scholarly journals A hybrid MIDAS approach for forecasting hotel demand using large panels of search data

2021 ◽  
pp. 135481662110155
Author(s):  
Binru Zhang ◽  
Nao Li ◽  
Rob Law ◽  
Heng Liu
Keyword(s):  
Demand Forecasting ◽  
Mixed Data ◽  
Dynamic Factor ◽  
Data Sampling ◽  
Data Set ◽  
Large Panels ◽  
Forecast Combinations ◽  
Search Data ◽  
Hospitality And Tourism ◽  
Mixed Frequency Data

The large amounts of hospitality and tourism-related search data sampled at different frequencies have long presented a challenge for hospitality and tourism demand forecasting. This study aims to evaluate the applicability of large panels of search series sampled at daily frequencies to improve the forecast precision of monthly hotel demand. In particular, a hybrid mixed-data sampling regression approach integrating a dynamic factor model and forecast combinations is the first reported method to incorporate mixed-frequency data while remaining parsimonious and flexible. A case study is undertaken by investigating Sanya, the southernmost city in Hainan province, as a tourist destination using 9 years of the experimental data set. Dynamic factor analysis is used to extract the information from large panels of web search series, and forecast combinations are attempted to obtain the final prediction results of the individual forecasts to enhance the prediction accuracy further. The empirical analysis results suggest that the developed hybrid forecast approach leads to improvements in monthly forecasts of hotel occupancy over its competitors.

scholarly journals Forecasting Tourism Demand with an Improved Mixed Data Sampling Model

2020 ◽  
Vol 60 (2) ◽  
pp. 336-353 ◽  
Author(s):  
Long Wen ◽  
Chang Liu ◽  
Haiyan Song ◽  
Han Liu
Keyword(s):  
Factor Model ◽  
Moving Average ◽  
Demand Forecasting ◽  
Mainland China ◽  
Mixed Data ◽  
Dynamic Factor ◽  
Data Sampling ◽  
Tourism Demand ◽  
Composite Indices ◽  
Mixed Data Sampling

Search query data reflect users’ intentions, preferences and interests. The interest in using such data to forecast tourism demand has increased in recent years. The mixed data sampling (MIDAS) method is often used in such forecasting, but is not effective when moving average (MA) dynamics are involved. To investigate the relevance of the MA components in MIDAS models to tourism demand forecasting, an improved MIDAS model that integrates MIDAS and the seasonal autoregressive integrated moving average process is proposed. Its performance is tested by forecasting monthly tourist arrivals in Hong Kong from mainland China with daily composite indices constructed from a large number of search queries using the generalized dynamic factor model. The forecasting results suggest that this new model significantly outperforms the benchmark model. In addition, comparing the forecasts and nowcasts shows that the latter generally outperforms the former.

scholarly journals Forecasting tourist arrivals: Google Trends meets mixed-frequency data

2019 ◽  
pp. 135481661987958 ◽  
Author(s):  
Tomas Havranek ◽  
Ayaz Zeynalov
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Google Trends ◽  
Mixed Data ◽  
Data Sampling ◽  
Actual Number ◽  
Mixed Data Sampling ◽  
Search Data ◽  
Mixed Frequency Data ◽  
Overnight Stays

In this article, we examine the usefulness of Google Trends data in predicting monthly tourist arrivals and overnight stays in Prague during the period between January 2010 and December 2016. We offer two contributions. First, we analyze whether Google Trends provides significant forecasting improvements over models without search data. Second, we assess whether a high-frequency variable (weekly Google Trends) is more useful for accurate forecasting than a low-frequency variable (monthly tourist arrivals) using mixed-data sampling (MIDAS). Our results suggest the potential of Google Trends to offer more accurate predictions in the context of tourism: we find that Google Trends information, both 2 months and 1 week ahead of arrivals, is useful for predicting the actual number of tourist arrivals. The MIDAS forecasting model employing weekly Google Trends data outperforms models using monthly Google Trends data and models without Google Trends data.

