FACTOR MODELS AND TIME‐VARYING PARAMETER FRAMEWORK FOR FORECASTING EXCHANGE RATES AND INFLATION: A SURVEY

A survey of models used for forecasting exchange rates and inflation reveals that the factor‐based and time‐varying parameter or state space models generate superior forecasts relative to all other models. This survey also finds that models based on Taylor rule and portfolio balance theory have moderate predictive power for forecasting exchange rates. The evidence on the use of Bayesian Model Averaging approach in forecasting exchange rates reveals limited predictive power, but strong support for forecasting inflation. Overall, the evidence overwhelmingly points to the context of the forecasts, relevance of the historical data, data transformation, choice of the benchmark, selected time horizons, sample period and forecast evaluation methods as the crucial elements in selecting forecasting models for exchange rate and inflation.     

LoMEF: A framework to produce local explanations for global model time series forecasts

Global forecasting models (GFMs) that are trained across a set of multiple time series have shown superior results in many forecasting competitions and real-world applications compared with univariate forecasting approaches. One aspect of the popularity of statistical forecasting models such as ETS and ARIMA is their relative simplicity and interpretability (in terms of relevant lags, trend, seasonality, and other attributes), while GFMs typically lack interpretability, especially relating to particular time series. This reduces the trust and confidence of stakeholders when making decisions based on the forecasts without being able to understand the predictions. To mitigate this problem, we propose a novel local model-agnostic interpretability approach to explain the forecasts from GFMs. We train simpler univariate surrogate models that are considered interpretable (e.g., ETS) on the predictions of the GFM on samples within a neighbourhood that we obtain through bootstrapping, or straightforwardly as the one-step-ahead global black-box model forecasts of the time series which needs to be explained. After, we evaluate the explanations for the forecasts of the global models in both qualitative and quantitative aspects such as accuracy, fidelity, stability, and comprehensibility, and are able to show the benefits of our approach.     

A Bayesian approach for predicting food and beverage sales in staff canteens and restaurants

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. Hence, we are interested in predicting future values of the daily sold quantities of given menu items. The corresponding time series show multiple strong seasonalities, trend changes, data gaps, and outliers. We propose a forecasting approach that is solely based on the data retrieved from point-of-sale systems and allows for a straightforward human interpretation. Therefore, we propose two generalized additive models for predicting future sales. In an extensive evaluation, we consider two data sets consisting of multiple time series collected at a casual restaurant and a large staff canteen and covering a period of 20 months. We show that the proposed models fit the features of the considered restaurant data. Moreover, we compare the predictive performance of our method against the performance of other well-established forecasting approaches.     

MGARCH models: Trade-off between feasibility and flexibility

Multivariate GARCH (MGARCH) models need to be restricted so that their estimation is feasible in large systems and so that the covariance stationarity and positive definiteness of conditional covariance matrices are guaranteed. This paper analyzes the limitations of some of the popular restricted parametric MGARCH models that are often used to represent the dynamics observed in real systems of financial returns. These limitations are illustrated using simulated data generated by general VECH models of different dimensions in which volatilities and correlations are interrelated. We show that the restrictions imposed by the BEKK model are very unrealistic, generating potentially misleading forecasts of conditional correlations. On the other hand, models based on the DCC specification provide appropriate forecasts. Alternative estimators of the parameters are important in order to simplify the computations, and do not have implications for the estimates of conditional correlations. The implications of the restrictions imposed by the different specifications of MGARCH models considered are illustrated by forecasting the volatilities and correlations of a five-dimensional system of exchange rate returns.     

Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts?

Recent electricity price forecasting studies have shown that decomposing a series of spot prices into a long-term trend-seasonal and a stochastic component, modeling them independently and then combining their forecasts, can yield more accurate point predictions than an approach in which the same regression or neural network model is calibrated to the prices themselves. Here, considering two novel extensions of this concept to probabilistic forecasting, we find that (i) efficiently calibrated non-linear autoregressive with exogenous variables (NARX) networks can outperform their autoregressive counterparts, even without combining forecasts from many runs, and that (ii) in terms of accuracy it is better to construct probabilistic forecasts directly from point predictions. However, if speed is a critical issue, running quantile regression on combined point forecasts (i.e., committee machines) may be an option worth considering. Finally, we confirm an earlier observation that averaging probabilities outperforms averaging quantiles when combining predictive distributions in electricity price forecasting.     

