Journal articles: 'Seasonal Unit Root Test'

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Caner, Mehmet. "A Locally Optimal Seasonal Unit-Root Test." Journal of Business & Economic Statistics 16, no. 3 (July 1998): 349. http://dx.doi.org/10.2307/1392511.

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Caner, Mehmet. "A Locally Optimal Seasonal Unit-Root Test." Journal of Business & Economic Statistics 16, no. 3 (July 1998): 349–56. http://dx.doi.org/10.1080/07350015.1998.10524774.

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Depalo, Domenico. "A Seasonal Unit-Root Test with Stata." Stata Journal: Promoting communications on statistics and Stata 9, no. 3 (September 2009): 422–38. http://dx.doi.org/10.1177/1536867x0900900305.

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Popp, Stephan. "Modified seasonal unit root test with seasonal level shifts at unknown time." Economics Letters 97, no. 2 (November 2007): 111–17. http://dx.doi.org/10.1016/j.econlet.2007.02.026.

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Kurozumi, Eiji. "THE LIMITING PROPERTIES OF THE CANOVA AND HANSEN TEST UNDER LOCAL ALTERNATIVES." Econometric Theory 18, no. 5 (July 17, 2002): 1197–220. http://dx.doi.org/10.1017/s0266466602185082.

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This paper investigates the limiting properties of the Canova and Hansen test, testing for the null hypothesis of no unit root against seasonal unit roots, under a sequence of local alternatives with the model extended to have seasonal dummies and trends or no deterministic term and also only seasonal dummies. We derive the limiting distribution of the test statistic and its characteristic function under local alternatives. We find that the local limiting power is an inverse function of the spectral density at frequency π (π/2) when we test against a negative unit root (annual unit roots). We also theoretically show that the local limiting power of the Canova and Hansen test against a negative unit root (annual unit roots) does not increase when the true process has annual unit roots (a negative unit root) but not a negative unit root (annual unit roots), which has been observed in Monte Carlo simulations in such research as Caner (1998, Journal of Business and Economic Statistics 16, 349–356), Canova and Hansen (1995, Journal of Business and Economic Statistics 13, 237–252), and Hylleberg (1995, Journal of Econometrics 69, 5–25).

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Castro, Tomas del Barrio, and Denise R. Osborn. "TESTING FOR SEASONAL UNIT ROOTS IN PERIODIC INTEGRATED AUTOREGRESSIVE PROCESSES." Econometric Theory 24, no. 4 (April 4, 2008): 1093–129. http://dx.doi.org/10.1017/s0266466608080420.

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This paper examines the implications of applying the Hylleberg, Engle, Granger, and Yoo (1990, Journal of Econometrics 44, 215–238) (HEGY) seasonal root tests to a process that is periodically integrated. As an important special case, the random walk process is also considered, where the zero-frequency unit root t-statistic is shown to converge to the Dickey–Fuller distribution and all seasonal unit root statistics diverge. For periodically integrated processes and a sufficiently high order of augmentation, the HEGY t-statistics for unit roots at the zero and semiannual frequencies both converge to the same Dickey–Fuller distribution. Further, the HEGY joint test statistic for a unit root at the annual frequency and all joint test statistics across frequencies converge to the square of this distribution. Results are also derived for a fixed order of augmentation. Finite-sample Monte Carlo results indicate that, in practice, the zero-frequency HEGY statistic (with augmentation) captures the single unit root of the periodic integrated process, but there may be a high probability of incorrectly concluding that the process is seasonally integrated.

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Cáceres-Hernández, José J., and Gloria Martín-Rodríguez. "Stationarity of seasonal patterns in weekly agricultural prices." Spanish Journal of Agricultural Research 16, no. 3 (October 23, 2018): e0109. http://dx.doi.org/10.5424/sjar/2018163-12937.

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Weekly series of agricultural prices usually exhibit seasonal variations and the stationarity of these variations should be taken into account to analyse price relationships. However, unit root tests at seasonal frequencies are unlikely to have good power properties. Furthermore, movements in actual price series are often not as expected when unit roots are present. Therefore, stationarity tests at seasonal frequencies also need to be applied. In this paper, a procedure to test for the null hypothesis of stationarity at seasonal frequencies was extended to the weekly case. Once critical values were obtained by simulation exercises, unit root and stationarity tests were applied to weekly retail prices of different agricultural commodities in Spain. The most relevant finding was that many unit roots that seasonal unit root tests failed to reject did not seem to be present from the results of seasonal stationarity tests, whereas seasonal unit root tests led to the rejection of some unit roots that seemed to be present according to the results of seasonal stationarity tests. In conclusion, unit root tests should be complemented with stationarity tests before making decisions about the behaviour of seasonal patterns.

