This dissertation investigates several business cycle relationships when economic agents are heterogeneous. The particular focus is on the interactions between the cross-section of agents and the aggregate state of the economy. The first chapter shows that, when occasionally binding capacity constraints limit the production of heterogeneous firms, demand shocks can endogenously generate a number of important business cycle regularities: recessions are deeper than booms are high, firm-level volatility is countercyclical, the aggregate Solow residual is procyclical and the fiscal multiplier is countercyclical. A baseline calibration of a basic New Keynesian DSGE model with capacity constraints shows that this mechanism can explain more than a quarter of the empirically observed asymmetry in output, and matches the cyclicality of firm-level profitability dispersion and of the measured Solow residual. The model implies fluctuations in the fiscal multiplier of around 0.12 between expansions and recessions. Chapter two takes a different approach to firm level uncertainty, exploring how recessions can cause an endogenous rise in firm risk. If heterogeneous firms face real and financial frictions, then a shock to the mean of aggregate productivity endogenously leads to countercyclical profitability risk through firms' heterogeneous responses in price setting. Additionally, the mechanism endogenously generates countercyclical credit spreads and credit spread dispersion. The model explains a large share of the observed fluctuations in profitability dispersion (69%) and in credit spreads (40%) through fluctuations in aggregate TFP holding productivity risk constant. This suggests that the scope for uncertainty shocks to explain recessions may be smaller than previously thought. The third chapter focuses on distributional effects of oil price shocks on the household side. In the model, household behavior replicates two patterns found in household-level data which show that gas consumption increases with income, but on the intensive margin gasoline consumption as a share of the household's budget decreases with income. The model includes gas consumption in household utility on top of a fixed minimum level of gas consumption. Calibrated simulations suggest that a shock to the gas price is almost twice as costly for relatively poor households than for relatively rich households.

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Why do some high-tech start-ups succeed and others do not ? This question on the performance of high-tech start-ups has received much attention in recent literature and practitioners' discussions. Prior literature has treated the question mainly in two ways: (1) by building holistic qualitative frameworks and (2) by building partial quantitative models. However, in order to make performance predictions these two approaches need to be brought together in a holistic and quantitative framework. The present study addresses this gap by exploring alternative mathematical methods for the prediction of the global prospects of high-tech start-ups, as well as for their investment readiness. For this purpose, prior literature on new venture performance is reviewed. The present study integrates the diverse prior results by the proposition of an alternative integrative framework. It is furthermore proposed that such a framework should integrate non-linear relationships. Three alternative research methods - linear multiple regression, cluster analyses, and Fuzzy Theory analyses - were carried out on a sample of 124 European high-tech start-ups. Comparison of the three methods suggests that an integrative framework is quantified best by Fuzzy Theory. First, the fuzzy models show substantially superior accuracy of the performance predictions over regression and cIuster analyses. Second, their generalizability and predictive power exceed those of the statistical methods. It is therefore shown that alternative analytical methods may improve the predictive power of entrepreneurial models. The results of the present study confirm that performance can be explained best by a holistic view. ln fact, performance is predicted best by models (1) involving salient factors from ail high-tech start-up domains; (2) involving interactions between ail salient factors; and (3) involving non-linearities of the performance relationships. The results are transferred onto the practical terrain by the development of a hightech start-up diagnostic tool- the Venture Coach. The study advances entrepreneurship theory and has important implications for the practical terrain.