Source code for linearmodels.iv.absorbing

from __future__ import annotations

from collections import defaultdict
from typing import (
import warnings

from numpy import (
    any as npany,
from numpy.linalg import lstsq
from pandas import Categorical, DataFrame, Series
from pandas.api.types import is_categorical_dtype
import scipy.sparse as sp
from scipy.sparse.linalg import lsmr

from linearmodels.iv.common import f_statistic, find_constant
from import IVData
from linearmodels.iv.model import (
from linearmodels.iv.results import AbsorbingLSResults
from linearmodels.panel.utility import (
from linearmodels.shared.exceptions import missing_warning
from linearmodels.shared.hypotheses import InvalidTestStatistic, WaldTestStatistic
from linearmodels.shared.utility import DataFrameWrapper, SeriesWrapper
from linearmodels.typing import AnyPandas, BoolArray, Float64Array
from import ArrayLike, OptionalArrayLike

    from xxhash import xxh64 as hash_func
except ImportError:
    from hashlib import sha256 as hash_func

Hasher = TypeVar("Hasher", bound=hash_func)

_VARIABLE_CACHE: DefaultDict[Hashable, Dict[str, ndarray]] = defaultdict(dict)

def _reset(hasher: Hasher) -> Hasher:
        return hasher
    except AttributeError:
        return hash_func()

def clear_cache() -> None:
    """Clear the absorbed variable cache"""

def lsmr_annihilate(
    x: sp.csc_matrix,
    y: Float64Array,
    use_cache: bool = True,
    x_hash: Optional[Hashable] = None,
    **lsmr_options: Union[bool, str, ArrayLike, None, Dict[str, Any]],
) -> Float64Array:
    Removes projection of x on y from y

    x : csc_matrix
        Sparse array of regressors
    y : ndarray
        Array with shape (nobs, nvar)
    use_cache : bool
        Flag indicating whether results should be stored in the cache,
        and retrieved if available.
    x_hash : object
        Hashable object representing the values in x
    lsmr_options: dict
        Dictionary of options to pass to scipy.sparse.linalg.lsmr

        Returns the residuals from regressing y on x, (nobs, nvar)

    Residuals are estimated column-by-column as

    .. math::

        \hat{\epsilon}_{j} = y_{j} - x^\prime \hat{\beta}

    where :math:`\hat{\beta}` is computed using lsmr.
    if y.shape[1] == 0:
        return empty_like(y)
    use_cache = use_cache and x_hash is not None
    regressor_hash = x_hash if x_hash is not None else ""
    default_opts: Dict[str, Union[bool, str, ArrayLike, None, Dict[str, Any]]] = dict(
        atol=1e-8, btol=1e-8, show=False
    assert lsmr_options is not None
    resids = []
    for i in range(y.shape[1]):
        _y = y[:, i : i + 1]

        variable_digest = ""
        if use_cache:
            hasher = hash_func()
            variable_digest = hasher.hexdigest()

        if use_cache and variable_digest in _VARIABLE_CACHE[regressor_hash]:
            resid = _VARIABLE_CACHE[regressor_hash][variable_digest]
            beta = lsmr(x, _y, **default_opts)[0]
            resid = y[:, i : i + 1] - ([:, None]))).A
            _VARIABLE_CACHE[regressor_hash][variable_digest] = resid
    return column_stack(resids)

def category_product(cats: AnyPandas) -> Series:
    Construct category from all combination of input categories

    cats : {Series, DataFrame}
        DataFrame containing categorical variables.  If cats is a Series, cats
        is returned unmodified.

