New Keynesian models were once considered the workhorse of business cycle analysis. This view changed tremendously after the 2008 financial crisis. A common criticism is that DSGE models do not actively model a financial sector and financial constraints. However, Bernanke Gertler and Gilchrist (1999) developed a model with financial frictions. How does this model hold up to the data? Financial frictions emerge when consumers and firms are constrained in their ability to borrow, which leads to deeper recessions than with perfectly functioning financial markets. This dissertation project aims to clarify the shortcomings of this model and incorporating the lessons of the financial crisis.
Firstly, the model will be solved using a program written in C++ called Dynare++, that has been incorporated in python, using perturbation methods. Secondly, the model will be calibrated on macroeconomic data from the United Kingdom until 2008, NumPy, SciPy and Pandas will be used for this purpose. Then conditional forecasts will be made by estimating a Vector Autoregression (VAR) in python using the Pandas and Statsmodels libraries. The latter part of the dissertation will be devoted to analysing the causes of the discrepancy between the conditional forecasts and the actual behaviour of the economy during the financial crisis. The Bernanke model will be compared to other models that include financial frictions.
Attention will also be given to the matter in which the United Kingdom differed from the United States and the Eurozone in terms of the effect the crisis had and the policy response. The computation, simulation and data analysis will be integrated into an IPython notebook for the purpose of reproducibility.