My thesis consists of three chapters. The first chapter uses the Factor-augmented Error Correction Model in model averaging for predictive regressions, which provides significant improvements with large datasets in areas where the individual methods have not. I allow the candidate models to vary by the number of dependent variable lags, the number of factors, and the number of cointegration ranks. I show that the leave-h-out cross-validation criterion is an asymptotically unbiased estimator of the optimal mean squared forecast error, using either the estimated cointegration vectors or the nonstationary regressors. Empirical results demonstrate that including cointegration relationships significantly improves long-run forecasts of a standard set of macroeconomic variables. I also estimate simulation-based prediction intervals for six real and nominal macroeconomics variables. The results are consistent with the point estimates, which further support the usefulness of cointegration in long-run forecasts.<div><br></div><div>The second chapter is a Monte Carlo study comparing the finite sample performance of six recently proposed estimation methods designed for large-dimensional regressions with endogeneity. The methods are based on combining shrinkage estimation with two-stage least squares (2SLS) or generalized method of moments(GMM), where both the number of regressors and instruments can be large. The methods are evaluated in terms of bias and mean squared error of the estimators. I consider a variety of designs with practically relevant features such as weak instruments and heteroskedasticity as well as cases where the number of observations is smaller/larger than the number of regressors/instruments. The consistency results show that the methods using GMM with shrinkage provide smaller estimation errors than the methods using 2SLS with shrinkage. Moreover, the results support the use of cross-validation to select tuning parameters if theoretically derived parameters are unavailable. Lastly, the results indicate that all instruments should correlate with at least one endogenous regressor to ensure estimation consistency.<br></div><div><br></div><div>The third chapter is coauthored with Mohitosh Kejriwal. We present new evidence on the nexus between democracy and growth employing the dynamic common correlated effects (DCCE) approach advanced by Chudik and Pesaran (2015), which is robust to both parameter heterogeneity and cross-section dependence. The DCCE results indicate a positive and statistically significant effect of democracy on economic growth, with a point estimate between approximately 1.5-2% depending on the specification. We complement our estimates with a battery of diagnostic tests for heterogeneity and cross-section dependence that corroborate the use of the DCCE approach.<br></div>