The repercussions of COVID-19 on the stock market of the Tourism industry

2021 ◽  
Author(s):  
Theodoros Daglis ◽  
Maria-Anna Katsikogianni
Keyword(s):  
Time Series ◽  
Virus Infection ◽  
Impulse Response ◽  
Spillover Effects ◽  
Tourism Industry ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Negative Consequences ◽  
Travel Agents ◽  
Cruise Lines

The COVID-19 pandemic has already caused important negative consequences on the tourism industry globally. The lockdown measures suspended the tourism activities, and many tourists preferred to abstain from these activities in fear of the virus infection. As a result, investors have abandoned tourism-related companies’ stocks, impacting, even more, the tourism industry. In this paper, we examine the biggest companies’ stocks related to tourism, from the fields of airlines, cruise lines, resorts, hotel groups, travel agents, and other tourism activities (such as car rentals). Using time series analysis, we test and analyze the effect of the COVID-19 pandemic on these stocks, and we derive the spillover effects through the impulse-response functions from each company to the others. Based on our findings, the tourism-related stocks were affected by COVID-19, as shown by the causality technique, and, moreover, the tourism-related companies are interconnected with each other, transmitting the shock from a specific tourism industry to the others, as shown by the impulse-response functions.

Hilbert Transformation Impulse Response

2014 ◽  
Vol 19 (4) ◽  
pp. 27-35
Author(s):  
Mariusz Sulima
Keyword(s):  
Time Series ◽  
Impulse Response ◽  
Response Function ◽  
Signal To Noise Ratio ◽  
Impulse Response Function ◽  
Equation System ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Signal To Noise ◽  
Input Time

Abstract This work presents a new DHT impulse response function based on the proposed nonlinear equation system obtained as a result of combining the DHT and IDHT equation systems. In the case of input time series with selected characteristics, the DHT results obtained using this impulse response function are characterised by a higher accuracy compared to the DHT results obtained based on the convolution using other known DHT impulse response functions. The results are also characterised by a higher accuracy than the DHT results obtained using the popular indirect DHT method based on discrete Fourier transform (DFT). Analysis of these example time series with selected characteristics was performed based on the signal-to-noise ratio.

Study of Short-Run Grain Movements on the Inland Waterway System

2005 ◽  
Vol 1909 (1) ◽  
pp. 8-16 ◽  
Author(s):  
Mark A. Thoma ◽  
Wesley W. Wilson
Keyword(s):  
Time Series ◽  
Impulse Response ◽  
Important Variable ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Vector Autoregressive ◽  
Planning Models ◽  
Long Run ◽  
Short Run ◽  
Variance Decompositions

Time series techniques—particularly impulse–response functions and variance decompositions—are used to characterize the short-run relationships between 17 variables in a vector autoregressive model designed to trace the short-run interconnections among variables affecting lockages on the Mississippi and Illinois Rivers. The model contains five categories of variables: lockages, barge rates, grain bids, rail rates, and rail deliveries. Variance decompositions are constructed that identify barge rates as the most important variable affecting lockages at both short and long horizons. Barge rates are, in turn, explained largely by lockages and rail rates, indicating two-way feedback or bidirectional causality between lockages and barge rates. Impulse–response functions are also examined. The variance decompositions indicate that barge rates are important in explaining lockages, and the impulse–response functions show how lockages and other variables respond to such shocks. In general, there is a substitution away from barge transportation and toward rail transportation when barge rates increase. The results are useful for illuminating the causal relationships among variables in the model and for understanding behavioral relationships present in the data and can be used to guide short- and long-run planning models. For example, many planning models assume that barge traffic does not respond significantly to changes in barge rates; however, results obtained here imply that barge traffic and rail deliveries do respond to such changes. This potentially important implication illustrates the usefulness of the time series techniques used.

