Methodology and Results Sample Clauses

Methodology and Results. 1. Local comovement of stock returns Comovement of stock returns measures the extent to which the return of an individual stock is correlated with that of the stocks belonging to the same geographical location. Following Xxxxxxx Xxxxxxxx and Xxxxxxx Xxxx, my study measures the comovement effect by the following regression model: 𝑅𝑖,𝑡 = 𝛼𝑖 + β𝐿𝑂𝐶 ∗ 𝑅𝐿𝑂𝐶,𝑡 + β𝑀𝐾𝑇 ∗ 𝑅𝑀𝐾𝑇,𝑡 + 𝜀𝑖,𝑡 (1) In the equation (1), 𝑅𝑖,𝑡 is the monthly return for the individual stock 𝑖 at time 𝑡. 𝑅𝐿𝑂𝐶,𝑡 is the location index, which represents the monthly return of the local portfolio at time 𝑡. The local portfolio consists of all of the stocks in the same region as stock ��. Then 𝑅𝐿��𝐶,𝑡 measures the monthly excess return of the portfolio. The excess return of the portfolio is equal-weighted, which is simply the average of the returns of all stocks in the portfolio. The value-weighted approach is not adopted here in order to avoid the unintended interaction with market capitalization factor. However, for some regions with relative few stocks, this regression is problematic because each stock could have a misleadingly high correlation with the location index, which incurs a biased estimation of the comovement effect. As a result, a slightly adjusted methodology for deriving the location index is warranted. I instead construct the location index by first taking the individual stock 𝑖 out of the local portfolio and then applying the same method mentioned before. Therefore, for each individual stock in the same geographical region, the corresponding location index is also slightly different. In this way, the issue of spurious relationship could be largely avoided, as Xxxxxxx Xxxxxxxx and Xxxxxxx Xxxx claimed. In this model (1), the strength of the comovement effect is simply estimated by the coefficient, β𝐿𝑂𝐶.

Methodology and Results. 6.3.1. Temperature effect on tourism: monthly-data analysis We use monthly data for summer, covering June to September 2000-2016, for 29 European countries. Since we use in our analysis monthly data average temperature is 16.9 °C, maximum monthly (average) temperature is 30.8 °C. Average annual GDP is about 25,000 Euro, with a range at 6,200 to 77,300 PPS Euro. Countries vary in climate, geography, and area – on average there are 125 people per km2, see Table 6.1. Table 6.1 Sample descriptive statistics, monthly data, June-Sept, 2000-2016 Variable N Mean Std Min Max Dependent variables nights [1000/month] 1820 11 859 17 783 101 87 171 arrivals [1000/month] 1820 3 327 4 725 43 23 389 Socio-econ controls GDP PPS pc 1536 24 804 11 430 6 200 77 300 GDP EUR2010 pc 1700 25 899 16 183 3 250 84 547 Population density 1812 125.71 105.86 2.80 503.10 Weather controls tmean 1700 16.86 4.66 -2.00 30.23 tmax 1700 30.78 6.76 4.37 46.07 precipitation 1700 74.97 40.31 0.00 247.79 HI_max 1700 24.09 5.77 1.91 38.37 THI_max 1700 21.58 4.04 2.78 28.53 THI_min 1700 10.03 4.47 -7.16 23.88 WSDI 1816 9.50 12.34 0.00 115.00 Regions NORTH 1820 0.21 0.41 0 1 WEST 1820 0.23 0.42 0 1 EAST 1820 0.22 0.41 0 1 SOUTH 1820 0.19 0.39 0 1 BALKAN 1820 0.15 0.35 0 1 The dataset records the number of arrivals and the number of nights spent in the country of destination, without knowing from which country tourists are coming (i.e. country of origin). In order to examine different patterns in visiting countries we group all countries in five groups that differ in weather (warm, cold) and income. These five groups include NORTH, WEST, EAST, SOUTH, and BALKAN.56 As shown in table 2, countries grouped in the five groups differ with respect to economic performance (GDP per capita), population density, outdoor temperature, and weather in general (as measured by heat-related indexes). Moreover, arrival flows and nights spent also vary considerably, see Table 6.2. Table 6.2 Descriptive country statistics by regions, summer months 2000-2016, averages Variable ALL NORTH WEST EAST SOUTH BALKAN nights [1000/month] 11 936 8 452 18 497 2 689 25 628 3 956 arrivals [1000/month] 3 421 2 948 6 091 902 5 702 859 GDP PPS pc 24 482 32 611 38 731 17 114 22 914 13 110 GDP 2010 pc 26 482 38 998 41 640 10 553 21 558 7 758 Population, million 12.84 13.46 27.59 9.96 25.26 8.04 Population density, p/km2 219.60 71.88 238.11 84.38 113.32 84.14 Weather controls tmean 16.65 11.99 15.71 16.79 21.83 19.08 tmax 30.52 23.36 31.03 3...

Methodology and Results