Forecasting stock price volatility: New evidence from the GARCH-MIDAS model

This paper introduces a combination of asymmetry and extreme volatility effects in order to build superior extensions of the GARCH-MIDAS model for modeling and forecasting the stock volatility. Our in-sample results clearly verify that extreme shocks have a significant impact on the stock volatility and that the volatility can be influenced more by the asymmetry effect than by the extreme volatility effect in both the long and short term. Out-of-sample results with several robustness checks demonstrate that our proposed models can achieve better performances in forecasting the volatility. Furthermore, the improvement in predictive ability is attributed more strongly to the introduction of asymmetry and extreme volatility effects for the short-term volatility component.     

Estimating Precision and Recall for Deterministic and Probabilistic Record Linkage

Linking administrative, survey and census files to enhance dimensions such as time and breadth or depth of detail is now common. Because a unique person identifier is often not available, records belonging to two different units (e.g. people) may be incorrectly linked. Estimating the proportion of links that are correct, called Precision, is difficult because, even after clerical review, there will remain uncertainty about whether a link is in fact correct or incorrect. Measures of Precision are useful when deciding whether or not it is worthwhile linking two files, when comparing alternative linking strategies and as a quality measure for estimates based on the linked file. This paper proposes an estimator of Precision for a linked file that has been created by either deterministic (or rules‐based) or probabilistic (where evidence for a link being a match is weighted against the evidence that it is not a match) linkage, both of which are widely used in practice. This paper shows that the proposed estimators perform well.     

Evaluating factor forecasts for the UK: The role of asset prices

This paper applies a large data set, consisting of 167 monthly time series for the UK, both economic and financial, to simulate out-of-sample predictions of industrial production, inflation, 3-month Treasury Bills, and other variables. Fifteen dynamic factor models that allow forecasting based on large panels of time series are considered. The performances of these factor models are then compared to the following competing models: a simple univariate autoregressive, a vector autoregressive, a leading indicator, and a Phillips curve models. The results show that the best dynamic factor models outperform the competing models in forecasting at 6-, 12-, and 24-month horizons. Thus, the financial markets may have predictive power for the economic activity. This can be a useful tool for central banks and financial institutions, which may use the factor models to construct leading indicators of the economic conditions. In addition, researchers can see a strategic application of factor models.     

Spatio-temporal probabilistic forecasting of wind power for multiple farms: A copula-based hybrid model

Accurate probabilistic forecasting of wind power output is critical to maximizing network integration of this clean energy source. There is a large literature on temporal modeling of wind power forecasting, but considerably less work combining spatial dependence into the forecasting framework. Through the careful consideration of the temporal modeling component, complemented by support vector regression of the temporal model residuals, this work demonstrates that a DVINE copula model most accurately represents the residual spatial dependence. Additionally, this work proposes a complete set of validation mechanisms for multi-h-step forecasts that, when considered together, comprehensively evaluate accuracy. The model and validation mechanisms are demonstrated in two case studies, totaling ten wind farms in the Texas electric grid. The proposed method outperforms baseline and competitive models, with an average Continuous Ranked Probability Score of less than 0.15 for individual farms, and an average Energy Score of less than 0.35 for multiple farms, over the 24-hour-ahead horizon. Results show the model’s ability to replicate the power output dynamics through calibrated and sharp predictive densities.     

Modeling undecided voters to forecast elections: From bandwagon behavior and the spiral of silence perspective

Forecasting election results has been a highly attractive activity among political and social scientists. Different forecasting methods have been proposed, but those based on public opinion polls are the most common. However, there are challenges to using opinion polls, especially because they neglect undecided voters. Due to the significant number of undecided participants and their impact on voting outcomes, we analyze the potential behavior of undecided voters by considering opinion polls and sentiment based on voter expectation from the perspective of the bandwagon effect and the spiral of silence. We establish a hierarchical Bayesian forecasting model to predict voting results, and apply it to the 2016 United States presidential election and the 2016 Brexit referendum. The results of our model suggest that voting outcomes are more predictable when fully utilizing the impact of undecided voters. The results indicate that integrating aggregated polls into the hierarchical Bayesian framework is a strong predictor for forecasting outcomes, and they provide evidence for the influence of sentiment based on voter expectation in forecasting election results.     

Out-of-sample prediction of Bitcoin realized volatility: Do other cryptocurrencies help?

