Anticipating revisions in the transportation services index

2021 ◽  
pp. 1-13
Author(s):  
Tucker S. McElroy ◽  
Anindya Roy ◽  
James Livsey ◽  
Theresa Firestine ◽  
Ken Notis
Keyword(s):  
Confidence Intervals ◽  
Vector Autoregression ◽  
Data Availability ◽  
Economic Conditions ◽  
Forecast Errors ◽  
Release Date ◽  
Transportation Services ◽  
Publication Delay ◽  
Source Data ◽  
Out Of Sample

The Transportation Services Index (TSI) lags two months from its release date due to source data availability, and it is desirable to publish a preliminary TSI that is advanced two months ahead. We model and forecast TSI with a co-integrated Vector Autoregression, also considering two explanatory series that do not have publication delay. Thus we are able to produce forecasts and nowcasts of the index, and we demonstrate that – during normal economic conditions – out-of-sample performance is within the scope expected by the forecast confidence intervals. We also examine the performance of the models at the onset of the COVID-19 pandemic, and the large forecast errors at this regime change are beyond the bounds indicated by our model. The practical ramifications of this methodology is discussed.

scholarly journals Forecasting Macedonian Business Cycle Turning Points Using Qual Var Model

2016 ◽  
Vol 5 (3) ◽  
pp. 61-78
Author(s):  
Magdalena Petrovska ◽  
Aneta Krstevska ◽  
Nikola Naumovski
Keyword(s):  
Business Cycle ◽  
Predictive Power ◽  
Turning Points ◽  
Probit Model ◽  
Economic Conditions ◽  
Var Model ◽  
Leading Indicators ◽  
Out Of Sample ◽  
The Business Cycle

Abstract This paper aims at assessing the usefulness of leading indicators in business cycle research and forecast. Initially we test the predictive power of the economic sentiment indicator (ESI) within a static probit model as a leading indicator, commonly perceived to be able to provide a reliable summary of the current economic conditions. We further proceed analyzing how well an extended set of indicators performs in forecasting turning points of the Macedonian business cycle by employing the Qual VAR approach of Dueker (2005). In continuation, we evaluate the quality of the selected indicators in pseudo-out-of-sample context. The results show that the use of survey-based indicators as a complement to macroeconomic data work satisfactory well in capturing the business cycle developments in Macedonia.

Review of the 2009 offshore petroleum exploration release areas

2009 ◽  
Vol 49 (1) ◽  
pp. 465
Author(s):  
Thomas Bernecker
Keyword(s):  
Deep Water ◽  
South Australia ◽  
Northern Territory ◽  
Data Availability ◽  
Petroleum Exploration ◽  
Release Date ◽  
North West ◽  
The North ◽  
Release Area ◽  
Block Sizes

The Australian Government formally releases new offshore exploration areas at the annual APPEA conference. This year, 31 areas plus two special areas in five offshore basins are being released for work program bidding. Closing dates for bid submissions are either six or twelve months after the release date (i.e. 3 December 2009 and 29 April 2010), depending on the exploration status in these areas is and on data availability. The 2009 release areas are located in Commonwealth waters offshore Northern Territory, Western Australia, South Australia and Victoria, comprising intensively explored areas close to existing production as well as new frontiers. As usual, the North West Shelf features very prominently and is complimented by new areas along the southern margin, including frontier exploration areas in the Ceduna Sub-basin (Bight Basin) and the Otway Basin. The Bonaparte Basin is represented by one release area in the Malita Graben, while five areas are available in the Southern Browse Basin in an under-explored area of the basin. A total of 14 areas are being released in the Carnarvon Basin, with eight areas located in the Dampier Sub-basin, three small blocks in the Rankin Platform and three large blocks on the Northern Exmouth Plateau (these are considered a deep water frontier). In the south, six large areas are on offer in the Ceduna Sub-basin and five areas of varying sizes are being released in the Otway Basin, including a deep water frontier offshore Victoria. The special release areas are located in the Petrel Sub-basin, Bonaparte Basin offshore Northern Territory, and encompass the Turtle/Barnett oil discoveries. The 2009 offshore acreage release offers a wide variety of block sizes in shallow as well as deep water environments. Area selection has been undertaken in consultation with industry, the states and Territory. This year’s acreage release caters for the whole gamut of exploration companies given that many areas are close to existing infrastructure while others are located in frontier offshore regions. As part of Geoscience Australia’s Offshore Energy Security Program, new data has been acquired in offshore frontier regions and have yielded encouraging insights into the hydrocarbon prospectivity of the Ceduna-Sub-basin.

scholarly journals OPTIMIZING POLICYMAKERS’ LOSS FUNCTIONS IN CRISIS PREDICTION: BEFORE, WITHIN OR AFTER?

