Measuring the aggregate effects of Unconventional Monetary Policies

Lead Research Organisation: King's College London
Department Name: School of Management and Business


In the summer of 2007, the US economy experienced a financial meltdown in which the burst of real estate bubble in conjunction with vast and growing imbalances played a significant role. This financial crisis is considered to be the worst since "The Great Depression", as its effects rippled throughout the global economy. At this point, the long-lasting beliefs about the role of Central Banks and existing policy instruments were challenged.
During "normal times", the monetary policy rate (the rate of the main refinancing operations) is the usual benchmark for other interest rates relevant to firms and households. Changes in the rate by Central Banks, normally cause subsequent changes in the financing costs of households and firms. However, during times of severe disruption within the financial markets, a variety of problems emerge in the transmission channels, rendering the monetary policy inefficient.
Despite the attempts of the Central Banks to mitigate recession by manipulating interest rates, it soon became evident that the conventional monetary policy toolkit was not influential enough, to restore the economic welfare.
My aim is to evaluate the non-standard policy tools that the Central Banks [the European Central Bank (ECB), Federal Reserve (Fed) and Bank of England ( BOE)] executed in order to boost economic activity from the onset of the financial crisis. In particular, for each currency union, I will assess the measures undertaken and their joint impact on key macroeconomic variables. Furthermore, I will give critical focus on how the transmission channels of those actions performed in the restoration process of the impaired banks.
To estimate the impact of these policies on the economy, I will use the Bayesian Methods which are widely known in macroeconomic modeling. The implementation of a Bayesian VAR model by incorporating large data sets of economic variables should yield to meaningful inferences. This type of models is useful when analysing interconnections between changes in interest rates and economic activity (Kapetanios et al. 2009). In addition, the sign restrictions on the impulse responses can capture to a great extent the shocks caused by monetary policy (Baumeister and Benati 2010).
Another crucial issue is whether there are changes in the transmission mechanism of the policies over time. I propose to allow time variation using a time-varying structural model as Uhlig (2005) which can, also, shed light on how the economy reacted and to what extent over time to those shocks.
My approach to enhance the communication tool of the Central Banks will be by incorporating the "news" series to a standard VAR, and ordering it first. Perotti (2011) has called these models "Expectational VARs".
To make my results robust to the Federal Reserve Boards monetary policy, I will possibly use the federal funds' shadow rate which captures unconventional measures such as QE in the zero lower bound environment (Wu and Xia 2016).
The proposed research aims to examine the unconventional measures employed by Central Banks. By introducing the role of communication from Central Banks as an unconventional mechanism and formulating joint models that include all of the alternative measures, I expect to contribute to the academic literature and further expand the field of economics. This research, also, intends to shed light on whether unconventional monetary policies have the potential to become a standard tool for Central Banks and under which circumstances.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
ES/P000703/1 30/09/2017 29/09/2027
1916670 Studentship ES/P000703/1 30/09/2017 29/06/2021 Eleni Kalamara
Description There is a recent surge in data collection (Big Data), such as textual data, financial transactions, selected internet searches, surveys. Big Data may be able to aid in improving economic forecasts. However, traditional forecasting tools cannot handle the size and complexity inherent in Big Data. Econometricians need to refined numerous techniques from different disciplines to digest the ever-growing amount of data, avoid overfitting and improve forecast accuracy. I explore three important challenges on the use of machine learning and big data for macroeconomics and provide some proposals on ways forward.
First challenge is whether text data drawn from newspaper articles help us to forecast real economy variables applying high dimensional regressions and machine learning methods. Second challenge is whether and how to incorporate big datasets in models that account for the time series nature of macroeconomic data. Finally, a third challenge is to allow for machine learning models to model structural change.
Exploitation Route My research involves new econometric modelling techniques situated at the intersection of machine learning and econometrics. I propose new models that can help to advance our understanding of the current economic conditions by exploiting alternative data sets that can add significant value and used as another toolbox for policy making and macro economic research.
Sectors Creative Economy

Description Yes my research findings have been welcomed from policy institutions like the Bank of England. Collection and publication of official data are subject to substantial processing delays; For example in the United Kingdom the monthly index of industrial production (including manufacturing output) is published at least 40 days after the end of the month to which it refers to. Therefore, significant resources are devoted to exploit alternative sources of information in order to gauge the continuously evolving state of the real economy. As such, the use of timely and reliable information about current economic conditions is crucial for policy makers and expectations formation. By exploring alternative data sources like text data, policy makers can track in real time the economic activity and shape their monetary policy decisions.
First Year Of Impact 2019
Sector Creative Economy
Impact Types Policy & public services