No abstract

The class of Functional Signal plus Noise (FSN) models is introduced that provides a new, general method for modelling and forecasting time series of economic functions. The underlying, continuous economic function (or 'signal') is a natural cubic spline whose dynamic evolution is driven by a cointegrated vector autoregression for the ordinates (or 'y-values') at the knots of the spline. The natural cubic spline provides flexible cross-sectional fit and results in a linear, state space model. This FSN model achieves dimension reduction, provides a coherent description of the observed yield curve and its dynamics as the cross-sectional dimension N becomes large, and can feasibly be estimated and used for forecasting when N is large. The integration and cointegration properties of the model are derived. The FSN models are then applied to forecasting 36-dimensional yield curves for US Treasury bonds at the one month ahead horizon. The method consistently outperforms the Diebold and Li (2006) and random walk forecasts on the basis of both mean square forecast error criteria and economically relevant loss functions derived from the realised profits of pairs trading algorithms. The analysis also highlights in a concrete setting the dangers of attempts to infer the relative economic value of model forecasts on the basis of their associated mean square forecast errors.

We present new evidence on the macroeconomic effects of changes in microprudential bank capital requirements, using confidential regulatory data from the Basel I and II regimes in the United Kingdom. Our central result is that an increase in capital requirements lowered lending to firms and households, reduced aggregate expenditure and raised credit spreads. A financial accelerator effect is found to have amplified the macroeconomic responses to shifts in bank credit supply. Results from a counterfactual experiment that links capital requirements to house prices and mortgage spreads indicate that tighter macroprudential policy would have had a moderating effect on house price and mortgage lending growth in the early 2000s, with easier monetary policy acting to offset its contractionary effects on output.

We develop a macroeconomic model in which commercial banks can offload risky loans to a 'shadow' banking sector, and financial intermediaries trade in securitized assets. We analyze the responses of aggregate activity, credit supply and credit spreads to business cycle and financial shocks. We find that: interactions and spillover effects between financial institutions affect credit dynamics; high leverage in the shadow banking system makes the economy excessively vulnerable to aggregate disturbances; and following a financial shock, stabilization policy aimed solely at the securitization markets is relatively ineffective.

Several recent papers have found that exogenous shocks to spreads paid in corporate credit markets are a substantial source of macroeconomic fluctuations. An alternative explanation of the data is that spreads respond endogenously to expectations of future default. We use a simple model of bond spreads to derive sign restrictions on the impulse-response functions of a VAR that identify credit shocks in the bond market, and compare them to results from a benchmark recursive VAR. We find that credit market shocks cause a persistent decline in output, prices and policy rates. Historical decompositions clearly show the negative effect of adverse credit market shocks on output in the recent recession. The identified credit shocks are unrelated to exogenous innovations to monetary policy and measures of bond market liquidity, but are related to measures of risk compensation. In contrast to results found using the benchmark restrictions, our identified credit shocks account for relatively little of the variance of output. Our results are consistent with a role for shocks in financial crises, but also with a lesser but non-zero role in normal business fluctuations.

We develop a macroeconomic model in which commercial banks can offload risky loans to a "shadow" banking sector, and financial intermediaries trade in securitized assets. The model can account both for the business cycle comovement between output, traditional bank, and shadow bank credit, and for the behavior of macroeconomic variables in a liquidity crisis centered on shadow banks. We find that following a liquidity shock, stabilization policy aimed solely at the market in securitized assets is relatively ineffective.JEL codes: E32, E44, G21, G23

We develop a macroeconomic model in which commercial banks can offload risky loans to a “shadow” banking sector, and financial intermediaries trade in securitized assets. The model can account both for the business cycle comovement between output, traditional bank, and shadow bank credit, and for the behavior of macroeconomic variables in a liquidity crisis centered on shadow banks. We find that following a liquidity shock, stabilization policy aimed solely at the market in securitized assets is relatively ineffective.

We establish a set of novel empirical facts concerning cross-section distributions of inflation expectations reported in surveys. Almost all the variation in expectations about their mean may be summarized via three factors we call disagreement, skew, and shape. We adopt a functional principal component regression approach to estimating forward-looking models of inflation that exploits the heterogeneity present in individual-level data. By using survey information more effectively, our approach reveals an enhanced role for expectations in inflation dynamics that is robust to lagged inflation, trend inflation, and supply factors. Our findings hold in similar form across two major economies.