Forecast Encompassing Research Articles

The impact of Google Trends index and encompassing tests on forecast combinations in tourism

El impacto de Google Trends en la previsión de viajes combinados y su evidencia relacionadaPropósitoDado que el uso de los datos de Google Trends es útil para mejorar la precisión de las predicciones, este estudio examina si el uso del índice de búsqueda web de Google Trends combinado con la agregación de relevancia puede mejorar la precisión del predictor.Diseño/metodología/enfoqueEl modelo predictivo gris genera predicciones bajo un único modelo, mientras que el modelomultivariado utiliza el indicador Google Trends como variable explicativa. Se generaron tresensamblajes generales, incluido el Modelo armónico único (CUGM), los ensamblajes de todos loscomponentes (CAGM) y la prueba de presencia de componentes con predicción (CSET). Laspredicciones individuales encada grupo luego se combinan utilizando métodos de correlación deuso común.RecomendacionesUtilizando el número de turistas en las cuatro ciudades más investigadas de Taiwán, los tresgrupos combinados se clasificaron según su precisión. Las pruebas incluidas muestran que losmodelos multivariados en escala de grises son importantes para la predicción. Además, losresultados de las pruebas muestran que el índice de Google Trends y las pruebas que incluyenmétodos de suma lineal son útiles porque los métodos combinados con CSET funcionan majorque los métodos combinados con CSET. CAGM y VCUG.Implicaciones practicesLa industria de viajes puede usar el índice de búsqueda web de Google Trends para desarrollarestrategias comerciales para atracciones basadas en un conjunto lineal de componentes.Autenticidad/valorCon el objetivo de mejorar la precisión de los pronósticos agregados, este estudio investiga larelación entre el índice de tendencias de Google y las expectativas generales de viaje junto con laevidencia global.Palabras claveDemanda de viajes, Experiencia global, Tendencias de Google, Predicción grisTipo de papelTrabajo de investigación

Evaluating forecast performance with state dependence

We propose a novel forecast evaluation methodology to assess models’ absolute and relative forecasting performance when it is a state-dependent function of economic variables. In our framework, the forecasting performance, measured by a forecast error loss function, is modeled via a hard or smooth threshold model with unknown threshold values. Existing tests either assume a constant out-of-sample forecast performance or use non-parametric techniques robust to time-variation; consequently, they may lack power against a state-dependent performance. Our tests can be applied to relative forecast comparisons, forecast encompassing, forecast efficiency, and, more generally, moment-based tests of forecast evaluation. Monte Carlo results suggest that our proposed tests perform well in finite samples and have better power than existing tests in selecting the best forecast or assessing its efficiency in the presence of state dependence. Our tests uncover “pockets of predictability” in U.S. equity premia; although the term spread is not a useful predictor on average over the sample, it forecasts significantly better than the benchmark forecast when real GDP growth is low. In addition, we find that leading indicators, such as measures of vacancy postings and new orders for durable goods, improve the forecasts of U.S. industrial production when financial conditions are tight.

Improving Hotel Room Demand Forecasts for Vienna across Hotel Classes and Forecast Horizons: Single Models and Combination Techniques Based on Encompassing Tests

The present study employs daily data made available by the STR SHARE Center covering the period from 1 January 2010 to 31 January 2020 for six Viennese hotel classes and their total. The forecast variable of interest is hotel room demand. As forecast models, (1) Seasonal Naïve, (2) Error Trend Seasonal (ETS), (3) Seasonal Autoregressive Integrated Moving Average (SARIMA), (4) Trigonometric Seasonality, Box–Cox Transformation, ARMA Errors, Trend and Seasonal Components (TBATS), (5) Seasonal Neural Network Autoregression (Seasonal NNAR), and (6) Seasonal NNAR with an external regressor (seasonal naïve forecast of the inflation-adjusted ADR) are employed. Forecast evaluation is carried out for forecast horizons h = 1, 7, 30, and 90 days ahead based on rolling windows. After conducting forecast encompassing tests, (a) mean, (b) median, (c) regression-based weights, (d) Bates–Granger weights, and (e) Bates–Granger ranks are used as forecast combination techniques. In the relative majority of cases (i.e., in 13 of 28), combined forecasts based on Bates–Granger weights and on Bates–Granger ranks provide the highest level of forecast accuracy in terms of typical measures. Finally, the employed methodology represents a fully replicable toolkit for practitioners in terms of both forecast models and forecast combination techniques.

