Three Essays in Business Cycles - Karimzada, Muhebullah (My University, 2023)
In chapter one of the thesis, we incorporate shocks to the efficiency with which firms learn from production activity and accumulate knowledge into an otherwise standard real DSGE model with imperfect competition. Using real aggregate data and Bayesian inference techniques, we find that learning efficiency shocks are an important source of observed variation in the growth rate of aggregate output, investment, consumption and especially hours worked in post-war US data. The estimated shock processes suggest much less exogenous variation in preferences and total factor productivity are needed by our model to account for the joint dynamics of consumption and hours. This occurs because learning efficiency shocks induce shifts in labour demand uncorrelated with current TFP, a role usually played by preference shocks which shift labour supply. At the same time, knowledge capital acts like an endogenous source of productivity variation in the model. Measures of model fit prefer the specification with learning efficiency shocks. The results are robust to the addition of many observables and shocks. In chapter 2, I estimate a "Learning-by-doing'' model with "Learning efficiency shocks'' using Bayesian estimation techniques and real aggregate data from Euro Area. I find that learning efficiency shocks explain a large fraction of the fluctuations in the growth rate of real aggregate variables such as consumption, output, investment and employment. This paper is the first to estimate a learning-by-doing model with learning efficiency shocks for the Euro Area and analyses its business cycles. In chapter 3, We study the impact of COVID 19 pandemic on the Canadian housing market. The Canadian economy has been hit hard by the COVID-19 pandemic like almost every other country in the World. The residential real estate market that makes a significant contribution to the Canadian economy however behaved far differently in the wake of the COVID-19 downturn. Unlike previous recessions, housing market recovered much faster and house prices steadily increased from 2020:QII. Since the pandemic has started, working from home (WFH) has become more prevalent. How important is WFH in producing large swings in house prices as observed in the data? To address this question, we estimate an augmented New Keynesian model with collateralized household debt and remote working condition. We argue that remote working condition improves the performance of the model, particularly explaining the house price dynamics in the last two years.
Keywords: Business Cycles, Knowledge Capital, Learning Efficiency Shocks, Housing Investment, COVID-19