        Categorical series containing the cartesian product of the categories
        in cats
    if isinstance(cats, Series):
        return cats

    sizes = []
    for c in cats:
        if not is_categorical_dtype(cats[c]):
            raise TypeError("cats must contain only categorical variables")
        col = cats[c]
        max_code =
        size = 1
        while max_code >= 2**size:
            size += 1
    nobs = cats.shape[0]
    total_size = sum(sizes)
    if total_size >= 63:
        raise ValueError(
            "There are too many cats with too many states to use this method."
    dtype_size = min(filter(lambda v: total_size < (v - 1), (8, 16, 32, 64)))
    dtype_str = "int{0:d}".format(dtype_size)
    dtype_val = dtype(dtype_str)
    codes = zeros(nobs, dtype=dtype_val)
    cum_size = 0
    for i, col in enumerate(cats):
        if dtype_str == "int8":
            shift: Union[int8, int16, int32, int64] = int8(cum_size)
        elif dtype_str == "int16":
            shift = int16(cum_size)
        elif dtype_str == "int32":
            shift = int32(cum_size)
        else:  # elif dtype_str == "int64":
            shift = int64(cum_size)
        codes += cats[col] << shift
        cum_size += sizes[i]
    return Series(Categorical(codes), index=cats.index)

def category_interaction(cat: Series, precondition: bool = True) -> sp.csc_matrix:
    cat : Series
        Categorical series to convert to dummy variables
    precondition : bool
        Flag whether dummies should be preconditioned

        Sparse matrix of dummies with unit column norm
    codes = asarray(category_product(cat)[:, None]
    mat = dummy_matrix(codes, precondition=precondition)[0]
    assert isinstance(mat, sp.csc_matrix)
    return mat

def category_continuous_interaction(
    cat: AnyPandas, cont: AnyPandas, precondition: bool = True
) -> sp.csc_matrix:
    cat : Series
        Categorical series to convert to dummy variables
    cont : {Series, DataFrame}
        Continuous variable values to use in the dummy interaction
    precondition : bool
        Flag whether dummies should be preconditioned

        Sparse matrix of dummy interactions with unit column norm
    codes = category_product(cat)
    interact = sp.csc_matrix((cont.to_numpy().flat, (arange(codes.shape[0]), codes)))
    if not precondition:
        return interact
        contioned = preconditioner(interact)[0]
        assert isinstance(contioned, sp.csc_matrix)
        return contioned