Analysis of USDA Livestock Price Forecasting Accuracy for Cattle, Hogs, and Broilers

Agricultural markets, compared to other sectors, are typically characterized by uncertainty and high price fluctuations. High price volatility in livestock markets leads to inefficient resource allocation and production planning. Expert price forecasts are not always affordable for all market players, so readily available public forecasts have risen in popularity. This study uses accuracy-based testing methods to evaluate the accuracy of the United States Department of Agriculture (USDA) livestock price forecasting by utilizing the World Agricultural Supply and Demand Estimates (WASDE) quarterly data for slaughter cattle, hogs, and broilers. The study also employs a vector error correction (VEC) model to compare USDA price forecasts. Results suggest that the USDA forecast was more accurate than the competing VEC model across three sectors, suggested by low RMSE and MAE. The beta efficiency test results showed that USDA price forecasts were efficient for all three price series, whereas VEC forecasts were biased for hogs and broiler prices. The findings of the study also confirm that USDA price forecasts are biased for cattle prices with a tendency to repeat past forecast error in all three markets. Results from the forecast encompassing tests showed that USDA cattle and broiler forecasts captured the information contained in VEC forecasts. However, because the hog prices did not show any improvement over time, there is room for improvement of the USDA price forecasts. Overall, results suggest that USDA price forecasts for slaughter cattle, hogs and broilers provide useful information to the market. However, the results also indicate that USDA price forecasts reduce forecast error by economically significant levels.

VOLATILITY FORECASTING USING HYBRID GARCH NEURAL NETWORK MODELS: THE CASE OF THE ITALIAN STOCK MARKET

In several financial applications, it is extremely useful to predict volatility with the highest precision. Neural Networks alongside GARCH-type models have been extensively employed in the last decades for estimating volatility of financial indices. The motivation of this survey is to decide whether combining different types of models can improve the return volatility forecasts. Thus, two hybrid models are utilized and compared with an asymmetric GARCH model and a Neural Network in terms of their ability to predict the volatility of the FTSE MIB index. The conclusions reveal that the hybrid model, which is based on a Neural Network having as inputs the returns and its historical values as well as the estimates of conditional volatility obtained by an EGARCH model, provides the best predictive power. Moreover, the dominance of this hybrid model is such that it forecast encompasses the remaining models. Finally, it is demonstrated that there are significant leverage effects in the Italian stock market.Keywords: Artificial Neural Network; Forecast Encompassing; GARCH Models; Realized volatility; Stock Market; Volatility ForecastJEL Classifications: C22; C45; C53; G15DOI: https://doi.org/10.32479/ijefi.10842

Investors’ Uncertainty and Forecasting Stock Market Volatility

This article examines whether incorporating investors’ uncertainty, as captured by the conditional volatility of sentiment, can help forecasting volatility of stock markets. In this regard, using the Markov-switching multifractal (MSM) model, we find that investors’ uncertainty can substantially increase the accuracy of the forecasts of stock market volatility according to the forecast encompassing test. We further provide evidence that the MSM outperforms the dynamic conditional correlation-generalized autoregressive conditional heteroskedasticity (DCC-GARCH) model.

Forecast encompassing tests for the expected shortfall

We introduce new forecast encompassing tests for the risk measure Expected Shortfall (ES). The ES has received much attention since its introduction into the Basel III Accords, which stipulate its use as the primary market risk measure for international banking regulation. We utilize joint loss functions for the pair ES and Value at Risk to set up three ES encompassing test variants. The tests are built on an asymptotic theory that is robust to misspecifications. We investigate the finite sample properties of the tests in an extensive simulation study. Finally, we use the encompassing tests to illustrate the potential of forecast combination methods for different financial assets.

