Please click on each project to find out more.
Please click on each project to find out more.
We study the implications of multi-period mortgage loans for monetary policy, considering several realistic modifications — fixed interest rate contracts, lower bound constraint on newly granted loans, and possibility for the collateral constraint to become slack — to an otherwise standard DSGE model with housing and financial intermediaries. We estimate the model in its nonlinear form and argue that all these features are important to understand the evolution of mortgage debt during the recent US housing market boom and bust. We show how the nonlinearities associated with the two constraints make the transmission of monetary policy dependent on the housing cycle, with weaker effects observed when house prices are high or start falling sharply. We also find that higher average loan duration makes monetary policy less effective, and may lead to asymmetric responses to positive and negative monetary shocks.
This paper examines the effects of unconventional monetary policy measures by the European Central Bank on nine European countries not adopting the euro with a novel Bayesian mixed-frequency structural vector autoregressive technique. Unconventional monetary policy disturbances generate important domestic fluctuations. The wealth, the risk, and the portfolio rebalancing channels matter for international propagation; the credit channel does not. The responses of foreign output and inflation are independent of the exchange rate regime. International spillovers are larger in countries with more advanced financial systems and a larger share of domestic banks. A comparison with conventional monetary policy disturbances and with announcement surprises is provided.
We develop a dynamic stochastic general equilibrium framework that can account for important macroeconomic and financial moments, given Epstein-Zin preferences, heterogeneous banking and third-order approximation methods that yield a time-varying term premium that feeds back to the real economy. A risk perception shock increases term premia, lowers output, and reduces short-term credit in the private sector in response to higher loan rates and constrained borrowers, as banks rebalance their portfolios. A ‘bad’ credit boom, driven by investors mispricing risk, leads to a more severe recession and is less supportive of economic growth than a ‘good’ credit boom based on fundamentals.
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We use data on monthly patent applications to construct an external instrument for identification of technology news shocks in an information-rich VAR. Technology diffuses slowly and affects total factor productivity in an S-shaped pattern. Responsible for about a tenth of economic fluctuations at business cycle frequencies, the shock elicits a slow, large and positive response of quantities, and a sluggish contraction of prices followed by an endogenous easing of the monetary stance. The ensuing economic expansion substantially anticipates any material increase in TFP. Technology news shocks are strongly priced into the stock market on impact, but measures of consumer expectations take sensibly longer to adjust, consistent with a New-Keynesian framework with nominal rigidities, and featuring informationally constrained agents.
The 2008 financial crisis has shown that financial busts can influence the real economy. However, there is less evidence to suggest that the same holds for financial booms. Using a Markov-Switching vector autoregressive model and euro area data, I show that financial booms tend to be less procyclical than financial busts. To identify the sources of asymmetry, I estimate a non-linear DSGE model with a heterogeneous banking sector and an occasionally binding borrowing constraint. The model matches the key features of the data and shows that the borrowers’ balance sheet channel accounts for the asymmetry in the macro-financial linkages. The muted macro-financial transmission during financial booms can be exploited for macroprudential policies. By comparing capital buffer rules with monetary policy ‘leaning-against-the-wind’ rules, I find that countercyclical capital buffers improve welfare.
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Financial crises are extreme economic events and very challenging to predict. Part of the difficulty is the infrequent and non-linear nature that these crises occur. We run a horse race between various machine learning models and a benchmark logit model. We compare how well machine learning algorithms perform in predicting financial crisis in a macroeconomic dataset covering 17 countries between 1870 – 2013. The machine learning models, particular ensemble models of decision trees, outperform the simple linear logistic regression framework. To address the black-box critique of machine learning models, we extract the main indicators out of these models using the Shapley regression framework. Credit to GDP growth, especially on the global level, turns out to be the best predictor of a crisis, alongside the slope of the yield curve.