H2020Индивидуална стипендия2017–2020

DSGE-RD · Estimating a DSGE Model with Rare Disasters

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период
2017-08-01 → 2020-07-31
Финансиране от ЕС
263 385 €
Участници
2
Схема
MSCA-IF

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Накратко на български

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Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Estimating a DSGE Model with Rare Disasters

The global financial crisis in 2008 was followed by a severe economic recession, which is now called "The Great Recession". The Great Recession had three unique properties: First, it was unusually large in terms of the size of overall economic contraction. Second, by various measures (e.g. unemployment rate), the recovery period was particularly long, lasting roughly a decade. Third, standard monetary policy was useless, because interest rates fell to zero, so central banks could not lower their interest rates furthermore. Before the crisis, the dominating macroeconomic theory focused on small economic fluctuations around a stable trend, generally called "Business Cycle Models". These models were used to understand regular cycles of booms and recessions, but they failed to explain the causes and consequences of large macroeconomic shocks such as the Great Recession. The objective of the present project is to develop new models that are more suitable for studying large macroeconomic recessions. These models are inherently nonlinear. Namely, they generate different economic effects for large versus small shocks. For instance, the central bank can lower the interest rate in response to a small economic shock, but when the shock is too large the interest rate hits the zero bound and can no longer be used to stimulate the economy. This type of nonlinearity is associated only with large shocks. Importantly, it introduces new computational challenges that have not yet been fully resolved. The traditional tools applied to the standard business cycle models were based on linear techniques. These methods are not suitable for models that feature large shocks such as the Great Recession. The goal of this project is to develop new methodological tools that would enable to study macroeconomic models with large shocks. The project extends our previous work to two types of nonlinearities that have played a role in the Great Recession. The first kind of nonlinearity is called “Regime-Switching Parameters”. This concept refers to a temporary change in the model parameters that lasts for a certain period and then changes back. For instance, a decline in GDP growth for the duration of the recession and then a recovery of the growth rate to the pre-crisis level. The second form of nonlinearity is generated by “Occasionally Binding Constraints”. The most common example is a lower bound that restricts the interest rate from falling below zero. This constraint is ignored in regular times when the interest rate is high. However, in severe recessions the interest rate may fall to zero. Then, the constraint kicks in, making monetary policy useless. The methodological contributions of this project would allow economists to study unusual economic conditions such as those prevailing in the Great Recession. Hopefully, our understanding of severe recessions would improve our economic system by designing policy tools to prevent those events and mitigate their impact once they occur.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

This project will estimate a DSGE model with rare disasters using data from the Great Recession. Previous studies on rare disasters have been confined mainly to endowment economies. Extensions to production economies (business cycle models) have been limited. In particular, DSGE models with rare disasters have not been estimated before. The main obstacle is computational, as these models are very difficult to solve. In a recent paper with Jesus Fernandez-Villaverde we have shown that the computational problem can be resolved by a new solution method called ""Taylor Projection"", which is derived in my paper Levintal (2016). This method is able to solve large DSGE models with rare disasters in a few seconds only. This opens up the possibility of estimating rich DSGE models with rare disasters and conducting policy analysis.The project will implement the new ""Taylor Projection"" method to solve and estimate a DSGE model with rare disasters for the period of the Great Recession. This will require to extend the current solution method to allow for a ZLB constraint on the interest rate and for Markov-switching parameters, as described in the proposal. An estimated model with rare disasters may help to explain the extraordinary dynamics of the Great Recession. We conjecture that the interaction of a big macroeconomic shock (disaster shock) with the ZLB constraint played a significant role in the recession. The question is what kind of a shock generated the recession. Was it a permanent drop in long term growth, as suggested by the ""Secular Stagnation"" hypothesis of Summers (2014)? or an uncertainty shock that increased the demand for safe assets, in line with the ""Safe Assets"" view of Caballero and Farhi (2014)? or a combination of both? The proposed project will shed light on these questions by studying the Great Recession through an estimated DSGE model with rare disasters.""

Оригинален текст от CORDIS (на английски).

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Данни: CORDIS, © Европейски съюз