Inspired by cross-market information flows among international stock markets, we incorporate external predictive information from other cryptocurrency markets to forecast the realized volatility (RV) of Bitcoin. To make the most of such external information, we employ six widely accepted approaches to construct predictive models based on multivariate information. Our results suggest that the scaled principal component analysis (SPCA) approach steadily improves the predictive ability of the prevailing heterogeneous autoregressive (HAR) benchmark model considering both the model confidence set (MCS) test and the Diebold–Mariano (DM) test based on three widely accepted loss functions. The forecasting performance is persistent to various robustness checks and extensions. Notably, a mean–variance investor can obtain steady positive economic gains if the investment portfolio is constructed on the basis of the forecasts from the HAR-SPCA model. The results of this study show that external predictive information is statistically and economically important in forecasting Bitcoin RV.     

Multinomial modeling methods: Predicting four decades of international banking crises

This paper examines banking crises in a large sample of countries over a forty-year period. A multinomial modeling approach is applied to panel data in order to track and capture end-to-end cyclical crisis formations, which enhances the binary focus of previous research studies. Several macroeconomic and banking sector variables are shown to be emblematic of leading indicators across the idiosyncratic stages of a banking crisis. Gross domestic product is an early warning signal across all phases, and a concomitant deterioration in consumption spending and fixed capital formation, preceded by a credit boom, signal a banking crisis to come. Currency depreciation exemplifies ensuing financial distress, reinforced by developmental constructs and regional integration. Lower real interest rates, increasing imports, and rising deposits are frequently harbingers of a recovery. Period effects underscore the dynamic evolution of common contemporaneous precursors over time. Premised on pursuing cyclical movements through multiple outcomes, our findings on forecasting performance suggest enhanced predictive power. Several multinomial logistic models generate higher predictive accuracy in contrast to probit models. Compared to machine learning methods (which encompass artificial neural networks, gradient boost, k-nearest neighbors, and random forests methods), a multinomial logistic approach outperforms during pre-crisis periods and when crisis severity is modeled, whereas gradient boost has the highest predictive accuracy across numerous versions of the multinomial model. As investors and policy makers continue to confront banking crises, leading to high economic and social costs, enhanced multinomial modeling methods make a valuable contribution to improved forecasting performance.     

A comparison of monthly global indicators for forecasting growth

This paper evaluates the predictive content of a set of alternative monthly indicators of global economic activity for nowcasting and forecasting quarterly world real GDP growth using mixed-frequency models. It shows that a recently proposed indicator that covers multiple dimensions of the global economy consistently produces substantial improvements in forecasting accuracy, while other monthly measures have more mixed success. Specifically, the best-performing model yields impressive gains with MSPE reductions of up to 34% at short horizons and up to 13% at long horizons relative to an autoregressive benchmark. The global economic conditions indicator also contains valuable information for assessing the current and future state of the economy for a set of individual countries and groups of countries. This indicator is used to track the evolution of the nowcasts for the U.S., the OECD area, and the world economy during the COVID-19 pandemic and the main factors that drive the nowcasts are quantified.     

Forecasting global equity market volatilities

Motivated by a common belief that the international stock market volatilities are synonymous with information flow, this paper proposes a parsimonious way to combine multiple market information flows and assess whether cross-national volatility flows contain important information content that can improve the accuracy of international volatility forecasting. We concentrate on realized volatilities (RV) derived from the intra-day prices of 22 international stock markets, and employ the heterogeneous autoregressive (HAR) framework, along with two common diffusion indices that are constructed based on the simple mean and first principal component (PC) of the 22 stock market RVs, to forecast future volatilities of each market for 1-day, 1-week, and 1-month ahead. We provide strong evidence that the use of the cross-national information reflected by the simple and parsimonious common indices enhances the predictive accuracy of international volatilities at all forecasting horizons. Alternative volatility measures, estimation window sizes, and forecasting evaluation tests confirm the robustness of our results. Finally, our strategy of constructing common diffusion indices is also feasible for international market jumps.     

