An Investigation of the Time Series Behaviour of International Tourist Arrivals

1997 ◽  
Vol 3 (2) ◽  
pp. 185-199 ◽  
Author(s):  
Kevin KF. Wong
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Preliminary Investigation ◽  
Model Misspecification ◽  
Theoretical Implication ◽  
Ordinary Least Squares ◽  
Initial State ◽  
International Tourist ◽  
Stationary Type ◽  
Parameter Estimators

Most tourism econometric models are based on conventional least squares estimation, which assumes stationarity in their data generating mechanism. However, they fail to recognize the implications of the integrated properties of the historical time series of tourism data. Such time series properties may have important consequences with regard to the theoretical implication and interpretation of these tourism models. In this paper, historical data on international tourist arrivals from six major regions and seventeen individual countries are analysed to determine whether the series is better characterized by a stationary or non-stationary type process. Based on unit root tests, the results in most cases indicate that international tourist arrivals exhibit a non-stationary stochastic process that has the tendency to fluctuate away from a given initial state as time passes. These findings imply that studies which conveniently draw standard inferences from ordinary least squares estimated tourism models based on the levels of international tourist arrivals can be very misleading since non-stationarity in the data will produce inconsistent parameter estimators and unreliable test statistics. Furthermore, model misspecification that arises from unrelated integrated series can seriously bias conventional significance tests towards the acceptance of an apparently significant relationship. In this preliminary investigation, we conclude that econometric tourism models that focus on the levels of international tourist arrivals may not be reliable since the series is non-stationary and is integrated of order one, I(1).

Nuisance vs. Substance: Specifying and Estimating Time-Series-Cross-Section Models

1996 ◽  
Vol 6 ◽  
pp. 1-36 ◽  
Author(s):  
Nathaniel Beck ◽  
Jonathan N. Katz
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Cross Section ◽  
Generalized Least Squares ◽  
Ordinary Least Squares ◽  
Correlated Errors ◽  
Section Data ◽  
Variable Approach ◽  
Parameter Heterogeneity ◽  
Lagged Dependent Variable

In a previous article we showed that ordinary least squares with panel corrected standard errors is superior to the Parks generalized least squares approach to the estimation of time-series-cross-section models. In this article we compare our proposed method with another leading technique, Kmenta's “cross-sectionally heteroskedastic and timewise autocorrelated” model. This estimator uses generalized least squares to correct for both panel heteroskedasticity and temporally correlated errors. We argue that it is best to model dynamics via a lagged dependent variable rather than via serially correlated errors. The lagged dependent variable approach makes it easier for researchers to examine dynamics and allows for natural generalizations in a manner that the serially correlated errors approach does not. We also show that the generalized least squares correction for panel heteroskedasticity is, in general, no improvement over ordinary least squares and is, in the presence of parameter heterogeneity, inferior to it. In the conclusion we present a unified method for analyzing time-series-cross-section data.

scholarly journals Comparing Spectral Characteristics of Landsat-8 and Sentinel-2 Same-Day Data for Arctic-Boreal Regions

2019 ◽  
Vol 11 (14) ◽  
pp. 1730 ◽  
Author(s):  
Alexandra Runge ◽  
Guido Grosse
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Spectral Band ◽  
Ordinary Least Squares ◽  
Landsat 8 ◽  
Eastern Siberia ◽  
Landscape Changes ◽  
Least Squares Regression ◽  
Spectral Bandpass ◽  
Sentinel 2

The Arctic-Boreal regions experience strong changes of air temperature and precipitation regimes, which affect the thermal state of the permafrost. This results in widespread permafrost-thaw disturbances, some unfolding slowly and over long periods, others occurring rapidly and abruptly. Despite optical remote sensing offering a variety of techniques to assess and monitor landscape changes, a persistent cloud cover decreases the amount of usable images considerably. However, combining data from multiple platforms promises to increase the number of images drastically. We therefore assess the comparability of Landsat-8 and Sentinel-2 imagery and the possibility to use both Landsat and Sentinel-2 images together in time series analyses, achieving a temporally-dense data coverage in Arctic-Boreal regions. We determined overlapping same-day acquisitions of Landsat-8 and Sentinel-2 images for three representative study sites in Eastern Siberia. We then compared the Landsat-8 and Sentinel-2 pixel-pairs, downscaled to 60 m, of corresponding bands and derived the ordinary least squares regression for every band combination. The acquired coefficients were used for spectral bandpass adjustment between the two sensors. The spectral band comparisons showed an overall good fit between Landsat-8 and Sentinel-2 images already. The ordinary least squares regression analyses underline the generally good spectral fit with intercept values between 0.0031 and 0.056 and slope values between 0.531 and 0.877. A spectral comparison after spectral bandpass adjustment of Sentinel-2 values to Landsat-8 shows a nearly perfect alignment between the same-day images. The spectral band adjustment succeeds in adjusting Sentinel-2 spectral values to Landsat-8 very well in Eastern Siberian Arctic-Boreal landscapes. After spectral adjustment, Landsat and Sentinel-2 data can be used to create temporally-dense time series and be applied to assess permafrost landscape changes in Eastern Siberia. Remaining differences between the sensors can be attributed to several factors including heterogeneous terrain, poor cloud and cloud shadow masking, and mixed pixels.

scholarly journals EFFECTS OF FOREIGN LOANS, INTEREST RATE, AND EXPORT FOR THE FOREIGN EXCHANGE RESERVES IN INDONESIA 2002-2016

