Table 2

Results for sectoral corporate profits and climate risk nexus without the role of green innovation

SectorPhysical climate risksTransition climate risks
Profitt1CrisktLagPDL01PDL02Profitt1CrisktLagPDL01PDL02
Communication service0.7503*** (0.0808)−0.1750** (0.0850)2−0.4446* (0.2350)0.2695* (0.0157)0.7341*** (0.0792)0.2234*** (0.0837)20.6079*** (0.2165)−0.3844*** (0.1381)
Consumer discretionary0.6738*** (0.0844)0.0546*** (0.0204)20.1534** (0.0625)−0.0988** (0.0434)0.5980*** (0.0826)0.0549*** (0.0185)20.1806*** (0.0550)−0.1258*** (0.0384)
Consumer staple0.7593*** (0.0676)0.0380*** (0.0123)30.0624** (0.0245)−0.0244* (0.0126)0.7574*** (0.0704)0.0407*** (0.014620.1166*** (0.0435)−0.0759** (0.0305)
Energy0.1977* (0.1100)−0.4438* (0.2345)2−1.3809** (0.6928)0.9370** (0.4743)0.2045* (0.1057)0.4929*** (0.1679)21.3507*** (0.4753)−0.8577*** (0.3191)
Financial−0.0989 (0.1092)0.4839*** (0.1649)40.6892*** (0.2492)−0.2053*** (0.0907)−0.0662 (0.1122)0.4586*** (0.1593)30.8851*** (0.3185)−0.4265** (0.1647)
Health care0.7675*** (0.0662)−0.0298** (0.0152)2−0.1073*** (0.0398)0.0775*** (0.0268)0.7764*** (0.0656)−0.0237** (0.0102)4−0.0398** (0.0159)0.0160*** (0.0058)
Industrial0.5502*** (0.0909)−0.0224* (0.0121)4−0.0363** (0.0182)0.0139** (0.0066)0.5587*** (0.0904)−0.0285* (0.0166)2−0.0901** (0.0441)0.0615** (0.0283)
Information technology0.8450*** (0.0748)−0.0472 (0.0298)2−0.1575** (0.0778)0.1103** (0.0523)0.8182*** (0.0709)−0.0330** (0.0156)4−0.0622** (0.0245)0.0291*** (0.0093)
Material0.6747** (0.0938)0.0284** (0.0109)40.0483*** (0.0174)−0.0198*** (0.0068)0.6219*** (0.0881)−0.0254** (0.0103)4−0.0432*** (0.0160)0.0177*** (0.0059)
Real estate0.8850*** (0.0552)0.0088* (0.0048)20.0234* (0.0126)−0.0146* (0.0081)0.8902*** (0.0499)−0.0039 (0.0030)3−0.0104* (0.0059)0.0064** (0.0030)
Utility0.5321*** (0.0937)−0.0208 (0.0159)2−0.0844** (0.0419)0.0636** (0.0269)0.5584*** (0.0884)0.0243* (0.0132)40.0394* (0.0211)−0.0150* (0.0080)

Note(s): This table reports the ADL-MIDAS estimation results for the effect of climate risk on corporate profits without including control variables (e.g., technological innovations). For parsimony, the ADL-MIDAS model is estimated with a polynomial degree (PLD/Almon) of 2, or 3 when the lag length of 2 is found to be inefficient. The effect of each explanatory variable is obtained by summing the coefficients of the two polynomial degrees (PLD01 and PLD02), as determined using a Wald test. The lag length used corresponds to the optimal lag for the independent variable. Values in parentheses are standard errors. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively

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