Flexible rules cum constrained discretion: a new consensus in monetary policy

This paper demonstrates that recent influential contributions to monetary policy imply an emerging consensus whereby neither rigid rules nor complete discretion are found optimal. Instead, middle-ground monetary regimes based on rules (operative under 'normal' circumstances) to anchor inflation expectations over the long run, but designed with enough flexibility to mitigate the short-run effect of shocks (with communicated discretion in 'exceptional' circumstances temporarily overriding these rules), are gaining support in theoretical models and policy formulation and implementation. The opposition of 'rules versus discretion' has, thus, reappeared as the synthesis of 'rules cum discretion', in essence as inflation-forecast targeting. But such synthesis is not without major theoretical problems, as we argue in this contribution. Furthermore, the very recent real-world events have made it obvious that the inflation targeting strategy of monetary policy, which rests upon the new consensus paradigm in modern macroeconomics is at best a 'fair weather' model. In the turbulent economic climate of highly unstable inflation, deep financial crisis and world-wide, abrupt economic slowdown nowadays this approach needs serious rethinking to say the least, if not abandoning it altogether

Spatiotemporal behavior of the TIGGE medium-range ensemble forecasts

Using the recently-developed mean–variance of logarithms (MVL) diagram, together with the TIGGE archive of medium-range ensemble forecasts from nine different centres, an analysis is presented of the spatiotemporal dynamics of their perturbations, showing how the differences between models and perturbation techniques can explain the shape of their characteristic MVL curves. In particular, a divide is seen between ensembles based on singular vectors or empirical orthogonal functions, and those based on bred vector, Ensemble Transform with Rescaling or Ensemble Kalman Filter techniques. Consideration is also given to the use of the MVL diagram to compare the growth of perturbations within the ensemble with the growth of the forecast error, showing that there is a much closer correspondence for some models than others. Finally, the use of the MVL technique to assist in selecting models for inclusion in a multi-model ensemble is discussed, and an experiment suggested to test its potential in this context.

A review of climate risk information for adaptation and development planning

Although the use of climate scenarios for impact assessment has grown steadily since the 1990s, uptake of such information for adaptation is lagging by nearly a decade in terms of scientific output. Nonetheless, integration of climate risk information in development planning is now a priority for donor agencies because of the need to prepare for climate change impacts across different sectors and countries. This urgency stems from concerns that progress made against Millennium Development Goals (MDGs) could be threatened by anthropogenic climate change beyond 2015. Up to this time the human signal, though detectable and growing, will be a relatively small component of climate variability and change. This implies the need for a twin-track approach: on the one hand, vulnerability assessments of social and economic strategies for coping with present climate extremes and variability, and, on the other hand, development of climate forecast tools and scenarios to evaluate sector-specific, incremental changes in risk over the next few decades. This review starts by describing the climate outlook for the next couple of decades and the implications for adaptation assessments. We then review ways in which climate risk information is already being used in adaptation assessments and evaluate the strengths and weaknesses of three groups of techniques. Next we identify knowledge gaps and opportunities for improving the production and uptake of climate risk information for the 2020s. We assert that climate change scenarios can meet some, but not all, of the needs of adaptation planning. Even then, the choice of scenario technique must be matched to the intended application, taking into account local constraints of time, resources, human capacity and supporting infrastructure. We also show that much greater attention should be given to improving and critiquing models used for climate impact assessment, as standard practice. Finally, we highlight the over-arching need for the scientific community to provide more information and guidance on adapting to the risks of climate variability and change over nearer time horizons (i.e. the 2020s). Although the focus of the review is on information provision and uptake in developing regions, it is clear that many developed countries are facing the same challenges. Copyright © 2009 Royal Meteorological Society

Ensemble prediction for nowcasting with a convection-permitting model—I: description of the system and the impact of radar-derived surface precipitation rates

A key strategy to improve the skill of quantitative predictions of precipitation, as well as hazardous weather such as severe thunderstorms and flash floods is to exploit the use of observations of convective activity (e.g. from radar). In this paper, a convection-permitting ensemble prediction system (EPS) aimed at addressing the problems of forecasting localized weather events with relatively short predictability time scale and based on a 1.5 km grid-length version of the Met Office Unified Model is presented. Particular attention is given to the impact of using predicted observations of radar-derived precipitation intensity in the ensemble transform Kalman filter (ETKF) used within the EPS. Our initial results based on the use of a 24-member ensemble of forecasts for two summer case studies show that the convective-scale EPS produces fairly reliable forecasts of temperature, horizontal winds and relative humidity at 1 h lead time, as evident from the inspection of rank histograms. On the other hand, the rank histograms seem also to show that the EPS generates too much spread for forecasts of (i) surface pressure and (ii) surface precipitation intensity. These may indicate that for (i) the value of surface pressure observation error standard deviation used to generate surface pressure rank histograms is too large and for (ii) may be the result of non-Gaussian precipitation observation errors. However, further investigations are needed to better understand these findings. Finally, the inclusion of predicted observations of precipitation from radar in the 24-member EPS considered in this paper does not seem to improve the 1-h lead time forecast skill.