[docs]class Interaction(object): """ Class that simplifies specifying interactions Parameters ---------- cat : {ndarray, Series, DataFrame, DataArray} Variables to treat as categoricals. Best format is a Categorical Series or DataFrame containing Categorical Series. Other formats are converted to Categorical Series, column-by-column. cats has shape (nobs, ncat). cont : {ndarray, Series, DataFrame, DataArray} Variables to treat as continuous, (nobs, ncont). Notes ----- For each variable in `cont`, computes the interaction of the variable and the cartesian product of the categories. Examples -------- >>> import numpy as np >>> from linearmodels.iv.absorbing import Interaction >>> rs = np.random.RandomState(0) >>> n = 100000 >>> cats = rs.randint(2, size=n) # binary dummy >>> cont = rs.standard_normal((n, 3)) >>> interact = Interaction(cats, cont) >>> interact.sparse.shape # Get the shape of the dummy matrix (100000, 6) >>> rs = np.random.RandomState(0) >>> import pandas as pd >>> cats_df = pd.concat([pd.Series(pd.Categorical(rs.randint(5,size=n))) ... for _ in range(4)], axis=1) >>> cats_df.describe() 0 1 2 3 count 100000 100000 100000 100000 unique 5 5 5 5 top 3 3 0 4 freq 20251 20195 20331 20158 >>> interact = Interaction(cats_df, cont) >>> interact.sparse.shape # Cart product of all cats, 5**4, times ncont, 3 (100000, 1875) """ _iv_data = IVData(None, "none", 1) def __init__( self, cat: OptionalArrayLike = None, cont: OptionalArrayLike = None, nobs: Optional[int] = None, ) -> None: self._cat = cat self._cont = cont self._cat_data = self._iv_data self._cont_data = self._iv_data self._nobs = nobs self._check_data() @property def nobs(self) -> int: assert self._nobs is not None return self._nobs def _check_data(self) -> None: cat, cont = self._cat, self._cont cat_nobs = getattr(cat, "shape", (0,))[0] cont_nobs = getattr(cont, "shape", (0,))[0] nobs = max(cat_nobs, cont_nobs) if cat is None and cont is None: if self._nobs is not None: self._cont_data = self._cat_data = IVData(None, "none", nobs=self._nobs) else: raise ValueError("nobs must be provided when cat and cont are None") return self._nobs = nobs self._cat_data = IVData(cat, "cat", nobs=nobs, convert_dummies=False) self._cont_data = IVData(cont, "cont", nobs=nobs, convert_dummies=False) if self._cat_data.shape[1] == self._cont_data.shape[1] == 0: raise ValueError("Both cat and cont are empty arrays") cat_data = self._cat_data.pandas convert = [col for col in cat_data if not (is_categorical_dtype(cat_data[col]))] if convert: cat_data = DataFrame( {col: cat_data[col].astype("category") for col in cat_data} ) self._cat_data = IVData(cat_data, "cat", convert_dummies=False) @property def cat(self) -> DataFrame: """Categorical Variables""" return self._cat_data.pandas @property def cont(self) -> DataFrame: """Continuous Variables""" return self._cont_data.pandas @property def isnull(self) -> Series: return | self.cont.isnull().any(1)
[docs] def drop(self, locs: BoolArray) -> None: self._cat_data.drop(locs) self._cont_data.drop(locs)
@property def sparse(self) -> sp.csc_matrix: r""" Construct a sparse interaction matrix Returns ------- csc_matrix Dummy interaction constructed from the cartesian product of the categories and each of the continuous variables. Notes ----- The number of columns in `dummy_interact` is .. math:: ncont \times \prod_{i=1}^{ncat} |c_i| where :math:`|c_i|` is the number distinct categories in column i. """ if[1] and self.cont.shape[1]: out = [] for col in self.cont: out.append( category_continuous_interaction(, self.cont[col], precondition=False ) ) return sp.hstack(out, format="csc") elif[1]: return category_interaction(category_product(, precondition=False) elif self.cont.shape[1]: return sp.csc_matrix(self._cont_data.ndarray) else: # empty interaction return sp.csc_matrix(empty((self._cat_data.shape[0], 0))) @property def hash(self) -> List[Tuple[str, ...]]: """ Construct a hash that will be invariant for any permutation of inputs that produce the same fit when used as regressors""" # Sorted hashes of any categoricals hasher = hash_func() cat_hashes = [] cat = for col in cat: hasher.update(ascontiguousarray([col] cat_hashes.append(hasher.hexdigest()) hasher = _reset(hasher) sorted_hashes = tuple(sorted(cat_hashes)) hashes = [] cont = self.cont for col in cont: hasher.update(ascontiguousarray(cont[col].to_numpy()).data) hashes.append(sorted_hashes + (hasher.hexdigest(),)) hasher = _reset(hasher) return sorted(hashes)