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Narayan, Paresh Kumar, and Stephan Popp. "An application of a new seasonal unit root test to inflation." International Review of Economics & Finance 20, no. 4 (October 2011): 707–16. http://dx.doi.org/10.1016/j.iref.2011.01.001.

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Halim, Siana, and Indriati N. Bisono. "Automatic seasonal auto regressive moving average models and unit root test detection." International Journal of Management Science and Engineering Management 3, no. 4 (January 2008): 266–74. http://dx.doi.org/10.1080/17509653.2008.10671053.

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RODRIGUES, PAULO M. M. "NEAR SEASONAL INTEGRATION." Econometric Theory 17, no. 1 (February 2001): 70–86. http://dx.doi.org/10.1017/s0266466601171033.

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This paper presents asymptotic results for the seasonal unit root test proposed by Hylleberg, Engle, Granger and Yoo (1990, Journal of Econometrics 44, 215–238) in a near integration context. The findings are important in that they provide the asymptotic power functions of the Hylleberg et al. statistics when the characteristic roots of a seasonal process are local to unity. These conclusions extend the available asymptotic results for this test and serve as a framework for the potential development of more powerful test procedures.

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Nabeya, Seiji. "ASYMPTOTIC DISTRIBUTIONS FOR UNIT ROOT TEST STATISTICS IN NEARLY INTEGRATED SEASONAL AUTOREGRESSIVE MODELS." Econometric Theory 16, no. 2 (April 2000): 200–230. http://dx.doi.org/10.1017/s0266466600162036.

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Seasonal autoregressive models with an intercept or linear trend are discussed. The main focus of this paper is on the models in which the intercept or trend parameters do not depend on the season. One of the most important results from this study is the asymptotic distribution for the ordinary least squares estimator of the autoregressive parameter obtained under nearly integrated condition, and another is the approximation to the limiting distribution of the t-statistic under the null for testing the unit root hypothesis.

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Dreger*, Christian, and Hans-Eggert Reimers. "Panel Seasonal Unit Root Test: Further Simulation Results and An Application to Unemployment Data." Allgemeines Statistisches Archiv 89, no. 3 (August 2005): 321–37. http://dx.doi.org/10.1007/s10182-005-0207-8.

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13

Alleyne, Dillon. "Can Seasonal Unit Root Testing Improve the Forecasting Accuracy of Tourist Arrivals?" Tourism Economics 12, no. 1 (March 2006): 45–64. http://dx.doi.org/10.5367/000000006776387132.

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Tourism has emerged as one of the main industries in many Caribbean countries. Jamaica is no exception: tourist receipts are its largest source of foreign exchange earnings. Because of the importance of the tourism sector to the Jamaican economy, accurate forecasts of tourist arrivals are critical. This paper argues that forecasting accuracy can be improved by the use of seasonal unit root testing. Using six tourist arrival series, from 1968:1 to 2001:3, it was found that unit root testing, applying the HEGY procedure, did help to improve the forecasts at all horizons. The exceptions were those series that exhibited considerable volatility; in these cases the seasonal unit root test did not prove superior at the short horizon. These results, however, may not be robust with respect to data at other frequencies.

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Lee, Taiyeong, and David A. Dickey. "Limiting distributions of unconditional maximum likelihood unit root test statistics in seasonal time-series models." Journal of Time Series Analysis 25, no. 4 (July 2004): 551–61. http://dx.doi.org/10.1111/j.1467-9892.2004.01814.x.

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Castro, Tomás del Barrio, Denise R. Osborn, and A. M. Robert Taylor. "ON AUGMENTED HEGY TESTS FOR SEASONAL UNIT ROOTS." Econometric Theory 28, no. 5 (April 27, 2012): 1121–43. http://dx.doi.org/10.1017/s0266466612000060.

