Describing seasonal variability in the distribution of daily effective temperatures for 1985-2009 compared to 1904-1984 for De Bilt, Holland

This paper proposes a method for describing the distribution of observed temperatures on any day of the year such that the distribution and summary statistics of interest derived from the distribution vary smoothly through the year. The method removes the noise inherent in calculating summary statistics directly from the data thus easing comparisons of distributions and summary statistics between different periods. The method is demonstrated using daily effective temperatures (DET) derived from observations of temperature and wind speed at De Bilt, Holland. Distributions and summary statistics are obtained from 1985 to 2009 and compared to the period 1904–1984. A two-stage process first obtains parameters of a theoretical probability distribution, in this case the generalized extreme value (GEV) distribution, which describes the distribution of DET on any day of the year. Second, linear models describe seasonal variation in the parameters. Model predictions provide parameters of the GEV distribution, and therefore summary statistics, that vary smoothly through the year. There is evidence of an increasing mean temperature, a decrease in the variability in temperatures mainly in the winter and more positive skew, more warm days, in the summer. In the winter, the 2% point, the value below which 2% of observations are expected to fall, has risen by 1.2 °C, in the summer the 98% point has risen by 0.8 °C. Medians have risen by 1.1 and 0.9 °C in winter and summer, respectively. The method can be used to describe distributions of future climate projections and other climate variables. Further extensions to the methodology are suggested.

Secular trends in daily precipitation characteristics: greenhouse gas simulation with a coupled AOGCM

Secular trends of daily precipitation characteristics are considered in the transient climate change experiment with a coupled atmosphere-ocean general circulation model ECHAM4/OPYC3 for 1900-2099. The climate forcing is due to increasing concentrations of the greenhouse gases in the atmosphere. Mean daily precipitation, precipitation intensity, probability of wet days and parameters of the gamma distribution are analyzed. Particular attention is paid to the changes of heavy precipitation, Analysis of the annual mean precipitation trends for 1900-1999 revealed general agreement with observations with significant positive trends in mean precipitation over continental areas. In the 2000-2099 period precipitation trend patterns followed the tendency obtained for 1900-1999 but with significantly increased magnitudes. Unlike the annual mean precipitation trends for which negative values were found for some continental areas, the mean precipitation intensity and scale parameter of the fitted gamma distribution increased over all land territories . Negative trends in the number of wet days were found over most of the land areas except high latitudes in the Northern Hemisphere. The shape parameter of the gamma distribution in general revealed a slight negative trend in the areas of the precipitation increase. Investigation of daily precipitation revealed an unproportional increase of heavy precipitation events for the land areas including local maxima in Europe and the eastern United States.

Effects of spatial resolution in the simulation of daily and subdaily precipitation in the southwestern US

We evaluate the effects of spatial resolution on the ability of a regional climate model to reproduce observed extreme precipitation for a region in the Southwestern United States. A total of 73 National Climate Data Center observational sites spread throughout Arizona and New Mexico are compared with regional climate simulations at the spatial resolutions of 50 km and 10 km for a 31 year period from 1980 to 2010. We analyze mean, 3-hourly and 24-hourly extreme precipitation events using WRF regional model simulations driven by NCEP-2 reanalysis. The mean climatological spatial structure of precipitation in the Southwest is well represented by the 10 km resolution but missing in the coarse (50 km resolution) simulation. However, the fine grid has a larger positive bias in mean summer precipitation than the coarse-resolution grid. The large overestimation in the simulation is in part due to scale-dependent deficiencies in the Kain-Fritsch convective parameterization scheme that generate excessive precipitation and induce a slow eastward propagation of the moist convective summer systems in the high-resolution simulation. Despite this overestimation in the mean, the 10 km simulation captures individual extreme summer precipitation events better than the 50 km simulation. In winter, however, the two simulations appear to perform equally in simulating extremes.

Improving real-time estimates of output and inflation gaps with multiple-vintage models

Real-time estimates of output gaps and inflation gaps differ from the values that are obtained using data available long after the event. Part of the problem is that the data on which the real-time estimates are based is subsequently revised. We show that vector-autoregressive models of data vintages provide forecasts of post-revision values of future observations and of already-released observations capable of improving estimates of output and inflation gaps in real time. Our findings indicate that annual revisions to output and inflation data are in part predictable based on their past vintages.

Research practices that can prevent an inflation of false-positive rates

Recent studies have indicated that research practices in psychology may be susceptible to factors that increase false-positive rates, raising concerns about the possible prevalence of false-positive findings. The present article discusses several practices that may run counter to the inflation of false-positive rates. Taking these practices into account would lead to a more balanced view on the false-positive issue. Specifically, we argue that an inflation of false-positive rates would diminish, sometimes to a substantial degree, when researchers (a) have explicit a priori theoretical hypotheses, (b) include multiple replication studies in a single paper, and (c) collect additional data based on observed results. We report findings from simulation studies and statistical evidence that support these arguments. Being aware of these preventive factors allows researchers not to overestimate the pervasiveness of false-positives in psychology and to gauge the susceptibility of a paper to possible false-positives in practical and fair ways.

Evaluating the Bank of England density forecasts of inflation

We consider evaluating the UK Monetary Policy Committee's inflation density forecasts using probability integral transform goodness-of-fit tests. These tests evaluate the whole forecast density. We also consider whether the probabilities assigned to inflation being in certain ranges are well calibrated, where the ranges are chosen to be those of particular relevance to the MPC, given its remit of maintaining inflation rates in a band around per annum. Finally, we discuss the decision-based approach to forecast evaluation in relation to the MPC forecasts

Evaluating the Survey of Professional Forecasters probability distributions of expected inflation based on derived event probability forecasts

Techniques are proposed for evaluating forecast probabilities of events. The tools are especially useful when, as in the case of the Survey of Professional Forecasters (SPF) expected probability distributions of inflation, recourse cannot be made to the method of construction in the evaluation of the forecasts. The tests of efficiency and conditional efficiency are applied to the forecast probabilities of events of interest derived from the SPF distributions, and supplement a whole-density evaluation of the SPF distributions based on the probability integral transform approach.

