We are announcing the following dates for our upcoming webinars. Please note that all webinars run 11.30am-12.30pm, and that some descriptions and titles are still to be confirmed. Look out for further details on our events page escoe.ac.uk/events.
14 January: Martin Weale (King’s College London and ESCoE)
‘Household Cost Indices’
This talk explores how far it is possible to provide a theoretical framework for the Household Cost Indices. Four features are identified which distinguish the index from conventional consumer price indices: i) the index is calculated giving equal weight to each household’s expenditure pattern (democratic weights); ii) insurance premia are treated gross rather net of claims; iii) interest payments are included as a cost and iv) goods and services are accounted for when they are paid for rather than when they are consumed. Points i) and ii) are strongly supported. It is suggested that for theoretical coherence iii) needs to be expanded to include interest receipts as well as payments while noting that with democratic weights this may have little practical effect. Point iv) raises a number of questions. A coherent framework representing the life-time cost of consumption correctly would need to include payments made ahead of future consumption (saving) as well as payments made ex post (repayment of debt). At present the only expenditure item subject to the principles of iv) is higher education; the student loan scheme has many of the characteristics of a tax and the treatment in the household cost indices can be defended on those grounds. ONS intends to produce a variant of the index which reflects the capital costs of housing and some thoughts are offered on measurement of these.
28 January: Giordano Mion (University of Sussex and ESCoE) and Manuel Tong (NIESR and ESCoE)
‘The Impact of Offshore Profit Shifting on the Measurement of GDP: The Case of the UK’
In this talk we analyse the global distribution of profits declared by MNEs operating in the UK using the Orbis database. Our investigations cover the period 2007-2017 and focus on entities reporting non-consolidated accounts and belonging to corporate Global Ultimate Owners active worldwide. Our analyses suggest that, compared to actual declared profits, profits distributed according to a simple apportionment rule based on companies’ revenues shares within each MNE group would look quite different. In particular, MNEs operating in the UK reported in 2017 41 billion GBP (representing about 1.91% of UK GDP) more than what they would have reported based on our apportionment rule. In this light, the UK was in 2017 a net winner in terms of global MNEs’ profit shifting. The situation was actually reversed back in 2007, with MNEs operating in the UK reporting less profits than those arising from our apportionment rule. A closer inspection of the whole period 2007-2017 reveals a smooth change with the UK moving from a loser to a winner position mainly through changes in declared profits of UK-owned MNEs. We subsequently extend the analysis by examining industry-specific patterns and conduct a number of robustness checks concerning the apportionment rule and the companies involved in the analysis while pointing to a number of limitations of our approach related to difficulties arising in dealing with Crown Dependencies, Branches, Special Purpose Entities and Family Trusts.
11 February: Ivan Petrella (University of Warwick and ESCoE)
‘Modelling and Forecasting Macroeconomic Downside Risk’
We document a substantial increase in downside risk to US economic growth over the last 30 years. By modelling secular trends and cyclical changes of the predictive density of GDP growth, we recover an accelerating decline in the skewness of the conditional distributions, with significant, procyclical variations. Decreasing trend-skewness, turning negative in the aftermath of the Great Recession, is associated with the long-run growth slowdown stared in the early 2000s. Short-run skewness fluctuation imply negatively skewed predictive densities ahead, and during recessions, often anticipated by deteriorating financial conditions, while positively skewed distributions characterise expansions. The model delivers competitive out-of-sample (point, density and tail) forecasts, improving upon standard benchmarks, due to financial conditions providing strong signals of increasing downside risk.
25 February: Andrew Clark (Paris School of Economics)
‘COVID-19, Lockdowns and Well-Being: Evidence from Google Trends’
11 March: Thies Lindenthal (University of Cambridge)
‘Machine learning’ (title tbc)
25 March: James Mitchell (Federal Reserve Bank of Cleveland and ESCoE)
‘Censoring of the Bank of England Density Forecasts’