Diagnostic yield of short-term video-EEG monitoring for epilepsy and PNESs: a European assessment.

INTRODUCTION: Although long-term video-EEG monitoring (LVEM) is routinely used to investigate paroxysmal events, short-term video-EEG monitoring (SVEM) lasting <24 h is increasingly recognized as a cost-effective tool. Since, however, relatively few studies addressed the yield of SVEM among different diagnostic groups, we undertook the present study to investigate this aspect. METHODS: We retrospectively analyzed 226 consecutive SVEM recordings over 6 years. All patients were referred because routine EEGs were inconclusive. Patients were classified into 3 suspected diagnostic groups: (1) group with epileptic seizures, (2) group with psychogenic nonepileptic seizures (PNESs), and (3) group with other or undetermined diagnoses. We assessed recording lengths, interictal epileptiform discharges, epileptic seizures, PNESs, and the definitive diagnoses obtained after SVEM. RESULTS: The mean age was 34 (±18.7) years, and the median recording length was 18.6 h. Among the 226 patients, 127 referred for suspected epilepsy - 73 had a diagnosis of epilepsy, none had a diagnosis of PNESs, and 54 had other or undetermined diagnoses post-SVEM. Of the 24 patients with pre-SVEM suspected PNESs, 1 had epilepsy, 12 had PNESs, and 11 had other or undetermined diagnoses. Of the 75 patients with other diagnoses pre-SVEM, 17 had epilepsy, 11 had PNESs, and 47 had other or undetermined diagnoses. After SVEM, 15 patients had definite diagnoses other than epilepsy or PNESs, while in 96 patients, diagnosis remained unclear. Overall, a definitive diagnosis could be reached in 129/226 (57%) patients. CONCLUSIONS: This study demonstrates that in nearly 3/5 patients without a definitive diagnosis after routine EEG, SVEM allowed us to reach a diagnosis. This procedure should be encouraged in this setting, given its time-effectiveness compared with LVEM.

UK Macroeconomic Forecasting with Many Predictors: Which Models Forecast Best and When Do They Do So?

Block factor methods offer an attractive approach to forecasting with many predictors. These extract the information in these predictors into factors reflecting different blocks of variables (e.g. a price block, a housing block, a financial block, etc.). However, a forecasting model which simply includes all blocks as predictors risks being over-parameterized. Thus, it is desirable to use a methodology which allows for different parsimonious forecasting models to hold at different points in time. In this paper, we use dynamic model averaging and dynamic model selection to achieve this goal. These methods automatically alter the weights attached to different forecasting models as evidence comes in about which has forecast well in the recent past. In an empirical study involving forecasting output growth and inflation using 139 UK monthly time series variables, we find that the set of predictors changes substantially over time. Furthermore, our results show that dynamic model averaging and model selection can greatly improve forecast performance relative to traditional forecasting methods.

Forecasting Inflation Using Dynamic Model Averaging

We forecast quarterly US inflation based on the generalized Phillips curve using econometric methods which incorporate dynamic model averaging. These methods not only allow for coe¢ cients to change over time, but also allow for the entire forecasting model to change over time. We nd that dynamic model averaging leads to substantial forecasting improvements over simple benchmark regressions and more sophisticated approaches such as those using time varying coe¢ cient models. We also provide evidence on which sets of predictors are relevant for forecasting in each period.

Forecasting the European Carbon Market

In an effort to meet its obligations under the Kyoto Protocol, in 2005 the European Union introduced a cap-and-trade scheme where mandated installations are allocated permits to emit CO2. Financial markets have developed that allow companies to trade these carbon permits. For the EU to achieve reductions in CO2 emissions at a minimum cost, it is necessary that companies make appropriate investments and policymakers design optimal policies. In an effort to clarify the workings of the carbon market, several recent papers have attempted to statistically model it. However, the European carbon market (EU ETS) has many institutional features that potentially impact on daily carbon prices (and associated nancial futures). As a consequence, the carbon market has properties that are quite different from conventional financial assets traded in mature markets. In this paper, we use dynamic model averaging (DMA) in order to forecast in this newly-developing market. DMA is a recently-developed statistical method which has three advantages over conventional approaches. First, it allows the coefficients on the predictors in a forecasting model to change over time. Second, it allows for the entire fore- casting model to change over time. Third, it surmounts statistical problems which arise from the large number of potential predictors that can explain carbon prices. Our empirical results indicate that there are both important policy and statistical bene ts with our approach. Statistically, we present strong evidence that there is substantial turbulence and change in the EU ETS market, and that DMA can model these features and forecast accurately compared to conventional approaches. From a policy perspective, we discuss the relative and changing role of different price drivers in the EU ETS. Finally, we document the forecast performance of DMA and discuss how this relates to the efficiency and maturity of this market.

