Forthcoming research seminars

First semester of the academic year 2017/18

• 13 November 2017: ‘Combining forecast”, prof. Stephen Hall, at 12:00, Room 21.

  The paper proposes a simulation-based approach to multi-step probabilistic forecasting, applied for predicting the probability and duration of negative inflation. The essence of this approach is in counting runs simulated from a multivariate distribution representing the probabilistic forecasts, which enters the negative inflation regime. The marginal distributions of forecasts are estimated using the series of past forecast errors, and the joint distribution is obtained by a multivariate copula approach. This technique is applied for estimating the probability of negative inflation in China and its expected duration, with the marginal distributions computed by fitting weighted skew-normal and two-piece normal distributions to ARMA ex-post forecast errors and using the multivariate Student-t copula.

• 20 November 2017: Svetlana Makarova (University College London and Vistula University), ‘European Central bank footprints on the inflation forecast uncertainty’

  The finding of the paper shows the relative effectiveness of the ‘one size fits all’ policy of the European Central Bank. The paper provides strong evidence in favour of this by testing whether the monetary policy effects (footprints), found in inflation uncertainty converge to a common level. These footprints are measured as the fraction of the estimated policy-induced reduction in this uncertainty. The testing was conducted by applying a bootstrap-type test in a regression of the rate of growth of these fractions on their initial values, computed for 16-euro area countries.

• 27 November 2017: Rafał Raciborski (Vistula University), Modelling output gaps with structural and financial cycles” (coauthors Christophe Planas and Alessandro Rossi)

  Modelling output gaps with structural and financial cycles with Christophe Planas and Alessandro Rossi

  We build an alternative econometric framework for empirical analysis of business cycles. The proposed framework differs from more traditional approaches in that the cycle is modelled as driven not by one, but by two independent latent components, called here the structural and financial cycle. The structural component can be informally thought of as being associated with conventional cyclical indicators such as capacity utilization, inflation or short-term unemployment. As such, it corresponds to the traditional concept of output gap, as routinely calculated by governments, central banks and international organizations such as the IMF or the European Commission. Similarly, financial cycle can be identified as this component that captures the bulk of autonomous fluctuations around trends of financial variables such as credit or house prices. Hence, it can be loosely associated with Borio et al.’s (2013) “finance-neutral gap”.

  We find that both types of cycles explain a non-negligible part of business cycle volatility. However, recently, the contribution from the financial cycle has been increasing, to find the climax during the last crisis. We further find that the conventional structural output gap indicators, such as unemployment rate or price and wage inflation rates carry information about the structural, but no the financial cycle. These two facts together may explain why models of output gap used by most renowned international institutions (OECDIMF etc.), which traditionally employ only the former indicators, failed to identify a boom in the economy before the crisis and underestimated the bust in its aftermath. Finally, we also investigate how the two estimated cyclical components relate to other macroeconomic aggregates like consumption, non-residential and residential investment, employment and so on. We find that the financial component contributes to explaining variation in consumption, residential investment and employment in construction. We use these findings to suggest new indicators that may be useful in estimating output gaps in real time.