“A Bias Recognized Is a Bias Sterilized”: The Effects of a Bias in Forecast Evaluation

Are traditional tests of forecast evaluation well behaved when the competing (nested) model is biased? No, they are not. In this paper, we show analytically and via simulations that, under the null hypothesis of no encompassing, a bias in the nested model may severely distort the size properties of traditional out-of-sample tests in economic forecasting. Not surprisingly, these size distortions depend on the magnitude of the bias and the persistency of the additional predictors. We consider two different cases: (i) There is both in-sample and out-of-sample bias in the nested model. (ii) The bias is present exclusively out-of-sample. To address the former case, we propose a modified encompassing test (MENC-NEW) robust to a bias in the null model. Akin to the ENC-NEW statistic, the asymptotic distribution of our test is a functional of stochastic integrals of quadratic Brownian motions. While this distribution is not pivotal, we can easily estimate the nuisance parameters. To address the second case, we derive the new asymptotic distribution of the ENC-NEW, showing that critical values may differ remarkably. Our Monte Carlo simulations reveal that the MENC-NEW (and the ENC-NEW with adjusted critical values) is reasonably well-sized even when the ENC-NEW (with standard critical values) exhibits rejections rates three times higher than the nominal size.

Visibility and Ceiling Nowcasting Using Artificial Intelligence Techniques for Aviation Applications

This work presents a novel approach for simulating visibility (Vis) and ceiling base height (Hc) in up to 1 h using several machine learning (ML) algorithms. Ten years of meteorological data at 15 min intervals for Santos Dumont airport (SDA), Rio de Janeiro, Brazil were used in the ML method training and testing process. In the investigation, several categorical and regressive algorithms were trained and tested, and the results were verified with observations. The forecast results reveal that the categorical methods produced satisfactory results only up to 15 min for visibility prediction with the probability of detection greater than 85%. On the other hand, the regressive methods were found to be more capable of generating an accurate prediction of Vis and Hc compared to categorical method up to 60 min. The forecast evaluation metrics for Vis and Hc had correlation coefficients of 0.99 ± 0.00 and 0.96 ± 0.00, with mean absolute errors of 324 ± 77 m, and 167 ± 21 m, respectively. Results suggested that ML methods can improve the prediction of Vis and Hc up to 1 h when accurate observations are used for the analysis.

Multi-Scale Object-Based Probabilistic Forecast Evaluation of WRF-Based CAM Ensemble Configurations

Convection-allowing model (CAM) ensembles contain a distinctive ability to predict convective initiation location, mode, and morphology. Previous studies on CAM ensemble verification have primarily used neighborhood-based methods. A recently introduced object-based probabilistic (OBPROB) framework provides an alternative and novel framework in which to re-evaluate aspects of optimal CAM ensemble design with an emphasis on ensemble storm mode and morphology prediction. Herein, we adopt and extend the OBPROB method in conjunction with a traditional neighborhood-based method to evaluate forecasts of four differently configured 10-member CAM ensembles. The configurations include two single-model/single-physics, a single-model/multi-physics, and a multi-model/multi-physics configuration. Both OBPROB and neighborhood frameworks show that ensembles with more diverse member-to-member designs improve probabilistic forecasts over single-model/single-physics designs through greater sampling of different aspects of forecast uncertainties. Individual case studies are evaluated to reveal the distinct forecast features responsible for the systematic results identified from the different frameworks. Neighborhood verification, even at high reflectivity thresholds, is primarily impacted by mesoscale locations of convective and stratiform precipitation across scales. In contrast, the OBPROB verification explicitly focuses on convective precipitation only and is sensitive to the morphology of similarly located storms.

Forecasting transaction counts with integer-valued GARCH models

Using numerous transaction data on the number of stock trades, we conduct a forecasting exercise with INGARCH models, governed by various conditional distributions; the Poisson, the linear and quadratic negative binomial, the double Poisson and the generalized Poisson. The model parameters are estimated with efficient Markov Chain Monte Carlo methods, while forecast evaluation is done by calculating point and density forecasts.

Apple Inc. Analysis and Forecast Evaluation

Apple has been an American success story for quite a long time. After igniting the personal computer (PC) revolution in the 1970s and reinventing PC in the 1980s, it again brought various innovative and game-changing products, including smartphones, computers, and wearables in recent years. Its dominant product, iPhone, sparked years of massive growth and has become the biggest drive of the company’s success. Besides, with a market capitalization of more than $2T, Apple is currently the world’s most valuable company. This makes many investors interested in AAPL stock. Hence, this paper will explore whether the APPL stock is worth investing based on the analysis of its business model, SWOT analysis, and relative valuation in hope to provide some recommendations and predictions for investors.

Revisions in the Norwegian National Accounts: accuracy, unbiasedness and efficiency in preliminary figures

This paper investigates the quality of preliminary figures in the Norwegian National Accounts. To address the problem of few observations in such analyses, we use some recently developed system tests for forecast evaluation. We find that preliminary figures for growth rates NA figures (measured in real terms) are accurate, unbiased and efficient. The exception is growth rates for real gross fixed capital formation, which under-predict the final figures. Early published vintages of growth rates for real gross fixed capital formation are often closer to the final vintages than later vintages are.

MODELLING THE CONDITIONAL CO-MOVEMENTS OF PAKISTAN AND INTERNATIONAL STOCK MARKETS

Purpose of the study: This study assesses and evaluates the conditional co-movements and dynamic conditional correlation of the Pakistan Stock Exchange (PSX) with other Stock Market. Methodology: DCC-GARCH model has been applied due to its feasibility to model the covariance as a function of correlation and variance together. Main findings: The findings of the research suggest that the Pakistani Stock Exchange (PSX) is highly volatile compared to two other selected stock markets. In-sample fitting, the study has selected the DCC-GARCH (1, 1) model based on information criterion, conversely, the criterion used for out-of-sample forecast evaluation such as MSFE, RMSFE, MAPE, selected the DCC (2,1)-GARCH (1,1). Application of the study: This study is very useful for the Pakistan stock market and other international selected stocks markets until and unless the government of Pakistan and other governments will devise new policies which may open new opportunities to investors. Novelty/ Originality of the study: This study has a great potential in the Pakistani stock market to offer investors to several foreign and domestic investors, allowing them to hold Pakistan as well as foreign and local stocks all major benefits.

Review article: Observations for high-impact weather and their use in verification

Verification of forecasts and warnings of high-impact weather is needed by the meteorological centres, but how to perform it still presents many open questions, starting from which data are suitable as reference. This paper reviews new observations which can be considered for the verification of high-impact weather and provides advice for their usage in objective verification. Two high-impact weather phenomena are considered: thunderstorm and fog. First, a framework for the verification of high-impact weather is proposed, including the definition of forecast and observations in this context and creation of a verification set. Then, new observations showing a potential for the detection and quantification of high-impact weather are reviewed, including remote sensing datasets, products developed for nowcasting, datasets derived from telecommunication systems, data collected from citizens, reports of impacts and claim/damage reports from insurance companies. The observation characteristics which are relevant for their usage in forecast verification are also discussed. Examples of forecast evaluation and verification are then presented, highlighting the methods which can be adopted to address the issues posed by the usage of these non-conventional observations and objectively quantify the skill of a high-impact weather forecast.