Forecast Encompassing Tests for the Expected Shortfall

We introduce new forecast encompassing tests for the risk measure Expected Shortfall (ES). The ES currently receives much attention through its introduction into the Basel III Accords, which stipulate its use as the primary market risk measure for the international banking regulation. We utilize joint loss functions for the pair ES and Value at Risk to set up three ES encompassing test variants. The tests are built on misspecification robust asymptotic theory and we verify the finite sample properties of the tests in an extensive simulation study. We use the encompassing tests to illustrate the potential of forecast combination methods for different financial assets.

Predictive Performance of Mixed-Frequency Nowcasting and Forecasting Models (with Application to Philippine Inflation and GDP Growth)

This paper studies the comparative predictive accuracy of forecasting methods using mixed-frequency data, as applied to nowcasting Philippine inflation, real GDP growth, and other related macroeconomic variables. It focuses on variations of mixed-frequency dynamic latent factor models (DFM for short) and Mixed Data Sampling (MIDAS) Regression. DFM is parsimonious and dependent on a much smaller data set that needs to be updated regularly but technically and computationally more complicated, especially when there are mixed-frequency data. On the other hand, MIDAS is data-intensive but computationally more tractable. The analysis is done through comparison of forecast performance measures (such as mean squared prediction error) and application of statistical tests of comparative predictive accuracy and tests of forecast encompassing. Results obtained so far indicate that just about every method in the pool of forecasting methods studied performs best in some cases and worst in other cases. Thus, there is no clear winner. Under the circumstances, one viable approach in applications is to combine the forecasts from these powerful techniques to improve predictive accuracy. In most cases, least squares weights perform better for purposes of forecast averaging.

Eurozone or national inflation projections: Which has greater impact on consumer expectations?

We compare the dependence of consumer inflation expectations on European Central Bank (ECB) inflation projections with that on national central bank (NCB) projections in four economies: Austria, Belgium, Finland, and Germany. We aim to assess whether the information published by central banks affects consumers, and whether inflation projections published by NCBs are more relevant to consumers than those published for the entire Eurozone. Inflation expectations were obtained from the Business and Consumer Surveys conducted by the Directorate General for Economic and Financial Affairs of the European Commission and quantified using the probabilistic method. The methodology covers: (1) forecast encompassing tests; (2) the Granger causality test; (3) impulse response analysis complemented by (4) forecast error variance decomposition. The results suggest that the ECB outlook constitutes a more important factor in expectation formation. This article adds to the existing literature by comparing the impact of common and national projections on consumer expectations.

SEASONAL AND PERIODIC AUTOREGRESSIVE TIME SERIES MODELS USED FOR FORECASTING ANALYSIS OF RAINFALL DATA

The amount of rainfall received over an area is an important factor in assessing availability of water to meet various demands for agriculture, industry, irrigation, generation of hydroelectricity and other human activities. In our study, we consider seasonal and periodic time series models for statistical analysis of rainfall data of Punjab, India. In this research paper we apply the Seasonal Autoregressive Integrated Moving Average and Periodic autoregressive model to analyse the rainfall data of Punjab. For evaluation of the model identification and periodic stationarity the statistical tool used are PeACF and PePACF. For model comparison we use Root mean square percentage error and forecast encompassing test. The results of this research will provide local authorities to develop strategic plans and appropriate use of available water resources.

The usefulness of oil price forecasts—Evidence from survey predictions

This paper evaluates survey forecasts for crude oil prices and discusses the implications for decision makers. A novel disaggregated data set incorporating individual forecasts for Brent and Western Texas Intermediate is used. We carry out tests for unbiasedness, sign accuracy, and forecast encompassing, followed by the computation of coefficients for topically oriented trend adjustments and the Theil's U measure. We also control for the forecast horizon finding heterogeneous results. Forecasts are more precise for shorter horizons, but less accurate than the naïve prediction. For longer horizons, topically oriented trend adjustments become more pronounced, but forecasters tend to outperform the naïve predictions.