2019 ◽  
Vol 3 (2) ◽  
pp. 59
Author(s):  
Anas Iswanto Anwar ◽  
Bayu Pamungkas Djamal ◽  
Sri Undai Nurbayani
Keyword(s):  
Time Series ◽  
Interest Rate ◽  
Least Squares ◽  
Foreign Exchange ◽  
Secondary Data ◽  
Ordinary Least Squares ◽  
Foreign Exchange Reserves ◽  
Central Agency ◽  
Foreign Loans ◽  
The Interest Rate

The aim of this research is to analyze the effect of foreign loans, interest rate, and export for the foreign exchange reserves in Indonesia during 2002-2016. This research used secondary data which tends the time-series published by Bank Indonesia, The Ministry of Trade Republic of Indonesia, Central Agency on Statistics Indonesia in the year of 2002-2016. The result of the regression by using ordinary least squares (OLS) method showed that the foreign loans and export take effect positively to the foreign exchange reserves. It indicates that the increase of foreign loans and export could affect the foreign exchange reserves in Indonesia during 2002-2016. Otherwise, the interest rate could not affect the foreign exchange reserves in Indonesia during 2002-2016.

scholarly journals ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI VOLUME EKSPOR BIJI KAKAO INDONESIA KE MALAYSIA

2019 ◽  
Vol 13 (1) ◽  
pp. 37-58
Author(s):  
Ilma Yuni Rosita ◽  
Lilis Imamah Ichdayati ◽  
Rizki Adi Puspita Sari
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Least Squares ◽  
International Market ◽  
Ordinary Least Squares ◽  
Cocoa Beans ◽  
Significance Level ◽  
The Real ◽  
Analyze Time Series

This study aims to analyze the factors that affect the volume of Indonesian cocoa exports to Malaysia. Multiple linear regression and ordinary least squares (OLS) were employed to analyze time series of data from 2005 until 2013. Based on the analysis, it is obtained that factors that significantly effect the volume of Indonesian cocoa exports to Malaysia with a significance level (α) five percent are the real prices of Indonesian cocoa exports to Malaysia and the real prices of cocoa beans the international market.

scholarly journals Applications of Generalized Method of Moments Estimation

2001 ◽  
Vol 15 (4) ◽  
pp. 87-100 ◽  
Author(s):  
Jeffrey M Wooldridge
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Least Squares ◽  
Cross Section ◽  
Method Of Moments ◽  
Generalized Method Of Moments ◽  
Ordinary Least Squares ◽  
Estimation Problems ◽  
Method Of Moments Estimation ◽  
Moments Estimators

I describe how the method of moments approach to estimation, including the more recent generalized method of moments (GMM) theory, can be applied to problems using cross section, time series, and panel data. Method of moments estimators can be attractive because in many circumstances they are robust to failures of auxiliary distributional assumptions that are not needed to identify key parameters. I conclude that while sophisticated GMM estimators are indispensable for complicated estimation problems, it seems unlikely that GMM will provide convincing improvements over ordinary least squares and two-stage least squares--by far the most common method of moments estimators used in econometrics--in settings faced most often by empirical researchers.

Emipirical Evidence of Causality between Electricity and Economy

2011 ◽  
Vol 347-353 ◽  
pp. 32-35
Author(s):  
Okonga Wabuyabo Brigitte ◽  
Kaseeram Irrshad
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Granger Causality ◽  
Ordinary Least Squares ◽  
Economic Indicators ◽  
Series Method ◽  
Leading Economic Indicators ◽  
Time Series Method

This study seeks to empirically establish Granger-causality between electricity and manufacturing outputs in Kenya using ordinary least squares (OLS) time series method. Some of the leading economic indicators of Kenya that rely on electricity are used. The results are three-fold; some of the indicators enjoy bidirectional, others unidirectional while others register no causality at all with electricity output.

scholarly journals ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI VOLUME EKSPOR BIJI KAKAO INDONESIA KE MALAYSIA

2019 ◽  
Vol 11 (2) ◽  
pp. 161-182
Author(s):  
Ilma Yuni Rosita ◽  
Lilis Imamah Ichdayati ◽  
Rizki Adi Puspita Sari
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Least Squares ◽  
International Market ◽  
Ordinary Least Squares ◽  
Cocoa Beans ◽  
Significance Level ◽  
The Real ◽  
Analyze Time Series

This study aims to analyze the factors that affect the volume of Indonesian cocoa exports to Malaysia. Multiple linear regression and ordinary least squares (OLS) were employed to analyze time series of data from 2005 until 2013. Based on the analysis, it is obtained that factors that significantly effect the volume of Indonesian cocoa exports to Malaysia with a significance level (α) five percent are the real prices of Indonesian cocoa exports to Malaysia and the real prices of cocoa beans the international market.

scholarly journals INFRASTRUCTURES’ EFFECT ON ECONOMIC GROWTH IN SOUTHEAST SULAWESI

TRIKONOMIKA ◽  
2019 ◽  
Vol 18 (1) ◽  
pp. 8
Author(s):  
Manat Rahim ◽  
Armin Armin ◽  
La Ode Suriadi ◽  
Muh Armawaddin
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Data Analysis ◽  
Panel Data ◽  
Least Squares ◽  
Random Effect ◽  
Ordinary Least Squares ◽  
Legal Documents ◽  
Common Effect ◽  
Effect Method

The aim of the research is to analyze the effect of infrastructures on economic growth in Southeast Sulawesi. The data used the secunder data which formed time series-based. The data was obtained by publication and legal documents of Statistic Center Unit and relevant institution. The method of analysis used ordinary least squares with panel data. Thus, the data analysis of this research was regretion model with panel data. To estimate regretion of panel data, the researcher used common effect,fixed effect and random effect method. The findings of this research showed that insfrastructures comprised road, harbor, water and electicity simultaniously and significantly influenced the economic growth in Southeast Sulawesi. Partially, only harbor and electricity significantly affected on the economic growth Southeast Sulawesi.

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