Change Log

Version 4.27

  • Switched from patsy for formulaic for linear constraint translation when using the formula interface. This allows formulas to be specified using a dictionary of constraints in addition to the standard single string of list of strings (GH439, GH440).

  • Fixed a bug when using escaped variable names, e.g., y ~ 1 + `x 3`, in panel data models (GH435).

  • Fixed a bug that affected creating IV models using formulas with missing data (GH438).

  • Fixed examples in documentation (GH434).

Version 4.26

  • Fixed a bug in IV2SLS and related models where predict() would produce NaN values when exog and endog had different indices. An IndexWarning is now shown.

  • Added stacklevel to all warnings to improve accuracy of warning location.

Version 4.25

Version 4.24

Version 4.21

  • Fixed a bug that affected SUR when estimating models using method="gls" and heteroskedasticity-, hac, or cluster-robust inference.

  • Added rank_check argument to panel-data models that allows the rank check to be skipped. Estimating a model that is rank deficient may result in unreliable estimates and so caution is needed if using this option.

  • Changed the rank check to use numpy.linalg.lstsq() which is better aligned with parameter estimation than the numpy.linalg.svd()-based numpy.linalg.matrix_rank().

  • Changed the default least squares used to scipy.linalg.lstsq() so that the lapack_driver can be changed to use QR factorization.

Version 4.20

  • Correct calculation of first-stage F-statistic in IV models.

Version 4.19

  • Minor release to fix a wheel-building issue on Python 3.9

Version 4.18

Version 4.17

Version 4.16

Version 4.15

  • Blackened the code.

  • Added McElroy’s and Berndt’s measures of system fit (GH215).

  • Removed support for Python 3.5 inline with NEP-29 (GH222).

Version 4.14

  • Fixed issue where datasets were not installed with wheels (GH217).

  • Switched to property-cached to inherit cached property from property (GH211).

  • Removed all use of pandas.Panel (GH211).

Version 4.13

  • Added AbsorbingLS which allows a large number of variables to be absorbed. This model can handle very high-dimensional dummy variables and has been tested using up to 1,000,000 categories in a data set with 5,000,000 observations.

  • Fixed a bug when estimating weighted panel models that have repeated observations (i.e., more than one observation per entity and time id).

  • Added drop_absorbed option to PanelOLS which automatically drops variables that are absorbed by fixed effects. (GH206)

  • Added optional Cythonized node selection for dropping singletons

  • Added preconditioning to the dummy variable matrix when use_lsmr-True in fit(). In models with many effects, this can reduce run time by a factor of 4 or more.

Version 4.12

  • Added an option to drop singleton observations in PanelOLS by setting the keyword argument singletons-False. When False, singelton observations are dropped before the model is fit, so the the result is as-if the observations were never in exog or dependent.

  • Added a method to construct the 2-core graph for 2-way effects models, which allows singleton observations with no effect on estimated slopes to be excluded. (GH191)

  • Added support for LSMR estimation of parameters in fit() through the keyword argument use_lsmr. LSMR is a sparse estimation method that can be used to extend PanelOLS to more than two effects.

  • Fixed a bug where IV models estimated with only exogenous regressors where not being correctly labeled as OLS models in output. (GH185)

  • Added wald_test to panel-model results.

  • Renamed test_linear_constraint to wald_test

  • Added a low-memory option to fit() that avoids constructing dummy variables. Only used when both entity_effects and time_effects are True. By default, the low memory algorithm will be used whenever constructing the dummy variable array would require more than 1 GiB. (GH182)

  • Added an option in model comparison (compare() and compare()) to report standard errors or pvalues instead of t-stats. (GH178)

Version 4.11

  • Fixed a bug which did not correctly check the rank of the cross-section regression in FamaMacBeth (GH176)

  • Fixed a bug which failed to correctly check rank conditions when specifying asset pricing models (GH173)

  • Switched to external package cached-property to manage caching instead of custom and less-well-tested solution (GH172)

Version 4.10

  • Fixed a bug where weights were incorrectly calculated for HAC covariances when the weight function was 'parzen' or 'gallant' (GH170)

Version 4.9

  • Changed the return type of Wooldridge’s over identification test when invalid to InvalidTestStatistic

  • Add typing information to IV models

  • Allow optimization parameters to be passed to IVGMMCUE

  • Removed internal use of pandas Panel

  • Improved performance in panel models when using from_formula()

  • Switched to retaining index column names when original input index is named

  • Modified tests that were not well conceived

  • Added spell check to documentation build

  • Improve docstring for summary properties

Version 4.8

  • Corrected bug that prevented single character names in IV formulas

  • Corrected kappa estimation in LIML when there are no exogenous regressors

Version 4.7

  • Improved performance of Panel estimators by optimizing data structure construction

Version 4.6

  • Added a license

Version 4.5

  • Added System GMM estimator

  • Added automatic bandwidth for kernel-based GMM weighting estimators

  • Cleaned up HAC estimation across models

  • Added predict method to IV, Panel and System model to allow out-of-sample prediction and simplify retrieval of in-sample results

  • Fixed small issues with Fama-MacBeth which previously ignored weights

Version 4.0

  • Added Seemingly Unrelated Regression (SUR) Estimator

  • Added Three-stage Least Squares (3SLS) Estimator

Version 3.0

  • Added Fama-MacBeth estimator for panels

  • Added linear factor models for asset pricing applications

    • Time-series estimation using traded factors

    • 2- and 3-step estimation using OLS

    • GMM Estimation

Version 2.0

  • Added panel models – fixed effects, random effects, between, first difference and pooled OLS.

  • Addition of two-way clustering to some of the IV models (2SLS, LIML)

Version 1.0

  • Added Instrumental Variable estimators – 2SLS, LIML and k-class, GMM and continuously updating GMM.