2019 ◽  
pp. 1-24
Author(s):  
Peter Sarlin ◽  
Gregor von Schweinitz
Keyword(s):  
Early Warning ◽  
Loss Functions ◽  
Model Specification ◽  
Binary Choice ◽  
Forecast Errors ◽  
Ex Post ◽  
Binary Choice Models ◽  
Out Of Sample ◽  
Threshold Setting ◽  
Two Alternatives

Recurring financial instabilities have led policymakers to rely on early-warning models to signal financial vulnerabilities. These models rely on ex-post optimization of signaling thresholds on crisis probabilities accounting for preferences between forecast errors, but come with the crucial drawback of unstable thresholds in recursive estimations. We propose two alternatives for threshold setting with similar or better out-of-sample performance: (i) including preferences in the estimation itself and (ii) setting thresholds ex-ante according to preferences only. Given probabilistic model output, it is intuitive that a decision rule is independent of the data or model specification, as thresholds on probabilities represent a willingness to issue a false alarm vis-à-vis missing a crisis. We provide real-world and simulation evidence that this simplification results in stable thresholds, while keeping or improving on out-of-sample performance. Our solution is not restricted to binary-choice models, but directly transferable to the signaling approach and all probabilistic early-warning models.

scholarly journals Empirical prediction intervals improve energy forecasting

2017 ◽  
Vol 114 (33) ◽  
pp. 8752-8757 ◽  
Author(s):  
Lynn H. Kaack ◽  
Jay Apt ◽  
M. Granger Morgan ◽  
Patrick McSharry
Keyword(s):  
Energy Information Administration ◽  
Forecast Errors ◽  
Energy Forecasting ◽  
Empirical Prediction ◽  
Probabilistic Forecasts ◽  
Uncertainty Estimates ◽  
Gaussian Density ◽  
Density Forecasting ◽  
Out Of Sample ◽  
Empirical Density

Hundreds of organizations and analysts use energy projections, such as those contained in the US Energy Information Administration (EIA)’s Annual Energy Outlook (AEO), for investment and policy decisions. Retrospective analyses of past AEO projections have shown that observed values can differ from the projection by several hundred percent, and thus a thorough treatment of uncertainty is essential. We evaluate the out-of-sample forecasting performance of several empirical density forecasting methods, using the continuous ranked probability score (CRPS). The analysis confirms that a Gaussian density, estimated on past forecasting errors, gives comparatively accurate uncertainty estimates over a variety of energy quantities in the AEO, in particular outperforming scenario projections provided in the AEO. We report probabilistic uncertainties for 18 core quantities of the AEO 2016 projections. Our work frames how to produce, evaluate, and rank probabilistic forecasts in this setting. We propose a log transformation of forecast errors for price projections and a modified nonparametric empirical density forecasting method. Our findings give guidance on how to evaluate and communicate uncertainty in future energy outlooks.

scholarly journals ROBUST FORECAST COMPARISON

2016 ◽  
Vol 33 (6) ◽  
pp. 1306-1351 ◽  
Author(s):  
Sainan Jin ◽  
Valentina Corradi ◽  
Norman R. Swanson
Keyword(s):  
Loss Function ◽  
Forecast Accuracy ◽  
Forecast Errors ◽  
Local Power ◽  
Multiple Model ◽  
Test Statistics ◽  
Misspecified Models ◽  
Finite Samples ◽  
Out Of Sample ◽  
Forecast Comparison

Forecast accuracy is typically measured in terms of a given loss function. However, as a consequence of the use of misspecified models in multiple model comparisons, relative forecast rankings are loss function dependent. In order to address this issue, a novel criterion for forecast evaluation that utilizes the entire distribution of forecast errors is introduced. In particular, we introduce the concepts of general-loss (GL) forecast superiority and convex-loss (CL) forecast superiority; and we develop tests for GL (CL) superiority that are based on an out-of-sample generalization of the tests introduced by Linton, Maasoumi, and Whang (2005, Review of Economic Studies 72, 735–765). Our test statistics are characterized by nonstandard limiting distributions, under the null, necessitating the use of resampling procedures to obtain critical values. Additionally, the tests are consistent and have nontrivial local power, under a sequence of local alternatives. The above theory is developed for the stationary case, as well as for the case of heterogeneity that is induced by distributional change over time. Monte Carlo simulations suggest that the tests perform reasonably well in finite samples, and an application in which we examine exchange rate data indicates that our tests can help identify superior forecasting models, regardless of loss function.