Mixed Frequency Models

2018 ◽  
Author(s):  
Eric Ghysels
Keyword(s):  
Mixed Data ◽  
Space Representation ◽  
Data Sampling ◽  
Economic Time Series ◽  
Mixed Frequency ◽  
State Space Representation ◽  
Econometric Methods ◽  
Mixed Data Sampling ◽  
Economic Time ◽  
Mixed Frequency Data

The majority of econometric models ignore the fact that many economic time series are sampled at different frequencies. A burgeoning literature pertains to econometric methods explicitly designed to handle data sampled at different frequencies. Broadly speaking these methods fall into two categories: (a) parameter driven, typically involving a state space representation, and (b) data driven, usually based on a mixed-data sampling (MIDAS)-type regression setting or related methods. The realm of applications of the class of mixed frequency models includes nowcasting—which is defined as the prediction of the present—as well as forecasting—typically the very near future—taking advantage of mixed frequency data structures. For multiple horizon forecasting, the topic of MIDAS regressions also relates to research regarding direct versus iterated forecasting.

scholarly journals Forecasting tourist arrivals at attractions: Search engine empowered methodologies

2018 ◽  
Vol 25 (3) ◽  
pp. 425-447 ◽  
Author(s):  
Katerina Volchek ◽  
Anyu Liu ◽  
Haiyan Song ◽  
Dimitrios Buhalis
Keyword(s):  
Moving Average ◽  
Demand Forecasting ◽  
Network Models ◽  
Autoregressive Moving Average ◽  
Data Sampling ◽  
Neural Network Models ◽  
Explanatory Variables ◽  
Frequency Search ◽  
Artificial Neural Network Models ◽  
Mixed Frequency Data

Tourist decision to visit attractions is a complex process influenced by multiple factors of individual context. This study investigates how the accuracy of tourism demand forecasting can be improved at the micro level. The number of visits to five London museums is forecast and the predictive powers of Naïve I, seasonal Naïve, seasonal autoregressive moving average, seasonal autoregressive moving average with explanatory variables, SARMAX-mixed frequency data sampling and artificial neural network models are compared. The empirical findings extend understanding of different types of data and forecasting algorithms to the level of specific attractions. Introducing the Google Trends index on pure time-series models enhances the forecasts of the volume of arrivals to attractions. However, none of the applied models outperforms the others in all situations. Different models’ forecasting accuracy varies for short- and long-term demand predictions. The application of higher frequency search query data allows for the generation of weekly predictions, which are essential for attraction- and destination-level planning.

What Affects the Relationship Between Oil Prices and the U.S. Stock Market? A Mixed-Data Sampling Copula Approach*

2020 ◽  
Author(s):  
Yuting Gong ◽  
Ruijun Bu ◽  
Qiang Chen
Keyword(s):  
Aggregate Demand ◽  
Low Frequency ◽  
Oil Prices ◽  
Mixed Data ◽  
Copula Model ◽  
Data Sampling ◽  
Explanatory Variables ◽  
The Impact ◽  
Mixed Frequency Data ◽  
The Relationship

Abstract The relationship between oil prices and stocks is an important issue for portfolio selection and risk management. This article proposes a mixed frequency data sampling copula model with explanatory variables that incorporates low-frequency explanatory variables into a high-frequency dynamic copula model. It enables us to investigate the impacts of economic factors on the relationship between oil and stocks. It is found that the dependence of oil and stock markets is influenced by aggregate demand and stock-specific negative news. The impact of aggregate demand lasts for two years, while the impact of stock-specific news lasts for one quarter.

scholarly journals High‐dimensional macroeconomic forecasting and variable selection via penalized regression