A Comparison Of Forecasting Procedures For Macroeconomic Series: The Contribution Of Structural Break Models

This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the parameters defining the model which applies in each regime and the out-of-sample probability of a break occurring. In an extensive empirical evaluation involving many important macroeconomic time series, we demonstrate the presence of structural breaks and their importance for forecasting in the vast majority of cases. However, we find no single forecasting model consistently works best in the presence of structural breaks. In many cases, the formal modeling of the break process is important in achieving good forecast performance. However, there are also many cases where simple, rolling OLS forecasts perform well.

A Comparison Of Forecasting Procedures For Macroeconomic Series: The Contribution Of Structural Break Models

This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the parameters defining the model which applies in each regime and the out-of-sample probability of a break occurring. In an extensive empirical evaluation involving many important macroeconomic time series, we demonstrate the presence of structural breaks and their importance for forecasting in the vast majority of cases. However, we find no single forecasting model consistently works best in the presence of structural breaks. In many cases, the formal modeling of the break process is important in achieving good forecast performance. However, there are also many cases where simple, rolling OLS forecasts perform well.

Forecasting with Medium and Large Bayesian VARs

This paper is motivated by the recent interest in the use of Bayesian VARs for forecasting, even in cases where the number of dependent variables is large. In such cases, factor methods have been traditionally used but recent work using a particular prior suggests that Bayesian VAR methods can forecast better. In this paper, we consider a range of alternative priors which have been used with small VARs, discuss the issues which arise when they are used with medium and large VARs and examine their forecast performance using a US macroeconomic data set containing 168 variables. We nd that Bayesian VARs do tend to forecast better than factor methods and provide an extensive comparison of the strengths and weaknesses of various approaches. Our empirical results show the importance of using forecast metrics which use the entire predictive density, instead of using only point forecasts.

UK Macroeconomic Forecasting with Many Predictors: Which Models Forecast Best and When Do They Do So?

Block factor methods offer an attractive approach to forecasting with many predictors. These extract the information in these predictors into factors reflecting different blocks of variables (e.g. a price block, a housing block, a financial block, etc.). However, a forecasting model which simply includes all blocks as predictors risks being over-parameterized. Thus, it is desirable to use a methodology which allows for different parsimonious forecasting models to hold at different points in time. In this paper, we use dynamic model averaging and dynamic model selection to achieve this goal. These methods automatically alter the weights attached to different forecasting model as evidence comes in about which has forecast well in the recent past. In an empirical study involving forecasting output and inflation using 139 UK monthly time series variables, we find that the set of predictors changes substantially over time. Furthermore, our results show that dynamic model averaging and model selection can greatly improve forecast performance relative to traditional forecasting methods.

Forecasting Inflation Using Dynamic Model Averaging

We forecast quarterly US inflation based on the generalized Phillips curve using econometric methods which incorporate dynamic model averaging. These methods not only allow for coe¢ cients to change over time, but also allow for the entire forecasting model to change over time. We nd that dynamic model averaging leads to substantial forecasting improvements over simple benchmark regressions and more sophisticated approaches such as those using time varying coe¢ cient models. We also provide evidence on which sets of predictors are relevant for forecasting in each period.

Forecasting US bond default ratings allowing for previous and initial state dependence in an ordered probit model

In this paper we investigate the ability of a number of different ordered probit models to predict ratings based on firm-specific data on business and financial risks. We investigate models based on momentum, drift and ageing and compare them against alternatives that take into account the initial rating of the firm and its previous actual rating. Using data on US bond issuing firms rated by Fitch over the years 2000 to 2007 we compare the performance of these models in predicting the rating in-sample and out-of-sample using root mean squared errors, Diebold-Mariano tests of forecast performance and contingency tables. We conclude that initial and previous states have a substantial influence on rating prediction.

