Hypothesis Testing in Finite Samples With Time Dependent Data: Applications in Banking

Abstract

This thesis is concerned with hypothesis testing in models where data exhibits time dependence. The focus is on two cases where the dependence of observations across time leads to non-standard hypothesis testing techniques.

This thesis first considers models estimated by Generalized Method of Moments (GMM, Hansen (1982)) and the approach to inference. The main problem with standard tests are size distortions in the test statistics. An innovative resampling method, which we label Empirical Likelihood Block Bootstrapping, is proposed. The first-order asymptotic validity of the proposed procedure is proven, and a series of Monte Carlo experiments show it may improve test sizes over conventional block bootstrapping. Also staying in the context of GMM this thesis shows that the testcorrection given in Hall (2000) which improves power, can distort size with time dependent data. In this case it is of even greater importance to use a bootstrap that can have good size in finite samples.

The empirical likelihood is applied to a multifactor model of U.S. bank risk estimated by GMM. The approach to inference is found to be important to the overall conclusion about bank risk. The results suggest U.S. bank stock returns are sensitive to movements in market and liquidity risk.

In the context of panel data, this thesis is the first to my knowledge to consider the estimation of cost-functions as well as conduct inference taking into account the strong dependence of data across time. This thesis shows that standard approaches to estimating cost-functions for a set of Canadian banks lead to a downward bias in the estimated coefficients and therefore an upward bias in the measure of economies of scale. When non-stationary panel techniques are applied results suggest economies of scale of around 6 per cent in Canadian banking as well as cost-efficiency differences across banks that are correlated with size.