This book demonstrates the challenges of modern econometric practice and the role that computer software and hardware play in this field. It examines the properties of some tests for various types of non-stationarity that can arise with time-series data that are recorded semiannually.

1. Some Methodological Questions Arising from Large Data Sets 2. Finite-Sample Simulation-Based Tests in Seemingly Unrelated Regressions 3. Finding Optimal Penalties for Model Selection in the Linear Regression Model 4. On Bootstrap Coverage Probability with Dependent Data 5. A Comparison of Alternative Causality and Predictive Accuracy Tests in the Presence of Integrated and Cointegrated Economic Variables 6. Finite Sample Performance of the Empirical Likelihood Estimator Under Endogeneity 7. Testing for Unit Roots in Semiannual Data 8. Using Simulation Methods for Bayesian Econometric Models 9. Bayesian Inference in the Seemingly Unrelated Regressions Model 10. Computationally Intensive Methods for Deriving Optimal Trimming Parameters 11. Estimating and Testing Fundamental Stock Prices: Evidence from Simulated Economies 12. Neural Networks: An Econometric Tool 13. Real-Time Forecasting with Vector Autoregressions: Spurious Drift, Structural Change, and Intercept Correction 14. Econometric Modeling Based on Pattern Recognition via the Fuzzy C-Means Clustering Algorithm 15. Nonparametric Bootstrap Specification Testing in Econometric Models 16. The Effect of Economic Growth on Standard of Living: A Semiparametric Analysis