Advancing Inferential Analysis with Economic Data

Improved quantitative methods for economic and financial analysis
The rapid increase in the scope and complexity of economic and financial datasets has led to a need for improved inferential tools for economists. We are at the forefront of developing these tools for the modern era of complex data.

The modern information age has seen an explosion in the availability of data relevant for economic and financial decision making, and in the computing power available to model and process such data.

Richer data enables  deeper insights into economic phenomena, but only through the application of suitable methods of inferential analysis.

The development of quantitative methods tailored to the modern data environment and making appropriate use of economic and financial theory is a core focus of our research.

Such methods should allow analysts to not only draw reasonable conclusions from data, but also provide realistic assessments of the reliability of those conclusions.

One area of research which has been particularly affected by the increased availability of data and computing power is the modelling of asset prices, and associated methods of portfolio choice.

Models of daily asset prices which were cutting edge at the turn of the century have been replaced with models which track prices by the second or even faster.

At such high frequencies, the noisiness of price movements poses challenges for reliable inference. Markets of financial derivatives continue to increase in complexity, posing computational challenges to traditional methods of portfolio choice.

Our researchers make use of cutting-edge developments in mathematical statistics, computer science and operations research to develop quantitative methods suitable for financial analysts choosing between an imposing array of investment possibilities at high frequency.

Macroeconomic modelling has also been profoundly affected by advances in data availability and computing power.

Whereas  macroeocnomists once relied on infrequent observation of macroeconomic aggregates such as economic output, inflation and unemployment,  we now have access to a wealth of information on the behaviour of individual consumers and producers.

Large scale models of numerous idiosyncratic consumers and producers interacting with one another have replaced the traditional representative agent models. This has led to an increased focus on the distribution of economic outcomes, as opposed to the aggregate of economic outcomes.

We want to know not only the unemployment rate, but which people are affected by unemployment; not only the rate of wage growth, but whose wages are growing and whose are falling; not only the total wealth in an economy but how that wealth is distributed across individuals.

A focus of research in the School of Economics is on the development of quantitative methods which yield deeper insights into distributional outcomes of this kind.

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