2019 ◽  
Vol 22 (1) ◽  
pp. 34-56 ◽  
Author(s):  
Yoshimasa Uematsu ◽  
Shinya Tanaka
Keyword(s):  
New York ◽  
Variable Selection ◽  
Stock Price ◽  
Stock Exchange ◽  
Penalized Regression ◽  
Mixed Data ◽  
High Dimensional ◽  
Data Sampling ◽  
Data Set ◽  
Macroeconomic Forecasting

Summary This study examines high-dimensional forecasting and variable selection via folded-concave penalized regressions. The penalized regression approach leads to sparse estimates of the regression coefficients and allows the dimensionality of the model to be much larger than the sample size. First, we discuss the theoretical aspects of a penalized regression in a time series setting. Specifically, we show the oracle inequality with ultra-high-dimensional time-dependent regressors. Then we show the validity of the penalized regression using two empirical applications. First, we forecast quarterly US gross domestic product data using a high-dimensional monthly data set and the mixed data sampling (MIDAS) framework with penalization. Second, we examine how well the penalized regression screens a hidden portfolio based on a large New York Stock Exchange stock price data set. Both applications show that a penalized regression provides remarkable results in terms of forecasting performance and variable selection.

scholarly journals Evaluation of Mixed Frequency Approaches for Tracking Near-Term Economic Developments in North Macedonia

2021 ◽  
Vol 16 (2) ◽  
pp. 43-52
Author(s):  
Gani Ramadani ◽  
Magdalena Petrovska ◽  
Vesna Bucevska
Keyword(s):  
Aggregate Demand ◽  
Demand Forecasting ◽  
Var Model ◽  
Frequency Data ◽  
Data Set ◽  
Policy Makers ◽  
Mixed Frequency ◽  
Near Term ◽  
Economic Developments ◽  
Mixed Frequency Data

Abstract Aggregate demand forecasting, also known as nowcasting when it applies to current quarter assessment, is of notable interest to policy makers. This paper concentrates on the empirical methods dealing with mixed-frequency data. In particular, it focuses on the MIDAS approach and its later extension, the Bayesian MFVAR. The two strategies are evaluated in terms of their accuracy to nowcast Macedonian GDP growth, using same monthly frequency data set. The results of this study indicate that the MIDAS regressions demonstrate comparable forecasting performance to that of MF-VAR model. Moreover, it is interesting to note that the two approaches are reciprocal, since in general, their combined forecast demonstrates clear superiority in predicting business cycle turning points. Additionally, the MF-VAR model showed higher precision in times of increased uncertainty.

scholarly journals On business cycle forecasting

2020 ◽  
Vol 14 (1) ◽  
Author(s):  
Huiwen Lai ◽  
Eric C. Y. Ng
Keyword(s):  
Business Cycles ◽  
Factor Model ◽  
Mixed Data ◽  
Dynamic Factor ◽  
Dynamic Factor Model ◽  
Forecast Errors ◽  
Data Sampling ◽  
Target Variable ◽  
Financial Variables ◽  
Mixed Data Sampling

Abstract We develop a recession forecasting framework using a less restrictive target variable and more flexible and inclusive specification than those used in the literature. The target variable captures the occurrence of a recession within a given future period rather than at a specific future point in time (widely used in the literature). The modeling specification combines an autoregressive Logit model capturing the autocorrelation of business cycles, a dynamic factor model encompassing many economic and financial variables, and a mixed data sampling regression incorporating common factors with mixed sampling frequencies. The model generates significantly more accurate forecasts for U.S. recessions with smaller forecast errors and stronger early signals for the turning points of business cycles than those generated by existing models.

Measuring the Information Content of the Beige Book: A Mixed Data Sampling Approach

2008 ◽  
Author(s):  
Michelle T. Armesto ◽  
Ruben Hernandez-Murillo ◽  
Michael Owyang ◽  
Jeremy M. Piger
Keyword(s):  
Information Content ◽  
Mixed Data ◽  
Data Sampling ◽  
Mixed Data Sampling ◽  
Sampling Approach
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