Measuring output gap uncertainty

We propose a methodology for producing density forecasts for the output gap in real time using a large number of vector autoregessions in inflation and output gap measures. Density combination utilizes a linear mixture of experts framework to produce potentially non-Gaussian ensemble densities for the unobserved output gap. In our application, we show that data revisions alter substantially our probabilistic assessments of the output gap using a variety of output gap measures derived from univariate detrending filters. The resulting ensemble produces well-calibrated forecast densities for US inflation in real time, in contrast to those from simple univariate autoregressions which ignore the contribution of the output gap. Combining evidence from both linear trends and more flexible univariate detrending filters induces strong multi-modality in the predictive densities for the unobserved output gap. The peaks associated with these two detrending methodologies indicate output gaps of opposite sign for some observations, reflecting the pervasive nature of model uncertainty in our US data

The Cost Efficiency of UK Debt Management: A Recursive Modelling Approach

This paper presents an empirical analysis of the efficiency of the UK debt management authorities' (DMA) behaviour from a cost minimisation perspective over the period January 1985 to March 1995. During this period, the maturity structure of the government's bond portfolio was subject to frequent fine-tuning, aimed principally at lowering interest costs. The authors examine the efficiency of the DMA's behaviour from a cost minimisation perspective. Using a bi-variate version of the recursive modelling procedure applied to forecasting stock returns by Pesaran and Timmermann (1995, 2000), it is shown that bond returns are forecastable but that the predictive power of macroeconomic variables is time-dependent. The impact of adjusting the bond portfolio in response to these forecasts is simulated. The simulated average interest costs are lower than those resulting from the DMA's actual real-time behaviour. However, a substantial reduction in interest costs requires large monthly changes in the portfolio's maturity structure.Government debt management, Cost minimisation, Recursive modelling

Real-time inflation forecast densities from ensemble phillips curves

A popular macroeconomic forecasting strategy takes combinations across many models to hedge against model instabilities of unknown timing; see (among others) Stock andWatson (2004) and Clark and McCracken (2009). In this paper, we examine the effectiveness of recursive-weight and equal-weight combination strategies for density forecasting using a time-varying Phillips curve relationship between inflation and the output gap. The densities reflect the uncertainty across a large number of models using many statistical measures of the output gap, allowing for a single structural break of unknown timing. We use real-time data for the US, Australia, New Zealand and Norway. Our main finding is that the recursive-weight strategy performs well across the real-time data sets, consistently giving well-calibrated forecast densities. The equal-weight strategy generates poorly-calibrated forecast densities for the US and Australian samples. There is little difference between the two strategies for our New Zealand and Norwegian data. We also find that the ensemble modeling approach performs more consistently with real-time data than with revised data in all four countries

UK real-time macro data characteristics

We characterise the relationships between preliminary and subsequent measurements for 16 commonly-used UK macroeconomic indicators drawn from two existing real-time data sets and a new nominal variable database. Most preliminary measurements are biased predictors of subsequent measurements, with some revision series affected by multiple structural breaks. To illustrate how these findings facilitate real-time forecasting, we use a vector autoregresion to generate real-time one-step-ahead probability event forecasts for 1990Q1 to 1999Q2. Ignoring the predictability in initial measurements understates considerably the probability of above trend output growth

Forecasting substantial data revisions in the presence of model uncertainty

A recent revision to the preliminary measurement of GDP(E) growth for 2003Q2 caused considerable press attention, provoked a public enquiry and prompted a number of reforms to UK statistical reporting procedures. In this paper, we compute the probability of “substantial revisions” that are greater (in absolute value) than the controversial 2003 revision. The predictive densities are derived from Bayesian model averaging over a wide set of forecasting models including linear, structural break and regime-switching models with and without heteroskedasticity. Ignoring the nonlinearities and model uncertainty yields misleading predictives and obscures recent improvements in the quality of preliminary UK macroeconomic measurements