How Far Can We Forecast? Statistical Tests of the Predictive Content

Forecasts are useless whenever the forecast error variance fails to be smaller than the unconditional variance of the target variable. This paper develops tests for the null hypothesis that forecasts become uninformative beyond some limiting forecast horizon h. Following Diebold and Mariano (DM, 1995) we propose a test based on the comparison of the mean-squared error of the forecast and the sample variance. We show that the resulting test does not possess a limiting normal distribution and suggest two simple modifications of the DM-type test with different limiting null distributions. Furthermore, a forecast encompassing test is developed that tends to better control the size of the test. In our empirical analysis, we apply our tests to macroeconomic forecasts from the survey of Consensus Economics. Our results suggest that forecasts of macroeconomic key variables are barely informative beyond 2-4 quarters ahead.

Modeling and forecasting the volatility of carbon emission market: The role of outliers, time-varying jumps and oil price risk

The objectives of this study are three-fold. First, we aim to assess whether outliers or extreme observations occur in the European Union Allowance (EUA) data. Second, we examine if time-varying jumps are present in the carbon emission market. Third, we use the crude oil volatility index (OVX) to investigate the effect of oil market uncertainty on the emission price volatility. In order to detect the possible outliers in the EUA market, we employ a standard methodology proposed by Ané et al. (2008) and identify several outliers in the emission data. After spotting and then removing those extreme points, we apply the GARCH-jump models to both original and outlier free observations. The results of the jump model show that while time-varying jumps do exist in the uncorrected data, most of the jump parameters, however, become insignificant in case of outlier-free observations. Next, the application of an extended EGARCH model, in which OVX is introduced in the GARCH specification, demonstrates that emission prices are highly sensitive to oil market implied volatility and that the impact of OVX on the EUA market appears to be asymmetric. Additionally, the use of forecast encompassing test documents that considering the outlier-free data and using the information content of OVX would improve the volatility forecasts for the carbon emission market. The results of our research carry important implications for both investors and policymakers.

Modeling and forecasting oil price risk: the role of implied volatility index

PurposeWhile numerous empirical studies have tried to model and forecast the oil price volatility over the years, such attempts using the crude oil volatility index (OVX) rarely exist. In order to conceal this void, the purpose of this paper is to investigate whether including OVX in the realized volatility (RV) models improve the accuracy of predictions.Design/methodology/approachAt the empirical stage, the authors employ several measures to frame the RV of crude oil futures returns. In particular, the authors use three different range-based RV estimators recommended by Parkinson (1980), Rogers and Satchell (1991) and Alizadeh et al. (2002), respectively.FindingsThe findings reveal that the information content of crude OVX helps to provide more accurate volatility predictions in comparison to the base-line RV model which contains only historical oil volatilities. Besides, the forecast encompassing test further suggests that the modified RV model (when OVX is introduced in the base-line RV model) forecast encompasses the conventional RV forecast in majority of the cases.Practical implicationsSince forecasting oil price volatility plays a vital role in portfolio optimization, derivatives pricing, optimum asset allocation decisions and risk management, the findings of this study thus carry important implications for energy economists, investors and policymakers.Originality/valueThis paper adds to the existing literature, since it is one of the initial studies to explore whether OVX is informative about the realized variance of the US oil market returns. The findings recommend that the information content of oil implied volatilities should be taken into account when modeling the US oil market volatility. In addition, range-based measures should be utilized while estimating the RV.

Do VIX and Trading Volume Subsume Incremental Information above GARCH-Type Models for the Volatility Forecast?

