This paper analyses straightforward Bonferroni adjustments to critical values of in-sample tests of predictability, when data mining is used to search across models. Unlike conventional pseudo-out-of-sample tests, these in-sample tests have stable family-wise error rates (FWERs) when searching for models that predict well. Furthermore, when data mining, these in-sample tests have more power than pseudo-out-of-sample

The seed of this special section was the workshop celebrated at FUNCAS in Madrid in February 2019 “30 Years of Cointegration and Dynamic Factor Models Forecasting and its Future with Big Data”. In this editorial, we describe the main contributions of the 13 papers published within the special section towards forecasting in the context of non- stationary Big Data using cointegration or Dynamic Factor

Proper scoring rules are used to assess the out-of-sample accuracy of probabilistic forecasts, with different scoring rules rewarding distinct aspects of forecast performance. Herein, we re-investigate the practice of using proper scoring rules to produce probabilistic forecasts that are ‘optimal’ according to a given score and assess when their out-of-sample accuracy is superior to alternative forecasts

The random coefficient state-space model was first introduced by McKenzie and Gardner (2010). This model is a stochastic combination of simple and double exponential smoothing, a desirable feature for time-series forecasting. This paper provides a simple method to estimate the random coefficient state-space model parameters by exploiting the link between the model’s autocovariance and the Kalman filter

Sparse and short news headlines can be arbitrary, noisy, and ambiguous, making it difficult for classic topic model LDA (latent Dirichlet allocation) designed for accommodating long text to discover knowledge from them. Nonetheless, some of the existing research about text-based crude oil forecasting employs LDA to explore topics from news headlines, resulting in a mismatch between the short text and

This paper proposes a three-step approach to forecasting time series of electricity consumption at different levels of household aggregation. These series are linked by hierarchical constraints—global consumption is the sum of regional consumption, for example. First, benchmark forecasts are generated for all series using generalized additive models. Second, for each series, the aggregation algorithm

Hawkes processes are used in statistical modeling for event clustering and causal inference, while they also can be viewed as stochastic versions of popular compartmental models used in epidemiology. Here we show how to develop accurate models of COVID-19 transmission using Hawkes processes with spatial–temporal covariates. We model the conditional intensity of new COVID-19 cases and deaths in the

Accurate demand forecasting is one of the key aspects for successfully managing restaurants and staff canteens. In particular, properly predicting future sales of menu items allows for a precise ordering of food stock. From an environmental point of view, this ensures a low level of pre-consumer food waste, while from the managerial point of view, this is critical to the profitability of the restaurant

Credit scoring model development is very important for the lending decisions of financial institutions. The creditworthiness of borrowers is evaluated by assessing their hard and soft information. However, microfinance borrowers are very sensitive to a local economic downturn and extreme (weather or climate) events. Therefore, this paper is devoted to extending the standard credit scoring models by

Understanding changes in the frequency, severity, and seasonality of daily temperature extremes is important for public policy decisions regarding heat waves and cold snaps. A heat wave is sometimes defined in terms of both the daily minimum and maximum temperature, which necessitates the generation of forecasts of their joint distribution. In this paper, we develop time series models with the aim

Studies of financial market informational efficiency have proven burdensome in practice, because it is difficult to pinpoint when news breaks and is known by some or all the participants. We overcome this by designing a framework to detect mispricing, test informational efficiency and evaluate the behavioural biases within high-frequency prediction markets. We demonstrate this using betting exchange

We forecast CPI inflation in the United Kingdom up to one year ahead using a large set of monthly disaggregated CPI item series combined with a wide set of forecasting tools, including dimensionality reduction techniques, shrinkage methods and non-linear machine learning models. It is found that exploiting CPI item series over the 2011–2019 period yields strong improvements in forecasting UK inflation

National Statistical Institutes (NSIs) must balance between timeliness and accuracy of the indicators they publish. Because some of the house sales transactions are reported several months after they occur, many countries that include Israel, publish provisional house price indices (HPIs) that are subject to large revisions as further transactions are reported. This happens because the late-reported

We propose a new method to explore the information content of fixed-event forecasts and estimate structural parameters that are keys to sticky and noisy information models. Estimation follows a regression-based framework in which estimated coefficients map one-to-one with parameters that measure the degree of information rigidity. The statistical characterization of regression errors explores the laws

This study evaluates a wide range of machine learning techniques such as deep learning, boosting, and support vector regression to predict the collection rate of more than 65,000 defaulted consumer credits from the telecommunications sector that were bought by a German third-party company. Weighted performance measures were defined based on the value of exposure at default for comparing collection

