analyze_panel_iv_regression

source

analyze_panel_iv_regression(
    df,
    dv,
    endog,
    instruments,
    exog=None,
    *,
    entity=None,
    time=None,
    twoway=True,
    cluster_entity=True,
    format='gt',
)

Fit a panel IV (2SLS) regression absorbing entity (and time) fixed effects.

The panel analogue of :func:analyze_iv_regression and of Stata’s xtivreg2 ... fe: it instruments the endogenous regressor(s) while absorbing entity fixed effects (and, when twoway, time fixed effects), clustering by entity by default. The panel ids follow the usual expdpy resolution — explicit entity / time win, otherwise the pair declared by :func:expdpy.set_panel is used.

Parameters

Name	Type	Description	Default
df	pd.DataFrame	Data frame containing the data.	required
dv	str	Dependent (outcome) variable name.	required
endog	Sequence[str] \| str	Endogenous regressor name(s) to be instrumented.	required
instruments	Sequence[str] \| str	Excluded instrument name(s) (order condition: at least as many as `endog`).	required
exog	Sequence[str] \| None	Included exogenous regressor (control) names, if any.	`None`
entity	str \| None	Panel identifiers (unit and period). Resolved from :func:`expdpy.set_panel` when not passed; `entity` is required, `time` is needed only for the two-way specification.	`None`
time	str \| None	Panel identifiers (unit and period). Resolved from :func:`expdpy.set_panel` when not passed; `entity` is required, `time` is needed only for the two-way specification.	`None`
twoway	bool	If `True` (default) absorb both entity and time fixed effects; if `False` absorb only entity fixed effects (one-way).	`True`
cluster_entity	bool	If `True` (default) cluster the standard errors by `entity`.	`True`
format	Literal['gt', 'tex', 'md', 'df', 'html']	Output format for the rendered `etable` (see :func:`analyze_iv_regression`).	`'gt'`

Returns

Name	Type	Description
	IVRegressionResult	As :func:`analyze_iv_regression`, with the entity/time fixed effects absorbed.

Examples

Instrument night-time lights (a proxy for local economic activity) with lagged rainfall and drought across African regions, absorbing region and year fixed effects and clustering by region — a panel xtivreg2 fe. The first-stage F is well above 10:

import expdpy as ex
from expdpy.data import load_regional_conflict, load_regional_conflict_data_def

df = ex.set_labels(
    load_regional_conflict(), load_regional_conflict_data_def(), set_panel=True
)

result = ex.analyze_panel_iv_regression(
    df,
    dv="conflict",
    endog="log_lights_lag1",
    instruments=["rain_lag2", "drought_lag2"],
)
result.etable
result.first_stage_f
print(result.interpret())

This IV (2SLS) regression instruments **log_lights_lag1** with *rain_lag2, drought_lag2*, relating it to **conflict**. Two-stage least squares isolates the part of the endogenous regressor that moves with the instruments, purging the endogeneity that biases ordinary least squares.

- **log_lights_lag1** (instrumented): a one-unit increase is associated with conflict that is 0.296 lower (statistically significant at the 1% level).

The first-stage F is 25.3 — **above** the conventional weak-instrument threshold of 10, so the instruments are reasonably strong.
IV is trustworthy only when the instruments are both **relevant** (a strong first stage) and **excludable** — related to conflict solely through the instrumented regressor.

_These are associations, not causal effects. A causal reading needs a research design — see `explain('correlation_vs_causation')`._