*! version 1.0.2 24may2026

Title

asycaus efficient -- Hatemi-J (2024) efficient asymmetric causality tests (SUR-based)

Syntax

asycaus efficient depvar causvar [if] [in] [, options]

Description

Hatemi-J (2024) argues that running two independent VARs on the positive
and negative components is inefficient because residuals across the two
systems are correlated. He proposes a seemingly unrelated regression
(SUR) joint estimation that exploits the cross-equation covariance. Within
that single system four hypotheses are tested:

H1. No causality from causvar to depvar via POSITIVE shocks

H2. No causality from causvar to depvar via NEGATIVE shocks

H3. Joint no causality (H1 AND H2)

H4. Equality of positive and negative causal coefficients — the key asymmetry test introduced by the paper. Rejection means the positive and negative causal mechanisms differ in size.

Options

maxlag(#) max VAR lag (default 8)

ic(string) IC (default hjc)

intorder(#) TY augmentation lags (default 1)

lnform log of inputs

nograph suppress graph

saving(name) save graph

Examples

webuse lutkepohl2, clear

tsset qtr

asycaus efficient dln_inv dln_inc, maxlag(4)

Stored results

r(Wpos) r(p_pos) Wald and p-value for H1: no Pos causality

r(Wneg) r(p_neg) H2: no Neg causality

r(Wjoint) r(p_joint) H3: joint

r(Wdiff) r(p_diff) H4: equality Pos = Neg

r(dof) degrees of freedom (= lag p)

References

Hatemi-J, A. (2024). Efficient Asymmetric Causality Tests. arXiv 2408.03137.

Author

Dr Merwan Roudane -- merwanroudane920@gmail.com

See asycaus for the package overview.