Evolution of coronal mass ejection morphology with increasing heliocentric distance. II. In Situ observations

Interplanetary coronal mass ejections (ICMEs) are often observed to travel much faster than the ambient solar wind. If the relative speed between the two exceeds the fast magnetosonic velocity, then a shock wave will form. The Mach number and the shock standoff distance ahead of the ICME leading edge is measured to infer the vertical size of an ICME in a direction that is perpendicular to the solar wind flow. We analyze the shock standoff distance for 45 events varying between 0.5 AU and 5.5 AU in order to infer their physical dimensions. We find that the average ratio of the inferred vertical size to measured radial width, referred to as the aspect ratio, of an ICME is 2.8 ± 0.5. We also compare these results to the geometrical predictions from Paper I that forecast an aspect ratio between 3 and 6. The geometrical solution varies with heliocentric distance and appears to provide a theoretical maximum for the aspect ratio of ICMEs. The minimum aspect ratio appears to remain constant at 1 (i.e., a circular cross section) for all distances. These results suggest that possible distortions to the leading edge of ICMEs are frequent. But, these results may also indicate that the constants calculated in the empirical relationship correlating the different shock front need to be modified; or perhaps both distortions and a change in the empirical formulae are required.

Uncertainty and feasibility studies: an Italian case study

Uncertainty affects all aspects of the property market but one area where the impact of uncertainty is particularly significant is within feasibility analyses. Any development is impacted by differences between market conditions at the conception of the project and the market realities at the time of completion. The feasibility study needs to address the possible outcomes based on an understanding of the current market. This requires the appraiser to forecast the most likely outcome relating to the sale price of the completed development, the construction costs and the timing of both. It also requires the appraiser to understand the impact of finance on the project. All these issues are time sensitive and analysis needs to be undertaken to show the impact of time to the viability of the project. The future is uncertain and a full feasibility analysis should be able to model the upside and downside risk pertaining to a range of possible outcomes. Feasibility studies are extensively used in Italy to determine land value but they tend to be single point analysis based upon a single set of “likely” inputs. In this paper we look at the practical impact of uncertainty in variables using a simulation model (Crystal Ball ©) with an actual case study of an urban redevelopment plan for an Italian Municipality. This allows the appraiser to address the issues of uncertainty involved and thus provide the decision maker with a better understanding of the risk of development. This technique is then refined using a “two-dimensional technique” to distinguish between “uncertainty” and “variability” and thus create a more robust model.

Expert judgement in the processes of commercial property market forecasting

In this paper, we investigate the role of judgement in the formation of forecasts in commercial property markets. The investigation is based on interview surveys with the majority of UK forecast producers, who are using a range of inputs and data sets to form models to predict an array of variables for a range of locations. The findings suggest that forecasts need to be acceptable to their users (and purchasers) and consequently forecasters generally have incentives to avoid presenting contentious or conspicuous forecasts. Where extreme forecasts are generated by a model, forecasters often engage in ‘self‐censorship’ or are ‘censored’ following in‐house consultation. It is concluded that the forecasting process is significantly more complex than merely carrying out econometric modelling, forecasts are mediated and contested within organisations and that impacts can vary considerably across different organizational contexts.

The production and consumption of commercial real estate market forecasts

Whilst the vast majority of the research on property market forecasting has concentrated on statistical methods of forecasting future rents, this report investigates the process of property market forecast production with particular reference to the level and effect of judgemental intervention in this process. Expectations of future investment performance at the levels of individual asset, sector, region, country and asset class are crucial to stock selection and tactical and strategic asset allocation decisions. Given their centrality to investment performance, we focus on the process by which forecasts of rents and yields are generated and expectations formed. A review of the wider literature on forecasting suggests that there are strong grounds to expect that forecast outcomes are not the result of purely mechanical calculations.

An evaluation of the performance of UK real estate forecasters

Given the significance of forecasting in real estate investment decisions, this paper investigates forecast uncertainty and disagreement in real estate market forecasts. It compares the performance of real estate forecasters with non-real estate forecasters. Using the Investment Property Forum (IPF) quarterly survey amongst UK independent real estate forecasters and a similar survey of macro-economic and capital market forecasters, these forecasts are compared with actual performance to assess a number of forecasting issues in the UK over 1999-2004, including forecast error, bias and consensus. The results suggest that both groups are biased, less volatile compared to market returns and inefficient in that forecast errors tend to persist. The strongest finding is that forecasters display the characteristics associated with a consensus indicating herding.

Data dilemmas in forecasting European office market rents

This paper uses data provided by three major real estate advisory firms to investigate the level and pattern of variation in the measurement of historic real estate rental values for the main European office centres. The paper assesses the extent to which the data providing organizations agree on historic market performance in terms of returns, risk and timing and examines the relationship between market maturity and agreement. The analysis suggests that at the aggregate level and for many markets, there is substantial agreement on direction, quantity and timing of market change. However, there is substantial variability in the level of agreement among cities. The paper also assesses whether the different data sets produce different explanatory models and market forecast. It is concluded that, although disagreement on the direction of market change is high for many market, the different data sets often produce similar explanatory models and predict similar relative performance.