[docs] @staticmethod def from_frame(frame: DataFrame) -> Interaction: """ Convenience function the simplifies using a DataFrame Parameters ---------- frame : DataFrame Frame containing categorical and continuous variables. All categorical variables are passed to `cat` and all other variables are passed as `cont`. Returns ------- Interaction Instance using the columns of frame Examples -------- >>> import numpy as np >>> from linearmodels.iv.absorbing import Interaction >>> import pandas as pd >>> rs = np.random.RandomState(0) >>> n = 100000 >>> cats = pd.concat([pd.Series(pd.Categorical(rs.randint(i+2,size=n))) ... for i in range(4)], axis=1) >>> cats.columns = ["cat{0}".format(i) for i in range(4)] >>> columns = ["cont{0}".format(i) for i in range(6)] >>> cont = pd.DataFrame(rs.standard_normal((n, 6)), columns=columns) >>> frame = pd.concat([cats, cont], axis=1) >>> interact = Interaction.from_frame(frame) >>> interact.sparse.shape # Cart product of all cats, 5!, times ncont, 6 (100000, 720) """ cat_cols = [col for col in frame if is_categorical_dtype(frame[col])] cont_cols = [col for col in frame if col not in cat_cols] return Interaction(frame[cat_cols], frame[cont_cols], nobs=frame.shape[0])
InteractionVar = Union[DataFrame, Interaction] class AbsorbingRegressor(object): """ Constructed weights sparse matrix from components Parameters ---------- cat : DataFrame List of categorical variables (factors) to absorb cont : DataFrame List of continuous variables to absorb interactions : List[Interaction] List of included interactions weights : ndarray Weights, if any """ def __init__( self, *, cat: DataFrame = None, cont: DataFrame = None, interactions: Optional[List[Interaction]] = None, weights: Optional[Float64Array] = None, ): self._cat = cat self._cont = cont self._interactions = interactions self._weights = weights self._approx_rank: Optional[int] = None @property def has_constant(self) -> bool: """Flag indicating whether the regressors have a constant equivalent""" return self._cat is not None and self._cat.shape[1] > 0 @property def approx_rank(self) -> int: if self._approx_rank is None: self._regressors() assert self._approx_rank is not None return self._approx_rank @property def hash(self) -> Tuple[Tuple[str, ...], ...]: hashes: List[Tuple[str, ...]] = [] hasher = hash_func() if self._cat is not None: for col in self._cat: hasher.update( ascontiguousarray(self._cat[col] ) hashes.append((hasher.hexdigest(),)) hasher = _reset(hasher) if self._cont is not None: for col in self._cont: hasher.update(ascontiguousarray(self._cont[col].to_numpy()).data) hashes.append((hasher.hexdigest(),)) hasher = _reset(hasher) if self._interactions is not None: for interact in self._interactions: hashes.extend(interact.hash) # Add weight hash if provided if self._weights is not None: hasher = hash_func() hasher.update(ascontiguousarray( hashes.append((hasher.hexdigest(),)) return tuple(sorted(hashes)) @property def regressors(self) -> sp.csc_matrix: return self._regressors() def _regressors(self) -> sp.csc_matrix: regressors = [] if self._cat is not None and self._cat.shape[1] > 0: regressors.append(dummy_matrix(self._cat, precondition=False)[0]) if self._cont is not None and self._cont.shape[1] > 0: regressors.append(sp.csc_matrix(self._cont.to_numpy())) if self._interactions is not None: regressors.extend([interact.sparse for interact in self._interactions]) if regressors: regressor_mat = sp.hstack(regressors, format="csc") approx_rank = regressor_mat.shape[1] self._approx_rank = approx_rank if self._weights is not None: return ( sp.diags(sqrt(self._weights.squeeze())).dot(regressor_mat) ).asformat("csc") return regressor_mat else: self._approx_rank = 0 return sp.csc_matrix(empty((0, 0)))