The VIX index and trading volume (VO) can subsume information of the future volatility in financial markets, and therefore, have been commonly used as volatility forecasting instruments. Previous studies have identified superior VIX- and VO-based forecasts compared to various GARCH-type models, while contradictory evidence for the outperformance of GARCH-based forecasts rises controversies about the optimal forecast. To evaluate whether VIX and VO may subsume incremental information over GARCH-type models, this paper evaluates the volatility forecast efficiency of VIX, VO and GARCH. Latest daily VIX, VO and SPX during 01/01/2006-17/07/2017 are sorted from Bloomberg. To access the forecast efficiency, Mincer-Zarnowitz (MZ, 1969) regression, the forecast encompassing test and root-mean-square error (RMSE) are to be applied. The present empirical result indicates incremental information in VIX, while VO seems to subsume little or no additional information over GARCH, which contradicts previous findings of Kambouroudis and McMillan (2016). Recent concerns about the influence of financial turmoil on the volatility forecast have generated a considerable body of research; however, most previous investigations of the volatility forecast efficiency have not addressed this issue. Hence, this paper is motivated to identify the effect of 2008 financial crisis on the volatility estimates. Current findings confirm a negative crisis dilution effect occurred in 2012, especially for VO-based predictions. The identified dilution effect demonstrated a negative influence of turmoil on volatility forecasts due to the disruption of the economic condition during the post-crisis period. Further work is recommended on validating this issue in various financial markets, such as foreign exchange markets.

Has the forecasting performance of the Federal Reserve’s Greenbooks changed over time?

AbstractWe investigate how the forecasting performance of the Federal Reserve Greenbooks has changed relative to commercial forecasters between 1974 and 2009. To this end, we analyze time-variation in the Greenbook coefficients in forecast encompassing regressions. Assuming that model coefficients change continuously, we estimate unobserved components models using Bayesian inference techniques. To verify that our results do not depend on the specific way change is modeled, we also allow the coefficients to change discretely rather than continuously and test for structural breaks using classical inference techniques. We find that the Greenbook forecasts have been consistently superior to the commercial forecasts at all horizons throughout our sample period. Although the forecasting performance gap has narrowed at more distant horizons after the early-to-mid 1980s, it remains significant.

Economic forecasting in theory and practice: An interview with David F. Hendry

David Hendry has made major contributions to many areas of economic forecasting. He has developed a taxonomy of forecast errors and a theory of unpredictability that have yielded valuable insights into the nature of forecasting. He has also provided new perspectives on many existing forecast techniques, including mean square forecast errors, add factors, leading indicators, pooling of forecasts, and multi-step estimation. In addition, David has developed new forecast tools, such as forecast encompassing; and he has improved existing ones, such as nowcasting and robustification to breaks. This interview for the International Journal of Forecasting explores David Hendry’s research on forecasting.

Economic Forecasting in Theory and Practice: An Interview with David F. Hendry

David Hendry has made major contributions to many areas of economic forecasting. He has developed a taxonomy of forecast errors and a theory of unpredictability that have yielded valuable insights into the nature of forecasting. He has also provided new perspectives on many existing forecast techniques, including mean square forecast errors, add factors, leading indicators, pooling of forecasts, and multi-step estimation. In addition, David has developed new forecast tools, such as forecast encompassing; and he has improved existing ones, such as nowcasting and robustification to breaks. This interview for the International Journal of Forecasting explores David Hendry’s research on forecasting.

Forecasting city arrivals with Google Analytics

The ability of 10 Google Analytics website traffic indicators from the Viennese DMO website to predict actual tourist arrivals to Vienna is investigated within the VAR model class. To prevent overparameterization, big data shrinkage methods are applied: Bayesian estimation of the VAR, reduction to a factor-augmented VAR, and application of Bayesian estimation to the FAVAR, the novel Bayesian FAVAR. Forecast accuracy results show that for shorter horizons (h=1, 2months ahead) a univariate benchmark performs best, while for longer horizons (h=3, 6, 12) forecast combination methods that include the predictive information of Google Analytics perform best, notably combined forecasts based on Bates–Granger weights, on forecast encompassing tests, and on a novel fusion of these two.