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In this paper we extend the large-sample results provided for the augmented Dickey–Fuller test by Said and Dickey (1984, Biometrika 71, 599–607) and Chang and Park (2002, Econometric Reviews 21, 431–447) to the case of the augmented seasonal unit root tests of Hylleberg, Engle, Granger, and Yoo (1990, Journal of Econometrics 44, 215–238), inter alia. Our analysis is performed under the same conditions on the innovations as in Chang and Park (2002), thereby allowing for general linear processes driven by (possibly conditionally heteroskedastic) martingale difference innovations. We show that the limiting null distributions of the t-statistics for unit roots at the zero and Nyquist frequencies and joint F-type statistics are pivotal, whereas those of the t-statistics at the harmonic seasonal frequencies depend on nuisance parameters that derive from the lag parameters characterizing the linear process. Moreover, the rates on the lag truncation required for these results to hold are shown to coincide with the corresponding rates given in Chang and Park (2002); in particular, an o(T1/2) rate is shown to be sufficient.

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Caporale, Guglielmo M., and Luis A. Gil-Alana. "Testing for Seasonal Fractional Roots in German Real Output." German Economic Review 5, no. 3 (August 1, 2004): 319–33. http://dx.doi.org/10.1111/j.1465-6485.2004.00111.x.

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Abstract This paper examines the seasonal structure of German real GNP per capita by using a version of Robinson’s (1994) tests which is suitable in the context of seasonality. This method has several advantages over alternative approaches when testing for seasonal unit roots. First, unlike standard tests, which are nested in AR alternatives, it is embedded in fractional alternatives. Second, it allows testing at the zero frequency and at each of the seasonal frequencies separately. Third, it makes it possible to test for different orders of integration at each of the frequencies simultaneously. The empirical analysis suggests that the real output series may have a unit root at the zero frequency, and fractional rather than unit roots at the seasonal ones. This is in contrast to the findings reported by Lutkepohl et al. (1999) in their study on German money demand, and shows the importance of modelling the seasonal features of the data in alternative ways.

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Bolat, Süleyman, Aviral Kumar Tiwari, and Phouphet Kyophilavong. "Testing the inflation rates in MENA countries: Evidence from quantile regression approach and seasonal unit root test." Research in International Business and Finance 42 (December 2017): 1089–95. http://dx.doi.org/10.1016/j.ribaf.2017.07.043.

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陈, 志宗. "Seasonal Unit Root Test and Multiple Structural Breaks Estimate—An Empirical Study of China’s Inbound Tourists (1990.1-2014.12)." Management Science and Engineering 07, no. 04 (2018): 250–58. http://dx.doi.org/10.12677/mse.2018.74029.

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Lagnoux, Agnès, Thi Mong Ngoc Nguyen, and Frédéric Proïa. "On the Bickel–Rosenblatt test of goodness-of-fit for the residuals of autoregressive processes." ESAIM: Probability and Statistics 23 (2019): 464–91. http://dx.doi.org/10.1051/ps/2018016.

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We investigate in this paper a Bickel–Rosenblatt test of goodness-of-fit for the density of the noise in an autoregressive model. Since the seminal work of Bickel and Rosenblatt, it is well-known that the integrated squared error of the Parzen–Rosenblatt density estimator, once correctly renormalized, is asymptotically Gaussian for independent and identically distributed (i.i.d.) sequences. We show that the result still holds when the statistic is built from the residuals of general stable and explosive autoregressive processes. In the univariate unstable case, we prove that the result holds when the unit root is located at − 1 whereas we give further results when the unit root is located at 1. In particular, we establish that except for some particular asymmetric kernels leading to a non-Gaussian limiting distribution and a slower convergence, the statistic has the same order of magnitude. We also study some common unstable cases, like the integrated seasonal process. Finally, we build a goodness-of-fit Bickel–Rosenblatt test for the true density of the noise together with its empirical properties on the basis of a simulation study.

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Jatuporn, Chalermpon, Patana Sukprasert, Siros Tongchure, Vasu Suvanvihok, and Supat Thongkaew. "Forecasting Import Demand of Table Grapes: Empirical Evidence from Thailand." Asian Journal of Agriculture and Rural Development 10, no. 2 (July 20, 2020): 578–86. http://dx.doi.org/10.18488/journal.ajard.2020.102.578.586.