Estimation of groundwater recharge from time series modeling of groundwater levels in non-linear systems

2020 ◽  
Author(s):  
Raoul Collenteur ◽  
Steffen Birk ◽  
Gernot Klammler ◽  
Mark Bakker
Keyword(s):  
Time Series ◽  
Groundwater Recharge ◽  
Linear Systems ◽  
Impulse Response ◽  
Impulse Response Functions ◽  
Time Series Modeling ◽  
Response Functions ◽  
Groundwater Levels ◽  
Non Linear ◽  
Non Linear Systems

<p>Groundwater recharge remains a notoriously difficult flux to estimate, despite ongoing scientific efforts. In recent years, time series modeling using impulse response functions has gained popularity to simulate groundwater levels and is quickly becoming a common tool for hydrogeologists. Several approaches have been developed to estimate recharge from time series models for both linear and non-linear systems (e.g., [1], [2], and [3]). In this study, we introduce a novel approach to estimate groundwater recharge from observed groundwater levels in nonlinear systems (i.e., twice the precipitation does not necessarily lead to twice the recharge). We extend a time series model using impulse response functions with a non-linear unsaturated zone module that simulates recharge. The model parameters are estimated by fitting the simulated to the observed groundwater levels, with the groundwater recharge as an intermediate model result. </p><p>The method is tested on a time series of groundwater levels observed in Southeastern Austria (Wagna), where lysimeter data of seepage to the groundwater is available for model validation. The simulated groundwater recharge suggests an event-based recharge behavior, with most recharge occurring shortly after larger precipitation events. This finding agrees with the behavior observed in the lysimeter data. The estimated recharge fluxes show a high correlation with the observed seepage on time scales from years to months or weeks, while daily recharge rates show larger errors. Advantages of the method include limited data requirements (only precipitation, potential evapotranspiration, and groundwater time series are required) and the possibility to correct for other factors causing groundwater level fluctuations (e.g., pumping, river levels). This makes it possible to apply the method in locations where little system knowledge (e.g., soil profiles) is available.</p><p><strong>References:</strong><br>[1] Besbes, M. and De Marsily, G. (1984) From infiltration to recharge: use of a parametric transfer function, Journal of Hydrology.<br>[2] Peterson, T.J. and Fulton, S. (2019) Joint estimation of gross recharge, groundwater usage, and hydraulic properties within HydroSight, Groundwater.<br>[3] Obergfell, C., Bakker, M. and Maas, K. (2019) Estimation of average diffuse aquifer recharge using time series modeling of groundwater heads, Groundwater.</p>

Impulse-response functions and anthropogenic CO2

1995 ◽  
Vol 22 (4) ◽  
pp. 413-416 ◽  
Author(s):  
Francesco N. Tubiello ◽  
Michael Oppenheimer
Keyword(s):  
Impulse Response ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Anthropogenic Co2

IMPROVING EXTRACTION OF IMPULSE RESPONSE FUNCTIONS USING STATIONARY WAVELET SHRINKAGE IN TERAHERTZ REFLECTION IMAGING

2010 ◽  
Vol 09 (04) ◽  
pp. 387-394 ◽  
Author(s):  
YANG CHEN ◽  
YIWEN SUN ◽  
EMMA PICKWELL-MACPHERSON
Keyword(s):  
Experimental Data ◽  
Impulse Response ◽  
Response Function ◽  
Impulse Response Function ◽  
Terahertz Imaging ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Inverse Filtering ◽  
Wavelet Shrinkage ◽  
Gaussian Filtering

In terahertz imaging, deconvolution is often performed to extract the impulse response function of the sample of interest. The inverse filtering process amplifies the noise and in this paper we investigate how we can suppress the noise without over-smoothing and losing useful information. We propose a robust deconvolution process utilizing stationary wavelet shrinkage theory which shows significant improvement over other popular methods such as double Gaussian filtering. We demonstrate the success of our approach on experimental data of water and isopropanol.

A Comparison of Approaches to Select the Informativeness of Priors in BVARs

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jan Prüser ◽  
Christoph Hanck
Keyword(s):  
Impulse Response ◽  
Prior Information ◽  
Hierarchical Modeling ◽  
Small Samples ◽  
Model Parameters ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Vector Autoregressions ◽  
The Rich ◽  
Bayesian Vars

Abstract Vector autoregressions (VARs) are richly parameterized time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, in small samples the rich parametrization of VAR models may come at the cost of overfitting the data, possibly leading to imprecise inference for key quantities of interest such as impulse response functions (IRFs). Bayesian VARs (BVARs) can use prior information to shrink the model parameters, potentially avoiding such overfitting. We provide a simulation study to compare, in terms of the frequentist properties of the estimates of the IRFs, useful strategies to select the informativeness of the prior. The study reveals that prior information may help to obtain more precise estimates of impulse response functions than classical OLS-estimated VARs and more accurate coverage rates of error bands in small samples. Strategies based on selecting the prior hyperparameters of the BVAR building on empirical or hierarchical modeling perform particularly well.

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