In a low-dimensional linear regression setup, considering linear transformations/combinations of predictors does not alter predictions. However, when the forecasting technology either uses shrinkage or is nonlinear, it does. This is precisely the fabric of the machine learning (ML) macroeconomic forecasting environment. Pre-processing of the data translates to an alteration of the regularization –

A new semi-parametric expected shortfall (ES) estimation and forecasting framework is proposed. The proposed approach is based on a two-step estimation procedure. The first step involves the estimation of value at risk (VaR) at different quantile levels through a set of quantile time series regressions. Then, the ES is computed as a weighted average of the estimated quantiles. The quantile weighting

Estimation and prediction in high dimensional multivariate factor stochastic volatility models is an important and active research area, because such models allow a parsimonious representation of multivariate stochastic volatility. Bayesian inference for factor stochastic volatility models is usually done by Markov chain Monte Carlo methods (often by particle Markov chain Monte Carlo methods), which

Retailers supply a wide range of stock keeping units (SKUs), which may differ for example in terms of demand quantity, demand frequency, demand regularity, and demand variation. Given this diversity in demand patterns, it is unlikely that any single model for demand forecasting can yield the highest forecasting accuracy across all SKUs. To save costs through improved forecasting, there is thus a need

Orthogonal polynomials can be used to modify the moments of the distribution of a random variable. In this paper, polynomially adjusted distributions are employed to model the skewness and kurtosis of the conditional distributions of GARCH models. To flexibly capture the skewness and kurtosis of data, the distributions of the innovations that are polynomially reshaped include, besides the Gaussian

We introduce various methods that combine forecasts using constrained optimization with penalty. A non-negativity constraint is imposed on the weights, and several penalties are considered, taking the form of a divergence from a reference combination scheme. In contrast with most of the existing approaches, our framework performs forecast selection and combination in one step, allowing for potentially

Multi-horizon forecasting often contains a complex mix of inputs – including static (i.e. time-invariant) covariates, known future inputs, and other exogenous time series that are only observed in the past – without any prior information on how they interact with the target. Several deep learning methods have been proposed, but they are typically ‘black-box’ models that do not shed light on how they

Leveraging the increasing availability of ”big data” to inform forecasts of labor market activity is an active, yet challenging, area of research. Often, the primary difficulty is finding credible ways with which to consistently identify key elasticities necessary for prediction. To illustrate, we utilize a state-level event-study focused on the costliest hurricanes to hit the U.S. mainland since 2004

We propose a new forecast combination method for panel data vector autoregressions that permit limited forms of parameterized heterogeneity (including fixed effects or incidental trends). Models are fitted using bias-corrected least squares in order to attenuate the effects of small sample bias of forecast loss. We begin by constructing a general estimator of the quadratic forecast risk of the averaged

The rounding of point forecasts of CPI inflation and the unemployment rate by U.S. Professional Forecasters is modest. There is little evidence that forecasts are rounded to a greater extent in response to higher perceived uncertainty surrounding future outcomes. There is clear evidence that the probability of decline forecasts are rounded: over half of the forecast probabilities of decline in the

Election forecasting is a cottage industry among pollsters, the media, political scientists, and political anoraks. Here, we plow a fresh field in providing a systematic exploration of election forecasting in Ireland. We develop a structural forecast model for predicting incumbent government support in Irish general elections between 1977 and 2020 (the Iowa model). We contrast this structural model

There are two potential directions of forecast combination: combining for adaptation and combining for improvement. The former direction targets the performance of the best forecaster, while the latter attempts to combine forecasts to improve on the best forecaster. It is often useful to infer which goal is more appropriate so that a suitable combination method may be used. This paper proposes an AI-AFTER

Factor modeling is a powerful statistical technique that permits common dynamics to be captured in a large panel of data with a few latent variables, or factors, thus alleviating the curse of dimensionality. Despite its popularity and widespread use for various applications ranging from genomics to finance, this methodology has predominantly remained linear. This study estimates factors nonlinearly

Despite the extensive amount of data generated and stored during the maintenance capacity planning process, Maintenance, Repair, and Overhaul (MRO) organizations have yet to explore their full potential in forecasting the required capacity to face future and unprecedented maintenance interventions. This paper explores the integration of time series forecasting capabilities in a tool for maintenance

The national accounts provide a coherent and exhaustive description of the current state of the economy, but are available at the quarterly frequency and are released with a nonignorable publication lag. The paper illustrates a method for nowcasting and forecasting the sixteen main components of Gross Domestic Product (GDP) by output and expenditure type at the monthly frequency, using a high-dimensional

Decision-makers often collect and aggregate experts’ point predictions about continuous outcomes, such as stock returns or product sales. In this article, we model experts as Bayesian agents and show that means, including the (weighted) arithmetic mean, trimmed means, median, geometric mean, and essentially all other measures of central tendency, do not use all information in the predictions. Intuitively