Dating and Synchronizing Tourism Growth Cycles
International tourism demand and the business cycle. Annals of Tourism Research, 39 1 , — However, they argue that the detected Kuznets cycle could be in fact regarded as the third harmonic of the Kondratieff cycle rather than as a separate cycle. Time series decomposition and measurement of business cycles, trends and growth cycles.
History of economic analysis. Does detrending matter for the determination of the reference cycle and the selec- tion of turning points. With regard to tourism research literature, only two attempts to detect the tourism demand cycles by using spectral analysis could be considered as a significant contribution to the field. Sharpie markers would be good for that. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden.
Hence, it is strongly recommended to apply a detrending method before the appliance of spectral analysis. Modern detrending methods decompose time series into a trend and a cycle component. The cycle component represents growth cycles. The Hodrick—Prescott filter is the detrending method I that is used here. It is a modern detrending technique, which is frequently used to separate cyclical component from the nonlinear trend. The method has been described for the first time in the paper by Figure 1.
Since then, it has become very popular tool in macroeco- nomics. The method implies solving of the following minimisation problem: The first part of the equation represents the cycle component of the time series.
In accordance with Canova , , empirical tests have confirmed that the Hodrick— Prescott filter is most effective method of the time-series decomposition. However, detecting of spurious cycles is sometimes pointed out as its major shortcoming. It has to be mentioned that this is the case if the value of parameter l is not appropriately determined. After the application of the Hodrick—Prescott filter, the original data of annual inter- national tourist arrivals are decomposed into two components: Since it can be assumed that there are inherent irregularities in the cycle series, it needs to be smoothed.
Therefore, it is highly advisable to remove the irregular component from the series before the appliance of spectral analysis. Accordingly, a smoothing technique is applied to the extracted cycle series. More precisely, three-years simple moving averages are calculated to obtain smoothed cycle series. After all preliminary treatments, i. Spectral analysis can be regarded as a special technique of time-series analysis.
Unlike other time-series techniques, which analyse data in the time domain, the spectral analysis is a tool for analysis in the frequency domain. The essence of the spectral analysis is the Fourier transformation of a time series from the time domain into the frequency domain.
From a mathematical point of view, the time series is transformed into the sum of sine com- ponents, which could be supposed as potential cycles. A special graph, i. Significance of the identified components of the spectrum is then tested by stat- istical tests. The first one is a nonparametric test, which is proposed by Kolmogorov and Smirnov a, b. The test is applied to test the null hypothesis that the spectrum of a time series is not different from the spectrum of white noise.
A detail explanation of the Kolmogorov—Smirnov test and its critical values can be found in Lapin and Whisler The second test is a test of significance of any individual frequency.
It is proposed by Fisher For a complete and detailed description of correct appliance of spectral analysis, see Bloomfield The software applications used in the analysis are as follows. The extraction of the cycle component was conducted with EViews 6, and the spectral analysis was conducted with Statsoft Statistica Other calculations were conducted with Microsoft Excel As shown in Figure 2, the labelled values mark components of the spectrum that might be significant cyclical patterns within international tourist arrivals.
The first maximum per- iodogram value represents a cycle with a period of 9. The second maximum periodogram value represents a cycle with a period of 16 years. And the third maximum periodogram value represents a cycle with a period of 6. The statistical significance of detected cycles is tested by the statistical tests described in the previous part of the paper, and the results are given in the Table 1.
This implies that all of the three detected cycles are statistically significant. An interpretation of the results and heuristic meaning of findings are presented in the conclusion of the paper.
Conclusion The estimation of statistical significance confirmed that all of the detected cycles are stat- istically significant. The cycles are as follows: Periodogram of detrended smoothed series of international tourist arrivals.
Results of estimation of statistical significance of detected cycles. Bolded numbers stand for empirically obtained statistics. The cycle with a period of 16 years could be considered as a harmonic of an under- lying Kondratieff cycle, which is not detected, probably because of the shortness of the time series of international tourist arrivals. Since the cycles of similar periods have been also detected in the dynamics of world GDP, it is reasonable to assume that the cycles of inter- national tourism demand are driven by the same cycles as global economy.
However, it is important to mention that obtained results cannot be instantly applied to any particular country or tourism destination. Every single tourist destination in the world has certainly experienced tourism demand growth cycles, but those episodes were merely connected with episodes of economic expansion and recession in the countries of origin of its tourists. The effect of substitution should also be taken into account; accordingly, tourists can choose to replace a luxury destination with a less expensive one in the time of recession.
In spite of that, the cycles of a single tourist destination could be induced by some other factors e. The key policy implication is that destination managers should pay attention to the growth trends for the global economy. The results in this study show that the cycles of inter- national tourism demand are expected to go hand in hand with the periods of expansion and recession in the global economy.
A single tourist destination may even benefit from the global recession if its managers recognise global economic trends right on time. It is also important to point out that the types of detected cycles are recognised and acknowledged by scholars. However, the idea of regular periodic cycles is far from a con- sensual acceptance.
Therefore, one has to be careful with an interpretation of the results of this paper and their consequences. As the author of this paper, I would like to emphasise that I do not firmly assert that business cycles are an objective phenomenon. It is more likely that the results in this paper suggest that the concept of the business cycles could be helpful in understanding the fluctuation of tourism and other economic activities.
Fourier analysis of time series — an introductiona 2nd ed. Detrending and turning points. European Economic Review, 38 3—4 , — Does detrending matter for the determination of the reference cycle and the selec- tion of turning points. The Economic Journal, , — The analysis of time series: An introduction 5th ed. Spectral analysis of international tourism flows. Annals of Tourism Research, 27 3 , — Tests of significance in harmonic analyses.
Proceedings of the Royal Society, A , 54— Dating and synchronizing tourism growth cycles. Tourism Economics, 11 4 , — Spectral analysis of short series — a simulation study. Journal of the Royal Statistical Society, A , 83— Tourism demand for Italy and the business cycle. Tourism Management, 31 3 , — Journal of Money, Credit and Banking, 29 1 , 1— On determination of empirical low of distribution. Journal of Italian Institute of Actuaries, 4, 83— An aluminum foil sheet works too, but I recommend cookie trays because they are easier and quicker to get out of the oven.
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Imsges: dating and synchronizing tourism growth cycles
After all preliminary treatments, i. He named different types of cycles after their discoverers or proposers:
To analyse the presence of a time lag between turning points of economic cycles and tourism demand, they suggest a lag concordance index. However, they argue that the detected Kuznets cycle could be in fact regarded as the third harmonic of the Kondratieff cycle rather than as a separate cycle.
It could be firmly assumed that tourism demand cycles are in tight connection with the cycles of overall demand, accordingly, if the cycles of the world GDP can be detected, then the cycles of the inter- national tourism demand should also exist. The essence of the spectral analysis is the Fourier transformation of a time series from the time domain into the frequency dating and synchronizing tourism growth cycles. However, they argue that the detected Kuznets cycle could be in fact regarded as the third harmonic of the Kondratieff cycle rather than as fiji dating separate dating and synchronizing tourism growth cycles. Detecting international tourism demand growth cycles. Spectral analysis of short series — a simulation study. Journal of Monetary Economics, 53 7— Forecasting the Demand for International Business Tourism.