Real-time prediction with UK monetary aggregates in the presence of model uncertainty

A popular account for the demise of the UK monetary targeting regime in the 1980s blames the weak predictive relationships between broad money and inflation and real output. In this paper, we investigate these relationships using a variety of monetary aggregates which were used as intermediate UK policy targets. We use both real-time and final vintage data and consider a large set of recursively estimated Vector Autoregressive (VAR) and Vector Error Correction models (VECM). These models differ in terms of lag length and the number of cointegrating relationships. Faced with this model uncertainty, we utilize Bayesian model averaging (BMA) and contrast it with a strategy of selecting a single best model. Using the real-time data available to UK policymakers at the time, we demonstrate that the in-sample predictive content of broad money fluctuates throughout the 1980s for both strategies. However, the strategy of choosing a single best model amplifies these fluctuations. Out-of-sample predictive evaluations rarely suggest that money matters for either inflation or real output, regardless of whether we select a single model or do BMA. Overall, we conclude that the money was a weak (and unreliable) predictor for these key macroeconomic variables. But the view that the predictive content of UK broad money diminished during the 1980s receives little support using either the real-time or final vintage data

The Transparency and Accountability of UK Debt Management: A Proposal

Accountability of UK Debt Management: A Proposal' In this paper, we argue that UK debt management policy is insufficiently transparent and accountable. The debt management objectives are poorly understood and accountable, and partly as a consequence, there is no formal mechanism for performance measurement by the public. We propose a number of institutional reforms designed to improve transparency and accountability. These include the definition of more explicit objectives, an independent Debt Management Office and ex post evaluation of the government's domestic portfolio relative to a benchmark. To illustrate how ex post evaluation would work in practice we examine the efficiency of UK debt management policy over ten fiscal years, from April 1985 to March 1995. In a number of years the actual UK government domestic debt portfolio underperformed the benchmark in terms of standard measures of cost and risk.

Scope for Cost Minimization in Public Debt Management: the Case of the UK

This paper provides a framework for an empirical analysis of the scope for cost minimization in public debt management. It assumes that a debt manager aims at minimizing the expected cost of government’s debt portfolio for a given level of short term interest rate and subject to a number of risk and market impact constraints. The analysis is applied to the UK government debt over the period April 1985 to March 2000, by simulating “real time” interest costs of alternative portfolios constructed using monthly forecasts of return spreads based on recursive modelling (RM) procedure recently developed by Pesaran and Timmermann (1995, 2000), which limits the extent of data snooping. Statistically significant evidence of predictability of return spreads are provided before the introduction of reforms of the UK debt management system in 1995, although there seems to be little evidence of predictability once the post reform sample is included. Nevertheless, there appears to have been some scope for a small reduction in interest costs over the 1985-2000 period even if portfolio shares and their monthly changes are constrained to lie within historically observed upper and lower bounds in order to minimize the market impact effects of such changes.Public debt management, cost minimization, recursive modelling, data snooping

Measuring Output Gap Uncertainty

We propose a methodology for producing density forecasts for the output gap in real time using a large number of vector autoregessions in inflation and output gap measures. Density combination utilizes a linear mixture of experts framework to produce potentially non-Gaussian ensemble densities for the unobserved output gap. In our application, we show that data revisions alter substantially our probabilistic assessments of the output gap using a variety of output gap measures derived from univariate detrending filters. The resulting ensemble produces well-calibrated forecast densities for US inflation in real time, in contrast to those from simple univariate autoregressions which ignore the contribution of the output gap. Broadening our empirical analysis to consider output gap measures derived from linear time trends, as well as more flexible trends, generates very different point estimates of the output gap. Combining evidence from both linear trends and more flexible univariate detrending filters induces strong multi-modality in the predictive densities for the unobserved output gap. The peaks associated with these two detrending methodologies indicate output gaps of opposite sign for some observations, reflecting the pervasive nature of model uncertainty in our US data.