Direct and efficient estimation of bilinear forms in staggered tensor panels

We study the estimation of bilinear forms from noisy, partially observed tensor data.

The signal follows a Tucker2 model, with shared unit and time factors across tensor layers and slice-specific cores.

The missingness pattern is structured and motivated by staggered adoption designs, which are common in causal inference and related applications.

We first analyse the four-block missingness pattern, the basic building block for general staggered adoption, and propose a spectral algorithm that pools information across layers and targets the functional directly, rather than completing the entire tensor.

We prove a non-asymptotic mean squared error bound that exhibits a phase transition in the number of layers, showing when pooling improves estimation, and match it with a local minimax lower bound up to constants.

We then extend the construction to general staggered adoption designs via an anchored four-block reduction, and derive analogous theoretical guarantees.

Finally, we validate our theoretical findings through experiments on both simulated and real-world datasets.