[docs]class AbsorbingLS(object): r""" Linear regression with high-dimensional effects Parameters ---------- dependent : array_like Endogenous variables (nobs by 1) exog : array_like Exogenous regressors (nobs by nexog) absorb: {DataFrame, Interaction} The effects or continuous variables to absorb. When using a DataFrame, effects must be categorical variables. Other variable types are treated as continuous variables that should be absorbed. When using an Interaction, variables in the `cat` argument are treated as effects and variables in the `cont` argument are treated as continuous. interactions : {DataFrame, Interaction, List[DataFrame, Interaction]} Interactions containing both categorical and continuous variables. Each interaction is constructed using the Cartesian product of the categorical variables to produce the dummy, which are then separately interacted with each continuous variable. weights : array_like Observation weights used in estimation drop_absorbed : bool Flag indicating whether to drop absorbed variables Notes ----- Capable of estimating models with millions of effects. Estimates models of the form .. math:: y_i = x_i \beta + z_i \gamma + \epsilon_i where :math:`\beta` are parameters of interest and :math:`\gamma` are not. z may be high-dimensional, although must have fewer variables than the number of observations in y. The syntax simplifies specifying high-dimensional z when z consists of categorical (factor) variables, also known as effects, or when z contains interactions between continuous variables and categorical variables, also known as fixed slopes. The high-dimensional effects are fit using LSMR which avoids inverting or even constructing the inner product of the regressors. This is combined with Frish-Waugh-Lovell to orthogonalize x and y from z. z can contain factors that are perfectly linearly dependent. LSMR estimates a particular restricted set of parameters that captures the effect of non-redundant components in z. See also -------- Interaction linearmodels.iv.model.IVLIML linearmodels.iv.model.IV2SLS scipy.sparse.linalg.lsmr Examples -------- Estimate a model by absorbing 2 categoricals and 2 continuous variables >>> import numpy as np >>> import pandas as pd >>> from linearmodels.iv import AbsorbingLS, Interaction >>> dep = np.random.standard_normal((20000,1)) >>> exog = np.random.standard_normal((20000,2)) >>> cats = pd.DataFrame({i: pd.Categorical(np.random.randint(1000, size=20000)) ... for i in range(2)}) >>> cont = pd.DataFrame({i+2: np.random.standard_normal(20000) for i in range(2)}) >>> absorb = pd.concat([cats, cont], axis=1) >>> mod = AbsorbingLS(dep, exog, absorb=absorb) >>> res = Add interactions between the cartesian product of the categorical and each continuous variables >>> iaction = Interaction(cat=cats, cont=cont) >>> absorb = Interaction(cat=cats) # Other encoding of categoricals >>> mod = AbsorbingLS(dep, exog, absorb=absorb, interactions=iaction) """ def __init__( self, dependent: ArrayLike, exog: OptionalArrayLike = None, *, absorb: InteractionVar = None, interactions: Union[InteractionVar, Iterable[InteractionVar]] = None, weights: OptionalArrayLike = None, drop_absorbed: bool = False, ) -> None: self._dependent = IVData(dependent, "dependent") self._nobs = nobs = self._dependent.shape[0] self._exog = IVData(exog, "exog", nobs=self._nobs) self._absorb = absorb if isinstance(absorb, DataFrame): self._absorb_inter = Interaction.from_frame(absorb) elif absorb is None: self._absorb_inter = Interaction(None, None, nobs) elif isinstance(absorb, Interaction): self._absorb_inter = absorb else: raise TypeError("absorb must ba a DataFrame or an Interaction") self._weights = weights self._is_weighted = False self._drop_absorbed = drop_absorbed self._check_weights() self._interactions = interactions self._interaction_list: List[Interaction] = [] self._prepare_interactions() self._absorbed_dependent: Optional[DataFrame] = None self._absorbed_exog: Optional[DataFrame] = None self._check_shape() self._original_index = self._dependent.pandas.index self._drop_locs = self._drop_missing() self._columns = self._exog.cols self._index = self._dependent.rows self._method = "Absorbing LS" self._const_col = 0 self._has_constant = False self._has_constant_exog = self._check_constant() self._constant_absorbed = False self._num_params = 0 self._regressors: Optional[sp.csc_matrix] = None self._regressors_hash: Optional[Tuple[Tuple[str, ...], ...]] = None def _drop_missing(self) -> BoolArray: missing = self.dependent.isnull.to_numpy() missing |= self.exog.isnull.to_numpy() missing |= missing |= self._absorb_inter.cont.isnull().any(1).to_numpy() for interact in self._interaction_list: missing |= interact.isnull.to_numpy() if npany(missing): self.dependent.drop(missing) self.exog.drop(missing) self._absorb_inter.drop(missing) for