Before revision:  We develop a new estimation algorithm that uses insights from treatment effects, panel, and time-series literature. We construct a variance estimatorthat is robust to arbitrary clustering pattern across geographical units. We achieve this by considering a finite population framework, where potential outcomes are treated as fixed, and all randomness comes from the exogenous shocks .
After revision:  We develop a new estimation algorithm that uses insights from treatment effects, panel, and time-series aspects of the data. Next, we develop a graphical tool and a novel test to illustrate the issues of the design using data from influential studies in development economics and macroeconomics. Motivated by these studies, we construct a new estimator, which is based on the time-series model for the aggregate shock. We analyze the statistical properties of our estimator in the practically relevant case, where both cross-sectional and time-series dimensions are of similar size .