Seasonality and cycles in tourism demand—redux

Vatsa, Puneet

doi:10.1016/j.annals.2020.103105

Cited by 10 publications

(11 citation statements)

References 10 publications

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“…Thus, it can be used in larger multivariate models comprising macroeconomic and financial time series; its utility extends beyond univariate forecasting of tourism demand. However, unlike the present paper, neither Bosupeng (2019) nor Vatsa (2020b) analyzes the linkages between business cycles and tourism demand cycles.…”

Section: Literature Reviewmentioning

confidence: 86%

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A new perspective on the linkages between tourism demand and business cycles

Vatsa

Balli

Saleh

2021

Journal of Tourism Research

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Section: Literature Reviewmentioning

confidence: 86%

“…Only two papers, Bosupeng (2019) and Vatsa (2020b), have compared the two methods in the context of tourism demand. Although the former focuses on tourism demand forecasting, the latter points out that the Hamilton filter has much broader applications and implications.…”

Section: Literature Reviewmentioning

confidence: 99%

A new perspective on the linkages between tourism demand and business cycles

Vatsa

Balli

Saleh

2021

Journal of Tourism Research

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“…Ignoring these differences across time series may lead to incorrect conclusions. Often, filtering techniques such as the Hodrick–Prescott filter fail to remove seasonality from the cyclical components (Buss, 2010; Vatsa, 2020). Although one may seasonally adjust the data using off‐the‐shelf techniques such as the X‐13 and seasonal and trend decomposition using Loess (STL) methods, these may yield inconsistent results for different series—it is not suitable to apply the same seasonal adjustment method to different series or to apply ad hoc methods to different series.…”

Section: Method: Hamilton Filter and Time‐difference Analysismentioning

confidence: 99%

Energy and crop price cycles before and after the global financial crisis: A new approach

Vatsa

Miljković

2021

J Agricultural Economics

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Using the newly developed Hamilton filter, we decompose prices of three primary energy sources (crude oil, coal and natural gas) and five significant crops (corn, palm oil, rice, soybean and wheat) to isolate their cyclical components. Then, we apply time-difference analyses to study their co-movement. Analysing 15 energy price-crop price pairs over two periods, 1990-2009 and 2010-2020, we find that the correlations between coal and crop prices have weakened significantly during the 2010s, while correlations of crude oil and natural gas prices with crop prices have strengthened. Until 2009, crude oil and natural gas prices led crop prices, whereas during the 2010s they lagged crop prices. We conclude that the global financial crisis marked a pivotal shift in the associations between energy and crop prices. The results underscore the importance of examining the associations between specific crop and energy prices rather than aggregated indices; furthermore, using crude oil as a proxy for energy prices is not suitable-an aggregated approach that presumes the predominance of one energy source apropos different crops may yield incorrect results.

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“…Vatsa, 2020b) have shown that the HP filter is not well-suited to decomposing tourism demand time series, which have prominent seasonal factors, as these are not removed from the cycles. Vatsa (2020b) demonstrates the Hamilton filter, unlike the HP filter, purges seasonality from tourism demand cycles, while Bosupeng (2019), which concerns tourism demand forecasting, shows that the Hamilton filter yields superior forecasts of seasonally unadjusted tourism demand relative to the HP filter. Although these studies highlight important advantages of using the Hamilton filter, neither employs it to examine the nexus between business cycles and tourism demand.…”

Section: Methodsmentioning

confidence: 99%

Business cycles and tourism imports in the South Pacific

2021

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The demand for international tourism in Australia and New Zealand is vital to the South Pacific’s tourism-reliant islands. However, at the time of this study these two countries find themselves in precarious economic situations. The question addressed by this study is, will tourism imports in these two countries pick up on the back of economic recovery? We answer this question using time-difference analysis and the newly developed Hamilton filter. The short answer is yes, but more so in New Zealand than Australia. The key findings of this study are that tourism demand in both Australia and New Zealand is pro-cyclical, tourism demand cycles in New Zealand strongly lag business cycles by 1 year, whereas in Australia, they weakly lag business cycles by one quarter, and, overall, tourism demand and business cycles in New Zealand share a stronger association than they do in Australia.

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Seasonality and cycles in tourism demand—redux

Cited by 10 publications

References 10 publications

A new perspective on the linkages between tourism demand and business cycles

A new perspective on the linkages between tourism demand and business cycles

Energy and crop price cycles before and after the global financial crisis: A new approach

Business cycles and tourism imports in the South Pacific

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