Volatility forecasting for risk management

Recent research has suggested that forecast evaluation on the basis of standard statistical loss functions could prefer models which are sub-optimal when used in a practical setting. This paper explores a number of statistical models for predicting the daily volatility of several key UK financial time series. The out-of-sample forecasting performance of various linear and GARCH-type models of volatility are compared with forecasts derived from a multivariate approach. The forecasts are evaluated using traditional metrics, such as mean squared error, and also by how adequately they perform in a modern risk management setting. We find that the relative accuracies of the various methods are highly sensitive to the measure used to evaluate them. Such results have implications for any econometric time series forecasts which are subsequently employed in financial decisionmaking.

International evidence on the predictability of returns to securitized real estate assets: econometric models versus neural networks

The performance of various statistical models and commonly used financial indicators for forecasting securitised real estate returns are examined for five European countries: the UK, Belgium, the Netherlands, France and Italy. Within a VAR framework, it is demonstrated that the gilt-equity yield ratio is in most cases a better predictor of securitized returns than the term structure or the dividend yield. In particular, investors should consider in their real estate return models the predictability of the gilt-equity yield ratio in Belgium, the Netherlands and France, and the term structure of interest rates in France. Predictions obtained from the VAR and univariate time-series models are compared with the predictions of an artificial neural network model. It is found that, whilst no single model is universally superior across all series, accuracy measures and horizons considered, the neural network model is generally able to offer the most accurate predictions for 1-month horizons. For quarterly and half-yearly forecasts, the random walk with a drift is the most successful for the UK, Belgian and Dutch returns and the neural network for French and Italian returns. Although this study underscores market context and forecast horizon as parameters relevant to the choice of the forecast model, it strongly indicates that analysts should exploit the potential of neural networks and assess more fully their forecast performance against more traditional models.

Expert judgement in the processes of commercial property market forecasting

In this paper we investigate the role of judgement in the formation of forecasts in commercial real estate markets. Based on interview surveys with the majority of forecast producers, we find that real estate forecasters are using a range of inputs and data sets to form models to predict an array of variables for a range of locations. The findings suggest that forecasts need to be acceptable to their users (and purchasers) and consequently forecasters generally have incentives to avoid presenting contentious or conspicuous forecasts. Where extreme forecasts are generated by a model, forecasters often engage in ‘self-censorship’ or are ‘censored’ following in-house consultation. It is concluded that the forecasting process is more complex than merely carrying out econometric modelling and that the impact of the influences within this process vary considerably across different organizational contexts.

Forecasting the future: an overview of the strategic planning of the Bournemouth and Poole conurbation

An important part of strategic planning’s purpose should be to attempt to forecast the future, not simply to belatedly respond to events, or accept the future as inevitable. This paper puts forward a conceptual approach for seeking to achieve these aims and uses the Bournemouth and Poole area in Dorset as a vehicle for applying the basic methodology. The area has been chosen because of the significant issues that it currently faces in planning terms; and its future development possibilities. In order that alternative future choices for the area – different ‘developmental trajectories’ – can be evaluated, they must be carefully and logically constructed. Four Futures for Bournemouth/Poole have been put forward; they are titled and colour-coded: Future One is Maximising Growth – Golden Prospect which seeks to achieve the highest level of economic prosperity of the area; Future Two is Incremental Growth – Solid Silver which attempts to facilitate a steady, continuing, controlled pattern of the development for the area; Future Three is Steady State – Cobalt Blue which suggests that people in the area could be more concerned with preserving their quality of life in terms of their leisure and recreation rather than increasing wealth; Future Four is Environment First – Jade Green which makes the area’s environmental protection its top priority even at the possible expense of economic prosperity. The scenarios proposed here are not sacrosanct. Nor are they simply confined to the Bournemouth and Poole area. In theory, suitably modified, they could use in a variety of different contexts. Consideration of the scenarios – wherever located - might then generate other, additional scenarios. These are called hybrids, alloys and amalgams. Likewise it might identify some of them as inappropriate or impossible. Most likely, careful consideration of the scenarios will suggest hybrid scenarios, in which features from different scenarios are combined to produce alternative or additional futures for consideration. The real issue then becomes how best to fashion such a future for the particular area under consideration

Forecasting UK real estate cycle phases with leading indicators: a probit approach

This paper examines the significance of widely used leading indicators of the UK economy for predicting the cyclical pattern of commercial real estate performance. The analysis uses monthly capital value data for UK industrials, offices and retail from the Investment Property Databank (IPD). Prospective economic indicators are drawn from three sources namely, the series used by the US Conference Board to construct their UK leading indicator and the series deployed by two private organisations, Lombard Street Research and NTC Research, to predict UK economic activity. We first identify turning points in the capital value series adopting techniques employed in the classical business cycle literature. We then estimate probit models using the leading economic indicators as independent variables and forecast the probability of different phases of capital values, that is, periods of declining and rising capital values. The forecast performance of the models is tested and found to be satisfactory. The predictability of lasting directional changes in property performance represents a useful tool for real estate investment decision-making.