interact in self._interaction_list: interact.drop(missing) missing_warning(missing, stacklevel=4) return missing def _check_constant(self) -> bool: col_delta = ptp(self.exog.ndarray, 0) has_constant = npany(col_delta == 0) self._const_col = where(col_delta == 0)[0][0] if has_constant else None return bool(has_constant) def _check_weights(self) -> None: if self._weights is None: nobs = self._dependent.shape[0] self._is_weighted = False self._weight_data = IVData(ones(nobs), "weights") else: self._is_weighted = True weights = IVData(self._weights).ndarray weights = weights / nanmean(weights) self._weight_data = IVData(weights, var_name="weights", nobs=self._nobs) def _check_shape(self) -> None: nobs = self._nobs if self._absorb is not None: if self._absorb_inter.nobs != nobs: raise ValueError( "absorb and dependent have different number of observations" ) for interact in self._interaction_list: if interact.nobs != nobs: raise ValueError( "interactions ({0}) and dependent have different number of " "observations".format(str(interact)) ) @property def absorbed_dependent(self) -> DataFrame: """ Dependent variable with effects absorbed Returns ------- DataFrame Dependent after effects have been absorbed Raises ------ RuntimeError If called before `fit` has been used once """ if self._absorbed_dependent is not None: return self._absorbed_dependent raise RuntimeError( "fit must be called once before absorbed_dependent is available" ) @property def absorbed_exog(self) -> DataFrame: """ Exogenous variables with effects absorbed Returns ------- DataFrame Exogenous after effects have been absorbed Raises ------ RuntimeError If called before `fit` has been used once """ if self._absorbed_exog is not None: return self._absorbed_exog raise RuntimeError("fit must be called once before absorbed_exog is available") @property def weights(self) -> IVData: return self._weight_data @property def dependent(self) -> IVData: return self._dependent @property def exog(self) -> IVData: return self._exog @property def has_constant(self) -> bool: return self._has_constant @property def instruments(self) -> IVData: return IVData(None, "instrument", nobs=self._dependent.shape[0]) def _prepare_interactions(self) -> None: if self._interactions is None: return elif isinstance(self._interactions, DataFrame): self._interaction_list = [Interaction.from_frame(self._interactions)] elif isinstance(self._interactions, Interaction): self._interaction_list = [self._interactions] else: for interact in self._interactions: if isinstance(interact, DataFrame): self._interaction_list.append(Interaction.from_frame(interact)) elif isinstance(interact, Interaction): self._interaction_list.append(interact) else: raise TypeError( "interactions must contain DataFrames or Interactions" ) def _first_time_fit( self, use_cache: bool, absorb_options: Optional[ Dict[str, Union[bool, str, ArrayLike, None, Dict[str, Any]]] ], method: str, ) -> None: weights = ( cast(Float64Array, self.weights.ndarray) if self._is_weighted else None ) use_hdfe = weights is None and method in ("auto", "hdfe") use_hdfe = use_hdfe and not self._absorb_inter.cont.shape[1] use_hdfe = use_hdfe and not self._interaction_list if not use_hdfe and method == "hdfe": raise RuntimeError( "HDFE has been set as the method but the model cannot be estimated " "using HDFE. HDFE requires that the model is unweighted and that the " "absorbed regressors include only fixed effects (dummy variables)." ) areg = AbsorbingRegressor(, cont=self._absorb_inter.cont, interactions=self._interaction_list, weights=weights, ) areg_constant = areg.has_constant self._regressors = areg.regressors self._num_params += areg.approx_rank # Do not double count intercept-like terms self._has_constant = self._has_constant_exog or areg_constant self._num_params -= min(self._has_constant_exog, areg_constant) self._regressors_hash = areg.hash self._constant_absorbed = self._has_constant_exog and areg_constant dep = self._dependent.ndarray exog = cast(Float64Array, self._exog.ndarray) root_w = sqrt(self._weight_data.ndarray) dep = root_w * dep exog = root_w * exog denom = root_w.T @ root_w mu_dep = (root_w.T @ dep) / denom mu_exog = (root_w.T @ exog) / denom absorb_options = {} if absorb_options is None else absorb_options assert isinstance(self._regressors, sp.csc_matrix) if