Forecasting crude oil price volatility

We use high-frequency intra-day realized volatility data to evaluate the relative forecasting performances of various models that are used commonly for forecasting the volatility of crude oil daily spot returns at multiple horizons. These models include the RiskMetrics, GARCH, asymmetric GARCH, fractional integrated GARCH and Markov switching GARCH models. We begin by implementing Carrasco, Hu, and Ploberger’s (2014) test for regime switching in the mean and variance of the GARCH(1, 1), and find overwhelming support for regime switching. We then perform a comprehensive out-of-sample forecasting performance evaluation using a battery of tests. We find that, under the MSE and QLIKE loss functions: (i) models with a Student’s t innovation are favored over those with a normal innovation; (ii) RiskMetrics and GARCH(1, 1) have good predictive accuracies at short forecast horizons, whereas EGARCH(1, 1) yields the most accurate forecasts at medium horizons; and (iii) the Markov switching GARCH shows a superior predictive accuracy at long horizons. These results are established by computing the equal predictive ability test of Diebold and Mariano (1995) and West (1996) and the model confidence set of Hansen, Lunde, and Nason (2011) over the entire evaluation sample. In addition, a comparison of the MSPE ratios computed using a rolling window suggests that the Markov switching GARCH model is better at predicting the volatility during periods of turmoil.     

Forecasting bulk prices of Bordeaux wines using leading indicators

Agricultural price forecasting has been being abandoned progressively by researchers ever since the development of large-scale agricultural futures markets. However, as with many other agricultural goods, there is no futures market for wine. This paper draws on the agricultural prices forecasting literature to develop a forecasting model for bulk wine prices. The price data include annual and monthly series for various wine types that are produced in the Bordeaux region. The predictors include several leading economic indicators of supply and demand shifts. The stock levels and quantities produced are found to have the highest predictive power. The preferred annual and monthly forecasting models outperform naive random walk forecasts by 27.1% and 3.4% respectively; their mean absolute percentage errors are 2.7% and 3.4% respectively. A simple trading strategy based on monthly forecasts is estimated to increase profits by 3.3% relative to a blind strategy that consists of always selling at the spot price.     

The Predictive Ability of Geographic Segment Information at the Country, Continent, and Consolidated Levels

This study examines whether geographic information disclosed at an increasingly disaggregated level (specifically, consolidated vs. continent vs. country) results in increased predictive ability of company operations (specifically, sales, gross profit, and earnings). Multinational corporations (MNCs) are formed using a simulated merger approach by combining the annual operating results of six individual firms, one from each of six countries. This approach makes it possible to compare the forecasting accuracy of data disclosed at the country, continent, and consolidated levels, not possible using current geographic segment disclosures. Previous studies using year-ahead forecast models implicitly assume the predictive factors included in the models are significant in forecasting operating results. Using regression forecast models, this study tests whether the predictive factors included in the models are effective in forecasting operating results by examining the direction, size, and significance of the regression coefficient estimates. The coefficients provide evidence that exchange rate changes, inflation, and real GNP growth are useful in forecasting annual sales and gross profit. Whereas, at least for this sample and this time period, exchange rate changes, inflation, and real GNP growth are not significant variables in forecasting annual earnings. The results indicate that the accuracy of forecasts increase as sales and gross profit are disclosed at a more disaggregated geographic level. The hypothesized relationship between consolidated, continent, and country levels, while holding strongly under perfect foresight, holds to a lesser extent using forecasts of exchange rates, inflation, and real GNP.     

基于神经网络集成的失业预警方法

提出采用神经网络集成技术对中国失业预警系统进行建模,以克服当前失业预警系统建模中存在的小样本、高维度、非线性、噪音数据等难题。采用BP神经网络回归模型对失业率进行预测;基于两种集成技术Bagging与AdaBoost对多个神经网络进行集成,以获得比单个预测模型更好的精度与稳定性;最后基于广东省的社会经济调查数据进行了实证分析,实验结果表明:在对失业率的预测上,Bagging集成方法的预测效果优于Adaboost集成方法,也优于单个最好的神经网络模型。     

Forecasting and loss functions

This paper considers two problems of interpreting forecasting competition error statistics. The first problem is concerned with the importance of linking the error measure (loss function) used in evaluating a forecasting model with the loss function used in estimating the model. It is argued that because the variety of uses of any single forecast, such matching is impractical. Secondly, there is little evidence that matching would have any impact on comparative forecast performance, however measured. As a consequence the results of forecasting competitions are not affected by this problem. The second problem is concerned with the interpreting performance, when evaluated through M(ean) S(quare) E(rror). The authors show that in the Makridakis Competition, good MSE performance is solely due to performance on a small number of the 1001 series, and arises because of the effects of scale. They conclude that comparisons of forecasting accuracy based on MSE are subject to major problems of interpretation.