scholarly journals Global drivers of species variation in mobilized point-occurrence information

2015 ◽  
Author(s):  
Carsten Meyer ◽  
Walter Jetz ◽  
Robert P Guralnick ◽  
Susanne A Fritz ◽  
Holger Kreft
Keyword(s):  
Species Level ◽  
Data Availability ◽  
Economic Conditions ◽  
Species Variation ◽  
Level Variation ◽  
Mammal Species ◽  
Data Limitations ◽  
Global Drivers ◽  
Occurrence Records

Despite the central role of species distributions in ecology and conservation, occurrence information remains geographically and taxonomically incomplete and biased. Numerous socio-economic and ecological drivers of uneven record collection and mobilization among species have been suggested, but the generality of their effects remains untested. We develop scale-independent metrics of range coverage and geographical record bias, and apply them to 2.8M point-occurrence records of 3,625 mammal species to evaluate 13 putative drivers of species-level variation in data availability. We find that data limitations are mainly linked to range size and shape, and the geography of socio-economic conditions. Surprisingly, species attributes related to detection and collection probabilities, such as body size or diurnality, are much weaker predictors of the amount and range coverage of available records. Our results highlight the need to prioritize range-restricted species and to address the key socio-economic drivers of data bias in data mobilization efforts and distribution modeling.

scholarly journals Risk-based constraints with correlated uncertainties for the optimal operation of an energy community

2021 ◽  
Author(s):  
Mihály Dolányi ◽  
Kenneth Bruninx ◽  
Jean-François Toubeau ◽  
Erik Delarue
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Measurement Data ◽  
Real Life ◽  
Optimal Operation ◽  
Conditional Value At Risk ◽  
Forecast Errors ◽  
Worst Case ◽  
Life Measurement ◽  
Out Of Sample

<div>This paper formulates an energy community's centralized optimal bidding and scheduling problem as a time-series scenario-driven stochastic optimization model, building on real-life measurement data. In the presented model, a surrogate battery storage system with uncertain state-of-charge (SoC) bounds approximates the portfolio's aggregated flexibility. </div><div>First, it is emphasized in a stylized analysis that risk-based energy constraints are highly beneficial (compared to chance-constraints) in coordinating distributed assets with unknown costs of constraint violation, as they limit both violation magnitude and probability. The presented research extends state-of-the-art models by implementing a worst-case conditional value at risk (WCVaR) based constraint for the storage SoC bounds. Then, an extensive numerical comparison is conducted to analyze the trade-off between out-of-sample violations and expected objective values, revealing that the proposed WCVaR based constraint shields significantly better against extreme out-of-sample outcomes than the conditional value at risk based equivalent.</div><div>To bypass the non-trivial task of capturing the underlying time and asset-dependent uncertain processes, real-life measurement data is directly leveraged for both imbalance market uncertainty and load forecast errors. For this purpose, a shape-based clustering method is implemented to capture the input scenarios' temporal characteristics.</div>

Analysts, Taxes, and the Information Environment

2019 ◽  
Vol 42 (1) ◽  
pp. 103-131 ◽  
Author(s):  
Sangwan Kim ◽  
Andrew P. Schmidt ◽  
Kelly Wentland
Keyword(s):  
Accounting Information ◽  
Earnings Forecasts ◽  
Information Environment ◽  
Data Availability ◽  
Forecast Errors ◽  
Complete Understanding ◽  
The Public ◽  
Earnings Information ◽  
Earnings Components ◽  
Jel Classifications

ABSTRACT This paper investigates the extent to which analysts incorporate tax-based earnings information into their earnings forecasts relative to other earnings information. We find that analysts' misreaction to tax-based earnings information is distinct from their misreaction to other (nontax) accounting information, on average. We then show that analysts differ in their misestimation of tax and other (nontax) earnings components only when firms have weak information environments; when firms have strong information environments, analysts' forecasts fully incorporate tax-based earnings information and exhibit no difference incorporating tax-based earnings information relative to other accounting information. Our evidence suggests that, on average, forecasting tax-based earnings information is more difficult for analysts relative to forecasting other accounting information. However, access to appropriate information and resources enables analysts to better process tax information. Overall, we contribute to the literature by providing a more complete understanding of the source of analysts' tax-related forecast errors. JEL Classifications: H25; M41; D82; G14. Data Availability: Data are available from the public sources identified in the text.

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