The synthetic control method is widely used in comparative case studies to adjust for differences in pre-treatment characteristics. A major attraction of the method is that it limits extrapolation bias that can occur when untreated units with different pre-treatment characteristics are combined...

Linear regressions with period and group fixed effects are widely used to estimate treatment effects. We show that they identify weighted sums of the average treatment effects (ATE) in each group and period, with weights that may be negative. Due to the negative weights, the linear regression...

To estimate causal effects from observational data, an applied researcher must impose beliefs. The instrumental variables exclusion restriction, for example, represents the belief that the instrument has no direct effect on the outcome of interest. Yet beliefs about instrument validity do not...

Motivated by a recent literature on the double-descent phenomenon in machine learning, we consider highly over-parameterized models in causal inference, including synthetic control with many control units. In such models, there may be so many free parameters that the model fits the training data...

In this paper, I develop a new identification method to solve the problem of simultaneous equations, based on heteroskedasticity of the structural shocks. I show that if the heteroskedasticity can be described as a two-regime process, then the system is just identified under relatively weak...

This paper examines demand systems where the demand for a good depends on other prices only through a common price aggregator (a scalar function of all prices). We refer to this property as ``generalized separability'' and provide the functional forms of demand that this property implies when...

We propose and evaluate a technique for instrumental variables estimation of linear models with conditional heteroskedasticity. The technique uses approximating parametric models for the projection of right hand side variables onto the instrument space, and for conditional heteroskedasticity and...

We develop a framework to assess how successfully standard times eries models explain low-frequency variability of a data series. The low-frequency information is extracted by computing a finite number of weighted averages of the original data, where the weights are low-frequency trigonometric...

We estimate the interrelationships among economic institutions, political institutions, openness, and income levels, using identification through heteroskedasticity (IH). We split our cross-national dataset into two sub-samples: (i) colonies versus non-colonies; and (ii) continents aligned on an...

Movements in the prices of different assets are likely to directly influence one another. This paper develops a model that identifies the contemporaneous interactions between asset prices in U.S. financial markets by relying on the heteroskedasticity in their movements. In particular, we...