Joint Econometrics and Statistics Seminar Series

This event will take place in the Leverhulme Library (COL 6.14).

Title: TBC

Abstract: TBC

Bio: TBC

This event will take place in the Data Science Institute (COL.1.06).

Title: Consistent Causal Inference for High-Dimensional Time Series

Abstract: A methodology for high dimensional causal inference in a time series context is introduced. It is assumed that there is a monotonic transformation of the data such that the dynamics of the transformed variables are described by a Gaussian vector autoregressive process. This is tantamount to assuming that the dynamics are captured by a Gaussian copula. No knowledge or estimation of the marginal distribution of the data is required. The procedure consistently identifies the parameters that describe the dynamics of the process and the conditional causal relations among the possibly high dimensional variables under sparsity conditions. The methodology allows us to identify such causal relations in the form of a directed acyclic graph. As illustrative applications we consider the impact of supply side oil shocks on the economy, and the causal relations between aggregated variables constructed from the limit order book on four stock constituents of the S&P500.

Bio: TBC

This event will take place in the Data Science Institute (COL.1.06).
Title: Bayesian Optimal Experimental Design

Abstract: In order to use machine learning in a domain without pre-existing data, we must first gather data by conducting experiments. This presents an opportunity, because we can design experiments carefully to ensure we gather the most informative data. As we gather a little data, we can use this to guide the design of future experiments in an adaptive manner. Bayesian Optimal Experiment Design (BOED) is a mathematical framework that precisely defines the optimal experiment in a pipeline like this. Historically, though, BOED has been computationally too challenging to use in practice. In this talk, I discuss recent computational advances in the field that utilise variational inference, gradient-based optimisation and policy learning to overcome many of these computational bottlenecks. I will also touch on applications such as adaptive survey design and force-field learning in AI for science.

Bio: TBC

Title: The impact of job stability on monetary poverty in Italy: causal small area estimation

Abstract: Job stability refers to the security and predictability of employment, including factors such as long-term contracts, adequate wages, social security benefits, and access to training and career development opportunities. Stable employment can play a crucial role in reducing poverty, as it provides individuals and households with a stable income as well as improves their overall and subjective economic well-being. In this work, we leverage the EU-SILC survey and census data to assess the causal effect of job stability on monetary poverty across provinces in Italy. To this end, we propose a causal small area estimation (CSAE) framework for heterogeneous treatment effect estimation in which only a negligible fraction of outcomes is actually observed at the provincial level. Our estimators are more stable than the classical causal inference tools as they borrow strength from the other sources of data at the expense of additional modelling assumptions. On top of that, our new methodology proves to be successful in recovering provincial heterogeneity of the effect of job stability across six regions in Italy.

Bio: TBC