self._regressors.shape[1] > 0: if use_hdfe: from pyhdfe import create absorb_options["drop_singletons"] = False algo = create(, **absorb_options) dep_exog = column_stack((dep, exog)) resids = algo.residualize(dep_exog) dep_resid = resids[:, :1] exog_resid = resids[:, 1:] else: self._regressors = preconditioner(self._regressors)[0] dep_exog = column_stack((dep, exog)) resid = lsmr_annihilate( self._regressors, dep_exog, use_cache, self._regressors_hash, **absorb_options, ) dep_resid = resid[:, :1] exog_resid = resid[:, 1:] else: dep_resid = dep exog_resid = exog if self._constant_absorbed: dep_resid += root_w * mu_dep exog_resid += root_w * mu_exog if not self._drop_absorbed: check_absorbed(exog_resid, self.exog.cols, exog) else: ncol = exog_resid.shape[1] retain = not_absorbed(exog_resid) if not retain: raise ValueError( "All columns in exog have been fully absorbed by the " "included effects. This model cannot be estimated." ) elif len(retain) < ncol: drop = set(range(ncol)).difference(retain) dropped = ", ".join([str(self.exog.cols[i]) for i in drop]) warnings.warn( absorbing_warn_msg.format(absorbed_variables=dropped), AbsorbingEffectWarning, stacklevel=3, ) exog_resid = exog_resid[:, retain] self._columns = [self._columns[i] for i in retain] self._absorbed_dependent = DataFrame( dep_resid, index=self._dependent.pandas.index, columns=self._dependent.pandas.columns, ) self._absorbed_exog = DataFrame( exog_resid, index=self._exog.pandas.index, columns=self._columns )
[docs] def fit( self, *, cov_type: str = "robust", debiased: bool = False, method: str = "auto", absorb_options: Optional[ Dict[str, Union[bool, str, ArrayLike, None, Dict[str, Any]]] ] = None, use_cache: bool = True, lsmr_options: Optional[Dict[str, Union[float, bool]]] = None, **cov_config: Any, ) -> AbsorbingLSResults: """ Estimate model parameters Parameters ---------- cov_type : str Name of covariance estimator to use. Supported covariance estimators are: * "unadjusted", "homoskedastic" - Classic homoskedastic inference * "robust", "heteroskedastic" - Heteroskedasticity robust inference * "kernel" - Heteroskedasticity and autocorrelation robust inference * "cluster" - One-way cluster dependent inference. Heteroskedasticity robust debiased : bool Flag indicating whether to debiased the covariance estimator using a degree of freedom adjustment. method : str One of: * "auto" - (Default). Use HDFE when applicable and fallback to LSMR. * "lsmr" - Force LSMR. * "hdfe" - Force HDFE. Raises RuntimeError if the model contains continuous variables or continuous-binary interactions to absorb or if the model is weighted. absorb_options : dict Dictionary of options to pass to the absorber. Passed to either scipy.sparse.linalg.lsmr or pyhdfe.create depending on the method used to absorb the absorbed regressors. use_cache : bool Flag indicating whether the variables, once purged from the absorbed variables and interactions, should be stored in the cache, and retrieved if available. Cache can dramatically speed up re-fitting large models when the set of absorbed variables and interactions are identical. lsmr_options: dict Options to ass to scipy.sparse.linalg.lsmr. .. deprecated:: 4.17 Use absorb_options to pass options **cov_config Additional parameters to pass to covariance estimator. The list of optional parameters differ according to ``cov_type``. See the documentation of the alternative covariance estimators for the complete list of available commands. Returns ------- AbsorbingLSResults Results container Notes ----- Additional covariance parameters depend on specific covariance used. The see the docstring of specific covariance estimator for a list of supported options. Defaults are used if no covariance configuration is provided. If use_cache is True, then variables are hashed based on their contents using either a 64 bit value (if xxhash is installed) or a 256 bit value. This allows variables to be reused in different models if the set of absorbing variables and interactions is held constant. See also -------- linearmodels.iv.covariance.HomoskedasticCovariance linearmodels.iv.covariance.HeteroskedasticCovariance linearmodels.iv.covariance.KernelCovariance linearmodels.iv.covariance.ClusteredCovariance """ if lsmr_options is not None: if absorb_options is not None: raise ValueError("absorb_options