Change Point Estimation in Panel Data with Time-Varying Individual Effects

This paper proposes a method for estimating multiple change points in panel data models with unobserved individual effects via ordinary least-squares (OLS). Typically, in this setting, the OLS slope estimators are inconsistent due to the unobserved individual effects bias. As a consequence, existing methods remove the individual effects before change point estimation through data transformations such as first-differencing. We prove that under reasonable assumptions, the unobserved individual effects bias has no impact on the consistent estimation of change points. Our simulations show that since our method does not remove any variation in the dataset before change point estimation, it performs better in small samples compared to first-differencing methods. We focus on short panels because they are commonly used in practice, and allow for the unobserved individual effects to vary over time. Our method is illustrated via two applications: the environmental Kuznets curve and the U.S. house price expectations after the financial crisis.Comment: 26 page

Three essays in econometric theory

This thesis consists of three essays in econometric theory. In the first essay, he considers a prediction problem with a large number of predictors. He improves the prediction precision of the standard factor model by allowing some variables to have idiosyncratic factors that are relevant for prediction. He selects idiosyncratic factors using a new model selection approach. In the second essay he studies two related tests of bivariate central symmetry. The asymptotic distributions of the two test statistics are established under rather weak conditions. He compares the finite sample performance of the test procedures with alternative tests by simulation. In the third and final essay, Zhuojiong proposes a pooled least-squares break point estimator for panel data. The estimator is shown to be consistent when the number of cross-sectional observations tends to infinity. Based on this break point estimator, he further proposes three consistent and asymptotically normally distributed slope estimators. The asymptotic variances of the three estimators are compared under a few simplifying assumptions

Testing for central symmetry

Omnibus tests for central symmetry of a bivariate probability distribution are proposed. The test statistics compare empirical measures of opposite regions. Under rather weak conditions, we establish the asymptotic distribution of the test statistics under the null hypothesis; it follows that they are asymptotically distribution-free. After a simple transformation of the data, tests for central symmetry can also be employed for testing exchangeability. In a simulation study the good finite sample performance of the test procedures is shown

Change point estimation in panel data with time-varying individual effects

Existing panel data methods remove unobserved individual effects before change point estimation through data transformations such as first-differencing. In this paper, we show that multiple change points can be consistently estimated in short panels via ordinary least squares. Since no data variation is removed before change point estimation, our method has better small-sample properties compared to first-differencing methods. We also propose two tests that identify whether the change points found by our method originate in the slope parameters or in the covariance of the regressors with individual effects. We illustrate our method via modeling the environmental Kuznets curve and the US house price expectations after the financial crisis