• Financial Accelerator
• Business Cycle
• Search and Matching frictions

My aim is to build a New-Keynesian DSGE model embedded with both financial and labor market search and matching frictions. At the present moment, much of the literature has focused on either of those frictions but has not investigated their interaction at business cycle frequency. My work addresses this issue by building a model in which firms' borrowing from banks is affected by costly state verification. For this reason, the optimal amount of borrowing is increasing in the firms' net worth. This feature creates a positive feedback between the value of net worth, capital demand and output. At the same time, the total amount of workers is not free to adjust in every period. Instead, a fraction of the employees exogenously separate in every period, and some new workers are hired by firms. The incentive to hire is linked to the value to the firm of having an additional worker. Since such value is affected by the conditions in the capital market - given that, during depressions, optimal capital demand is low and viceversa - the hiring of new workers may be severely reduced in a downturn, and unemployment is likely to increase as a consequence.
In this regard, my work addresses the weak performance of search and matching models - that are not equipped with financial frictions - in replicating the high volatility of unemployment that can be found in the data. Furthermore, reactions of real variables to shocks have proved to be too fast in current financial accelerator models. The presence of frictions in the labor market may easily delay such responses - the total number of employees adjusts slowly - and improve the performance of this kind of models.