res = ResultsGeneric(
        var_cm=var_cm,
        stat_cmt=statistic,
        pval_cmt=pval,
        tvalues=tvalues,
        pvalues=pvalues,
    )

    return res


def conditional_moment_test_regression(
    mom_test,
    mom_test_deriv=None,
    mom_incl=None,
    mom_incl_deriv=None,
    var_mom_all=None,
    demean=False,
    cov_type="OPG",
    cov_kwds=None,
):
    """generic conditional moment test based artificial regression

    this is very experimental, no options implemented yet

    so far
    OPG regression, or
    artificial regression with Robust Wald test

    The latter is (as far as I can see) the same as an overidentifying test
    in GMM where the test statistic is the value of the GMM objective function
    and it is assumed that parameters were estimated with optimial GMM, i.e.
    the weight matrix equal to the expectation of the score variance.
    """
    # so far coded from memory
    nobs, k_constraints = mom_test.shape

    endog = np.ones(nobs)
    if mom_incl is not None:
        ex = np.column_stack((mom_test, mom_incl))
    else:
        ex = mom_test
    if demean:
        ex -= ex.mean(0)
    if cov_type == "OPG":
        res = OLS(endog, ex).fit()

        statistic = nobs * res.rsquared
        pval = stats.chi2.sf(statistic, k_constraints)
    else:
        res = OLS(endog, ex).fit(cov_type=cov_type, cov_kwds=cov_kwds)
        tres = res.wald_test(np.eye(ex.shape[1]))
        statistic = tres.statistic
        pval = tres.pvalue

    return statistic, pval


class CMTNewey:
    """generic moment test for GMM

    This is a class to calculate and hold the various results

    This is based on Newey 1985 on GMM.
    Lemma 1:
    Theorem 1

    The main method is `chisquare` which returns the result of the
    conditional moment test.

    Warning: name of class and methods will likely be changed

    Parameters
    ----------
    moments : ndarray, 1-D
        moments that are tested to be zero. They do not need to be derived
        from a likelihood function.
    moments_deriv : ndarray
        derivative of the moment function with respect to the parameters that
        are estimated
    cov_moments : ndarray
        An estimate for the joint (expected) covariance of all moments. This
        can be a heteroscedasticity or correlation robust covariance estimate,
        i.e. the inner part of a sandwich covariance.
    weights : ndarray
        Weights used in the GMM estimation.
    transf_mt : ndarray
        This defines the test moments where `transf_mt` is the matrix that
        defines a Linear combination of moments that have expected value equal
        to zero under the Null hypothesis.

    Notes
    -----
    The one letter names in Newey 1985 are

    moments, g :
    cov_moments, V :
    moments_deriv, H :
    weights, W :
    transf_mt, L :