The determinants of sovereign bond yield spreads in the EMU

We use a panel of euro area countries to assess the determinants of long-term sovereign bond yield spreads over the period 1999.01-2010.12. We find that, unlike the period preceding the global financial crisis, European government bond yield spreads are wellexplained by macro- and fiscal fundamentals over the crisis period. We also find that the menu of macro and fiscal risks priced by markets has been significantly enriched since March 2009, including the risk of the crisis’ transmission among EMU member states, international risk and liquidity risk. Finally, we find that sovereign credit ratings are statistically significant in explaining spreads, yet compared to macro- and fiscal fundamentals their role is limited.

Bayesian forecasting with highly correlated predictors

This paper considers Bayesian variable selection in regressions with a large number of possibly highly correlated macroeconomic predictors. I show that by acknowledging the correlation structure in the predictors can improve forecasts over existing popular Bayesian variable selection algorithms.

Modeling Dependence Structure and Forecasting Portfolio Value-at-Risk with Dynamic Copulas

We study the asymmetric and dynamic dependence between financial assets and demonstrate, from the perspective of risk management, the economic significance of dynamic copula models. First, we construct stock and currency portfolios sorted on different characteristics (ex ante beta, coskewness, cokurtosis and order flows), and find substantial evidence of dynamic evolution between the high beta (respectively, coskewness, cokurtosis and order flow) portfolios and the low beta (coskewness, cokurtosis and order flow) portfolios. Second, using three different dependence measures, we show the presence of asymmetric dependence between these characteristic-sorted portfolios. Third, we use a dynamic copula framework based on Creal et al. (2013) and Patton (2012) to forecast the portfolio Value-at-Risk of long-short (high minus low) equity and FX portfolios. We use several widely used univariate and multivariate VaR models for the purpose of comparison. Backtesting our methodology, we find that the asymmetric dynamic copula models provide more accurate forecasts, in general, and, in particular, perform much better during the recent financial crises, indicating the economic significance of incorporating dynamic and asymmetric dependence in risk management.

Interlinkages and forecasting made possible by the use of the DECOIN analytical tool kit

This technical report is a document prepared as a deliverable [D4.3 Report of the Interlinkages and forecasting prototype tool] of a EU project – DECOIN Project No. 044428 - FP6-2005-SSP-5A. The text is divided into 4 sections: (1) this short introductory section explains the purpose of the report; (2) the second section provides a general discussion of a systemic problem found in existing quantitative analysis of sustainability. It addresses the epistemological implications of complexity, which entails the need of dealing with the existence of Multiple-Scales and non-equivalent narratives (multiple dimensions/attributes) to be used to define sustainability issues. There is an unavoidable tension between a “steady-state view” (= the perception of what is going on now – reflecting a PAST --& PRESENT view of the reality) versus an “evolutionary view” (= the unknown transformation that we have to expect in the process of becoming of the observed reality and in the observer – reflecting a PRESENT --& FUTURE view of the reality). The section ends by listing the implications of these points on the choice of integrated packages of sustainability indicators; (3) the third section illustrates the potentiality of the DECOIN toolkit for the study of sustainability trade-offs and linkages across indicators using quantitative examples taken from cases study of another EU project (SMILE). In particular, this section starts by addressing the existence of internal constraints to sustainability (economic versus social aspects). The narrative chosen for this discussion focuses on the dark side of ageing and immigration on the economic viability of social systems. Then the section continues by exploring external constraints to sustainability (economic development vs the environment). The narrative chosen for this discussion focuses on the dark side of current strategy of economic development based on externalization and the “bubbles-disease”; (4) the last section presents a critical appraisal of the quality of energy data found in energy statistics. It starts with a discussion of the general goal of statistical accounting. Then it introduces the concept of multipurpose grammars. The second part uses the experience made in the activities of the DECOIN project to answer the question: how useful are EUROSTAT energy statistics? The answer starts with an analysis of basic epistemological problems associated with accounting of energy. This discussion leads to the acknowledgment of an important epistemological problem: the unavoidable bifurcations in the mechanism of accounting needed to generate energy statistics. By using numerical example the text deals with the following issues: (i) the pitfalls of the actual system of accounting in energy statistics; (ii) a critical appraisal of the actual system of accounting in BP statistics; (iii) a critical appraisal of the actual system of accounting in Eurostat statistics. The section ends by proposing an innovative method to represent energy statistics which can result more useful for those willing develop sustainability indicators.