cannot be used with lsmr_options") warnings.warn( "lsmr_options is deprecated. Use absorb_options.", FutureWarning, stacklevel=2, ) absorb_options = {k: v for k, v in lsmr_options.items()} if self._absorbed_dependent is None: self._first_time_fit(use_cache, absorb_options, method) exog_resid = self.absorbed_exog.to_numpy() dep_resid = self.absorbed_dependent.to_numpy() if self._exog.shape[1] == 0: params = empty((0, 1)) else: params = lstsq(exog_resid, dep_resid, rcond=None)[0] self._num_params += exog_resid.shape[1] cov_estimator = COVARIANCE_ESTIMATORS[cov_type] cov_config["debiased"] = debiased cov_config["kappa"] = 0.0 cov_config_copy = {k: v for k, v in cov_config.items()} if "center" in cov_config_copy: del cov_config_copy["center"] cov_estimator_inst = cov_estimator( exog_resid, dep_resid, exog_resid, params, **cov_config_copy ) results = {"kappa": 0.0, "liml_kappa": 0.0} pe = self._post_estimation(params, cov_estimator_inst, cov_type) results.update(pe) results["df_model"] = self._num_params return AbsorbingLSResults(results, self)
[docs] def resids(self, params: Float64Array) -> Float64Array: """ Compute model residuals Parameters ---------- params : ndarray Model parameters (nvar by 1) Returns ------- ndarray Model residuals """ resids = self.wresids(params) return resids / sqrt(self.weights.ndarray)
[docs] def wresids(self, params: Float64Array) -> Float64Array: """ Compute weighted model residuals Parameters ---------- params : ndarray Model parameters (nvar by 1) Returns ------- ndarray Weighted model residuals Notes ----- Uses weighted versions of data instead of raw data. Identical to resids if all weights are unity. """ assert isinstance(self._absorbed_dependent, DataFrame) assert isinstance(self._absorbed_exog, DataFrame) return ( self._absorbed_dependent.to_numpy() - self._absorbed_exog.to_numpy() @ params )
def _f_statistic( self, params: Float64Array, cov: Float64Array, debiased: bool ) -> Union[WaldTestStatistic, InvalidTestStatistic]: const_loc = find_constant(cast(Float64Array, self._exog.ndarray)) resid_df = self._nobs - self._num_params return f_statistic(params, cov, debiased, resid_df, const_loc) def _post_estimation( self, params: Float64Array, cov_estimator: Union[ HomoskedasticCovariance, HeteroskedasticCovariance, KernelCovariance, ClusteredCovariance, ], cov_type: str, ) -> Dict[str, Any]: columns = self._columns index = self._index eps = self.resids(params) fitted_values = self._dependent.ndarray - eps fitted = DataFrameWrapper( fitted_values, index=self._dependent.rows, columns=["fitted_values"], ) assert isinstance(self._absorbed_dependent, DataFrame) absorbed_effects = DataFrameWrapper( self._absorbed_dependent.to_numpy() - fitted_values, columns=["absorbed_effects"], index=self._dependent.rows, ) weps = self.wresids(params) cov = cov_estimator.cov debiased = cov_estimator.debiased residual_ss = (weps.T @ weps)[0, 0] w = self.weights.ndarray root_w = sqrt(w) e = self._dependent.ndarray * root_w if self.has_constant: e = e - root_w * average(self._dependent.ndarray, weights=w) total_ss = float(e.T @ e) r2 = max(1 - residual_ss / total_ss, 0.0) e = self._absorbed_dependent.to_numpy() # already scaled by root_w # If absorbing contains a constant, but exog does not, no need to demean assert isinstance(self._absorbed_exog, DataFrame) if self._const_col is not None: col = self._const_col x = self._absorbed_exog.to_numpy()[:, col : col + 1] mu = (lstsq(x, e, rcond=None)[0]).squeeze() e = e - x * mu aborbed_total_ss = float(e.T @ e) r2_absorbed = max(1 - residual_ss / aborbed_total_ss, 0.0) fstat = self._f_statistic(params, cov, debiased) out = { "params": Series(params.squeeze(), columns, name="parameter"), "eps": SeriesWrapper(eps.squeeze(), index=index, name="residual"), "weps": SeriesWrapper( weps.squeeze(), index=index, name="weighted residual" ), "cov": DataFrame(cov, columns=columns, index=columns), "s2": float(cov_estimator.s2), "debiased": debiased, "residual_ss": float(residual_ss), "total_ss": float(total_ss), "r2": float(r2), "fstat": fstat, "vars": columns, "instruments": [], "cov_config": cov_estimator.config, "cov_type": cov_type, "method": self._method, "cov_estimator": cov_estimator, "fitted": fitted, "original_index": self._original_index, "absorbed_effects": absorbed_effects, "absorbed_r2": r2_absorbed, } return out