4  Cheatsheet

This page provides an overview of the main functionalities of the fect package, including the required inputs, available plot types, key options, and testing capabilities across various methods.

4.1 Methods

Inputs for fect(methods = "___")

Method	Model for \(\hat{Y}(0)\)	References
fe	Standard two-way fixed effects model	Liu, Wang & Xu (2022), Borusyak, Jaravel & Spiess (2024)
ife	Interactive fixed effects model	Gobillon & Magnac (2016), Xu (2017)
mc	Matrix completion method	Athey et al. (2021)
polynomial	Two-way fixed effects model with unit-specific trends	–
cfe	“Complex” or multi-level fixed effects	–
gsynth	Interactive fixed effects model	Xu (2017)

4.2 Plot Types

Inputs for plot(fect_object, type = "___")

Type	Description	Applicable Methods
box	Box plot of ATT by period.	fe, ife, mc, gsynth, polynomial, cfe
calendar	ATT by calendar time.	fe, ife, mc, gsynth, polynomial, cfe
counterfactual	Observed vs. imputed outcome for treated units.	fe, ife, mc, gsynth
equiv	Pretreatment residuals with equivalence intervals.	fe, ife, mc, gsynth, polynomial, cfe
exit	Period-wise ATT relative to treatment exit.	fe, ife, mc, polynomial, cfe
factors	Estimated factors (factor-based methods).	ife, gsynth
gap	ATT by pre- and post-treatment periods.	fe, ife, mc, gsynth, polynomial, cfe
loadings	Estimated factor loadings (factor-based methods).	ife, gsynth
status	Treatment status by period for all units.	fe, ife, mc, gsynth, polynomial, cfe

4.3 Tests as Options

Inputs for fect(...)

Input	Description	Applicable Methods
loo	Leave-one-period-out goodness-of-fit test (TRUE/FALSE).	fe, ife, mc, gsynth
carryoverTest	Tests for carryover effects in post-treatment periods (TRUE/FALSE).	fe, ife, mc
carryover.period	Range of post-treatment periods for the carryover test (vector).	fe, ife, mc
permute	Permutation test (TRUE/FALSE).	fe, ife, mc, gsynth
m	Block length for permutation test (m=2 by default).	fe, ife, mc, gsynth
CV	Cross-validation for factor-based methods.	ife, mc, gsynth

4.4 Required and Optional Inputs

A check mark (✓) indicates that the method requires or accepts the input.

4.4.1 Required Inputs

Input	fe	ife	mc	gsynth	polynomial	cfe
Y (outcome)	✓	✓	✓	✓	✓	✓
D (treatment)	✓	✓	✓	✓	✓	✓
X (covariates)	✓	✓	✓	✓	✓	✓
data	✓	✓	✓	✓	✓	✓
index (unit & time IDs)	✓	✓	✓	✓	✓	✓
sfe (simple additive FEs)	–	–	–	–	–	✓
cfe (complex FEs)	–	–	–	–	–	✓

4.4.2 Optional Inputs

Input	fe	ife	mc	gsynth	polynomial	cfe
W (weight)	✓	✓	✓	✓	✓	✓
se (uncertainty)	✓	✓	✓	✓	✓	✓
nboots (# bootstrap reps)	✓	✓	✓	✓	✓	✓
force (FE structure)	✓	✓	✓	✓	✓	✓
lambda(Hyper-parameter sequence)	–	–	✓	✓	–	–
nlambda(Length of hyper-parameter sequence)	–	–	✓	✓	–	–
r (# factor)	–	✓	–	✓	–	–
k ( # cross-validation rounds)	–	✓	✓	✓	–	–
degree (degree of the interacted fixed effects)	–	–	–	–	✓	–

4.4.3 Important Options

Input	Options	Applicable Methods
force	Fixed effects structure: “none”, “unit”, “time”, “two-way” (default)	fe, ife, mc, poly, cfe, gsynth
vartype (when method = "gsynth")	Uncertainty estimator: “parametric” (default), “bootstrap”, “jackknife”	gsynth
vartype (otherwise)	Uncertainty estimator: “bootstrap” (default), “jackknife”	fe, ife, mc, polynomial, cfe
criterion	Model selection criterion: “mspe” (default), “gmspe”, “moment”, “pc”.	ife, mc, polynomial, cfe, gsynth

4.5 Notes

• Most methods share core inputs (Y, D, X, data, index).

• Method polynomial and cfe allow either “jackknife” or “bootstrap” for uncertainty estimates.

• Factor-based methods (ife, gsynth) can use rand k for factor selection and cross validation. We can implement a similar process using mc or gsynth with lambda andnlambda.

• carryoverTest and loo are available in fe, ife, mc, polynomial, and cfe but not in gsynth.

• force allows various levels of fixed effects ("none", "unit", "time", "two-way").

• fect includes several built-in values for optional arguments that often require an integer or boolean input. All boolean arguments (such as parallel, se, or CV) are set to FALSE by default to speed up computation. Below is a complementary table listing parameters that require numeric inputs.

Inputs	Description	Default
degree	The degree of the interacted fixed effects(int)	2
nboots	Number of bootstrap runs (int)	200
k	Sets the number of cross-validation rounds(int)	10
r	Sets the number of factors	0; c(0,5) when CV
m	Specifies the block length for the permutation test	2
nlambda	Length of hyper-parameter sequence	10

• With an integer input for nlambda, fect can randomly generate appropriate hyper-parameter sequence.

Enjoy using fect!

Athey, Susan, Mohsen Bayati, Nikolay Doudchenko, Guido Imbens, and Khashayar Khosravi. 2021. “Matrix Completion Methods for Causal Panel Data Models.” Journal of the American Statistical Association 116 (536): 1716–30.

Borusyak, Kirill, Xavier Jaravel, and Jann Spiess. 2024. “Revisiting Event-Study Designs: Robust and Efficient Estimation.” Review of Economic Studies 91: 3253–85.

Gardner, John. 2021. “Two-Stage Differences in Differences.” Working Paper, Boston College. https://bit.ly/3AF32Af.

Gobillon, Laurent, and Thierry Magnac. 2016. “Regional Policy Evaluation: Interactive Fixed Effects and Synthetic Controls.” Review of Economics and Statistics 98 (3): 535–51.

Liu, Licheng, Ye Wang, and Yiqing Xu. 2024. “A Practical Guide to Counterfactual Estimators for Causal Inference with Time-Series Cross-Sectional Data.” American Journal of Political Science 68 (1): 160–76.

Sun, Liyang, and Sarah Abraham. 2021. “Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects.” Journal of Econometrics 225 (2): 175–99.

Xu, Yiqing. 2017. “Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models.” Political Analysis 25 (1): 57–76.