Download or read book Three Essays in Macroeconomic Dynamics written by Hammad Qureshi and published by . This book was released on 2009 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: This dissertation examines theoretical and empirical topics in macroeconomic dynamics. A central issue in macroeconomic dynamics is understanding the sources of business cycle fluctuations. The idea that expectations about future economic fundamentals can drive business cycles dates back to the early twentieth century. However, the standard real business cycle (RBC) model fails to generate positive comovement in output, consumption, labor-hours and investment in response to news shocks. My dissertation proposes a solution to this puzzling feature of the RBC model by developing a theoretical model that can generate positive aggregate and sectoral comovement in response to news shocks. Another key issue in macroeconomic dynamics is gauging the performance of theoretical models by comparing them to empirical models. Some of the most widely used empirical models in macroeconomics are level vector autoregressive (VAR) models. However, estimated level VAR models may contain explosive roots, which is at odds with the widespread consensus among macroeconomists that roots are at most unity. My dissertation investigates the frequency of explosive roots in estimated level VAR models using Monte Carlo simulations. Additionally, it proposes a way to mitigate explosive roots. Finally, as macroeconomic datasets are relatively short, empirical models such as autoregressive models (i.e. AR or VAR models) may have substantial small-sample bias. My dissertation develops a procedure that numerically corrects the bias in the roots of AR models. This dissertation consists of three essays. The first essay develops a model based on learning-by-doing (LBD) that can generate positive comovement in output, consumption, labor-hours and investment in response to news shocks. I show that the one-sector RBC model augmented by LBD can generate aggregate comovement in response to news shock about technology. Furthermore, I show that in the two-sector RBC model, LBD along with an intratemporal adjustment cost can generate sectoral comovement in response to news about three types of shocks: i) neutral technology shocks, ii) consumption technology shocks, and iii) investment technology shocks. I show that these results hold for contemporaneous technology shocks and for different specifications of LBD. The second essay investigates the frequency of explosive roots in estimated level VAR models in the presence of stationary and nonstationary variables. Monte Carlo simulations based on datasets from the macroeconomic literature reveal that the frequency of explosive roots exceeds 40% in the presence of unit roots. Even when all the variables are stationary, the frequency of explosive roots is substantial. Furthermore, explosion increases significantly, to as much as 100% when the estimated level VAR coefficients are corrected for small-sample bias. These results suggest that researchers estimating level VAR models on macroeconomic datasets encounter explosive roots, a phenomenon that is contrary to common macroeconomic belief, with a very high frequency. Monte Carlo simulations reveal that imposing unit roots in the estimation can substantially reduce the frequency of explosion. Hence one way to mitigate explosive roots is to estimate vector error correction models. The third essay proposes a numerical procedure to correct the small-sample bias in autoregressive roots of univariate AR(p) models. I examine the median-bias properties and variability of the bias-adjusted parameters relative to the least-squares estimates. I show that the bias correction procedure substantially reduces the median-bias in impulse response functions. Furthermore, correcting the bias in roots significantly improves the median-bias in half-life, quarter-life and up-life estimates. The procedure pays a negligible-to-small price in terms of increased standard deviation for its improved median-bias properties.