My dissertation investigates several long-standing issues in macro and international macro, specifically questions related to technological change, financial market imperfections and international risk sharing. The first two chapters analyze these issues in a closed economy model, while the third chapter studies these issues in an open economy model. The first chapter examines the role of credit market imperfections in propagating news of future productivity, both theoretically and empirically. The second chapter investigates the technology-hours debate in an economy buffeted by anticipated technology and fiscal policy shocks. The third chapter, jointly written with Yi Chen, examines the role of a recursive preference developed in Epstein and Zin (1989) in explaining the equity home bias puzzle in an otherwise standard two-country endowment-driven open macro model. Viewed as a whole, my dissertation is an effort to connect technological processes with financial markets in macro models in order to further our understanding of macro phenomena. The first chapter investigates the role of credit market imperfections in shaping the response of the economy to news of future productivity, and proposes an alternative view of how news shocks propagate through the economy. In contrast to the conventional wisdom about news of future productivity - that it generates strong booms in the short run - I develop a novel news-driven business cycle model in which credit market imperfections significantly dampen the short-run response of economic activity to news. To exploit the fact that news of future productivity generates an asymmetry between expected returns and the current financial conditions faced by firms, I model credit market frictions as arising from the agency cost problem. In contrast to the limited enforceability problem, the agency cost problem serves to dampen the short-run response of investment because the desire to increase investment due to the higher expected returns is offset by the endogenous rise in the external finance premium in the absence of an actual rise in productivity. This inertial behavior of investment is in turn transmitted to hours worked and final output through the general equilibrium effect. I then estimate the response of economic activity to news shocks using U.S. manufacturing data and find some suggestive evidence for the credit frictions mechanism presented in the model. The main empirical findings are as follows. First, economic activity exhibits a muted response to news shocks during anticipation periods and therefore tracks, rather than leads, the actual change in productivity. Second, news shocks explain a small fraction of output fluctuations. Finally, industries that are more dependent on external finance or exhibit more volatile idiosyncratic productivity growth appear to have a more dampened response to news shocks in the short run. The second chapter investigates the reliability of using the structural vector autoregression (SVAR) evidence on the response of hours to a technology shock to discriminate between two workhorse business cycle models: standard real business cycle models and sticky price models. Given growing attention to the role of news shocks in the business cycle literature, I evaluate the performance of the SVAR procedure when the true data generating process is driven by news shocks about future technology and fiscal policy. The main results are summarized as follows. First, when the SVAR procedure is applied to the data simulated from an economy with unanticipated shocks to the technology process, the estimated impulse responses have the same sign and qualitative pattern as the true responses. Second, when the SVAR procedure is applied to the data generated from an economy with news shocks to the technology process, the estimated impulse responses generally have a different qualitative pattern from the true responses, and frequently they produce opposite signs. The poor performance of the SVAR procedure largely comes from the anticipation of technology, whereas little is attributed to the anticipation of fiscal policy. Third, if the true data generating process is driven by conventional unanticipated technology shocks, a SVAR researcher can be confident about drawing the conclusion about model discrimination. However, if the true data generating process is driven by news about future technology but a researcher still uses the SVAR procedure based on the conventional information assumption, then the probability that a researcher draws the right conclusion about model discrimination falls dramatically. The third chapter, written jointly with Yi Chen, investigates the role of a recursive preference developed in Epstein and Zin (1989) (EZ) in explaining the equity home bias puzzle, and shows that EZ preferences play a role of increasing the home equity share relative to standard CRRA preferences. This happens because EZ preferences generate a long-run risk hedging demand that contributes to a positive covariance between the relative expenditure and the excess equity return. As a result, the local equity is more likely to be a good asset since it pays off more when investors are willing to spend more. Additional main findings are as follows. First, using the least structural information, we show that the degree of equity home bias depends on the conditional covariance-variance ratio between the relative expenditure and the excess equity return, which nests as a special case the standard CRRA models' implication that the equity home bias depends on the conditional covariance-variance ratio between the real exchange rate and the excess equity return. Second, our model is an infinite-horizon model, while standard trade-cost-based explanations work within two-period models in which portfolio adjustment is impermissible by construction. Thus, our model gets the moment representations for the equity home bias right, while two-period trade-cost-based models assume away portfolio adjustment, thereby overstating the relationship between the real exchange rate and the excess equity return.