Quantile forecasts of daily exchange rate returns from forecasts of realized volatility

Quantile forecasts are central to risk management decisions because of the widespread use of Value-at-Risk. A quantile forecast is the product of two factors: the model used to forecast volatility, and the method of computing quantiles from the volatility forecasts. In this paper we calculate and evaluate quantile forecasts of the daily exchange rate returns of five currencies. The forecasting models that have been used in recent analyses of the predictability of daily realized volatility permit a comparison of the predictive power of different measures of intraday variation and intraday returns in forecasting exchange rate variability. The methods of computing quantile forecasts include making distributional assumptions for future daily returns as well as using the empirical distribution of predicted standardized returns with both rolling and recursive samples. Our main findings are that the Heterogenous Autoregressive model provides more accurate volatility and quantile forecasts for currencies which experience shifts in volatility, such as the Canadian dollar, and that the use of the empirical distribution to calculate quantiles can improve forecasts when there are shifts

Forecasting with vector autoregressive models of data vintages: US output growth and inflation

Vintage-based vector autoregressive models of a single macroeconomic variable are shown to be a useful vehicle for obtaining forecasts of different maturities of future and past observations, including estimates of post-revision values. The forecasting performance of models which include information on annual revisions is superior to that of models which only include the first two data releases. However, the empirical results indicate that a model which reflects the seasonal nature of data releases more closely does not offer much improvement over an unrestricted vintage-based model which includes three rounds of annual revisions.

Real-time forecasting of inflation and output growth with autoregressive models in the presence of data revisions

We examine how the accuracy of real-time forecasts from models that include autoregressive terms can be improved by estimating the models on ‘lightly revised’ data instead of using data from the latest-available vintage. The benefits of estimating autoregressive models on lightly revised data are related to the nature of the data revision process and the underlying process for the true values. Empirically, we find improvements in root mean square forecasting error of 2–4% when forecasting output growth and inflation with univariate models, and of 8% with multivariate models. We show that multiple-vintage models, which explicitly model data revisions, require large estimation samples to deliver competitive forecasts. Copyright © 2012 John Wiley & Sons, Ltd.

Probability distributions or point predictions? Survey forecasts of US output growth and inflation

We consider whether survey respondents’ probability distributions, reported as histograms, provide reliable and coherent point predictions, when viewed through the lens of a Bayesian learning model. We argue that a role remains for eliciting directly-reported point predictions in surveys of professional forecasters.

US inflation expectations and heterogeneous loss functions, 1968-2010

Recent literature has suggested that macroeconomic forecasters may have asymmetric loss functions, and that there may be heterogeneity across forecasters in the degree to which they weigh under- and over-predictions. Using an individual-level analysis that exploits the Survey of Professional Forecasters respondents’ histogram forecasts, we find little evidence of asymmetric loss for the inflation forecasters

Upland streamwater nitrate dynamics across decadal to sub-daily timescales: a case study of Plynlimon, Wales

Streamwater nitrate dynamics in the River Hafren, Plynlimon, mid-Wales were investigated over decadal to sub-daily timescales using a range of statistical techniques. Long-term data were derived from weekly grab samples (1984–2010) and high-frequency data from 7-hourly samples (2007–2009) both measured at two sites: a headwater stream draining moorland and a downstream site below plantation forest. This study is one of the first to analyse upland streamwater nitrate dynamics across such a wide range of timescales and report on the principal mechanisms identified. The data analysis provided no clear evidence that the long-term decline in streamwater nitrate concentrations was related to a decline in atmospheric deposition alone, because nitrogen deposition first increased and then decreased during the study period. Increased streamwater temperature and denitrification may also have contributed to the decline in stream nitrate concentrations, the former through increased N uptake rates and the latter resultant from increased dissolved organic carbon concentrations. Strong seasonal cycles, with concentration minimums in the summer, were driven by seasonal flow minimums and seasonal biological activity enhancing nitrate uptake. Complex diurnal dynamics were observed, with seasonal changes in phase and amplitude of the cycling, and the diurnal dynamics were variable along the river. At the moorland site, a regular daily cycle, with minimum concentrations in the early afternoon, corresponding with peak air temperatures, indicated the importance of instream biological processing. At the downstream site, the diurnal dynamics were a composite signal, resultant from advection, dispersion and nitrate processing in the soils of the lower catchment. The diurnal streamwater nitrate dynamics were also affected by drought conditions. Enhanced diurnal cycling in Spring 2007 was attributed to increased nitrate availability in the post-drought period as well as low flow rates and high temperatures over this period. The combination of high-frequency short-term measurements and long-term monitoring provides a powerful tool for increasing understanding of the controls of element fluxes and concentrations in surface waters.

Testing for non-linearity in daily sterling exchange rates

A number of tests for non-linear dependence in time series are presented and implemented on a set of 10 daily sterling exchange rates covering the entire post Bretton-Woods era until the present day. Irrefutable evidence of non-linearity is shown in many of the series, but most of this dependence can apparently be explained by reference to the GARCH family of models. It is suggested that the literature in this area has reached an impasse, with the presence of ARCH effects clearly demonstrated in a large number of papers, but with the tests for non-linearity which are currently available being unable to classify any additional non-linear structure.