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The purpose of this study is to forecast the import demand of table grapes of Thailand using monthly time series from January 2007 to April 2020. The ADF unit root test is used for stationarity checking, and seasonal autoregressive integrated moving average (SARIMA) is applied to forecast the import demand of table grapes. The results revealed that the integration of time series was in the first difference for non-seasonal and seasonal order. The best-fitted forecasting model was SARIMA(1,1,3)(2,1,0)12. The forecasted period for the next eight months showed the import demand of table grapes of Thailand that is slightly decreased by an average of 11.398 percent, with overall expected to decrease by an average of 15.218 percent in 2020.

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Rutkowska, Agnieszka, and Marek Ptak. "On Certain Stationarity Tests for Hydrologic Series." Studia Geotechnica et Mechanica 34, no. 1 (March 1, 2012): 51–63. http://dx.doi.org/10.1515/sgem-2017-0022.

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Abstract The main aim of the article was application of some statistical tests for investigation of weak stationarity of hydrologic time series. The tests were applied to mean monthly flow and maximum annual flow on three rivers: two Polish and one American river. Firstly, the modified Mann–Kendall test for autocorrelated data was used to detect trend. After detrending we used “unit root tests” based on the DF test and “stationarity tests” based on the KPSS test. The tests were investigated and compared in some aspects: analysis of residuals, application to seasonal series, AIC and Schwarz values.

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El Montasser, Ghassen, and Rangan Gupta. "An application of a new seasonal unit root test for trending and breaking series to industrial production of the brics." Journal of Developing Areas 50, no. 4 (2016): 183–94. http://dx.doi.org/10.1353/jda.2016.0160.

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Bauer, Dietmar, and Rainer Buschmeier. "Asymptotic Properties of Estimators for Seasonally Cointegrated State Space Models Obtained Using the CVA Subspace Method." Entropy 23, no. 4 (April 8, 2021): 436. http://dx.doi.org/10.3390/e23040436.

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This paper investigates the asymptotic properties of estimators obtained from the so called CVA (canonical variate analysis) subspace algorithm proposed by Larimore (1983) in the case when the data is generated using a minimal state space system containing unit roots at the seasonal frequencies such that the yearly difference is a stationary vector autoregressive moving average (VARMA) process. The empirically most important special cases of such data generating processes are the I(1) case as well as the case of seasonally integrated quarterly or monthly data. However, increasingly also datasets with a higher sampling rate such as hourly, daily or weekly observations are available, for example for electricity consumption. In these cases the vector error correction representation (VECM) of the vector autoregressive (VAR) model is not very helpful as it demands the parameterization of one matrix per seasonal unit root. Even for weekly series this amounts to 52 matrices using yearly periodicity, for hourly data this is prohibitive. For such processes estimation using quasi-maximum likelihood maximization is extremely hard since the Gaussian likelihood typically has many local maxima while the parameter space often is high-dimensional. Additionally estimating a large number of models to test hypotheses on the cointegrating rank at the various unit roots becomes practically impossible for weekly data, for example. This paper shows that in this setting CVA provides consistent estimators of the transfer function generating the data, making it a valuable initial estimator for subsequent quasi-likelihood maximization. Furthermore, the paper proposes new tests for the cointegrating rank at the seasonal frequencies, which are easy to compute and numerically robust, making the method suitable for automatic modeling. A simulation study demonstrates by example that for processes of moderate to large dimension the new tests may outperform traditional tests based on long VAR approximations in sample sizes typically found in quarterly macroeconomic data. Further simulations show that the unit root tests are robust with respect to different distributions for the innovations as well as with respect to GARCH-type conditional heteroskedasticity. Moreover, an application to Kaggle data on hourly electricity consumption by different American providers demonstrates the usefulness of the method for applications. Therefore the CVA algorithm provides a very useful initial guess for subsequent quasi maximum likelihood estimation and also delivers relevant information on the cointegrating ranks at the different unit root frequencies. It is thus a useful tool for example in (but not limited to) automatic modeling applications where a large number of time series involving a substantial number of variables need to be modelled in parallel.

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Hina, Hafsa, and Abdul Qayyum. "Re-estimation of Keynesian Model by Considering Critical Events and Multiple Cointegrating Vectors." Pakistan Development Review 54, no. 2 (June 1, 2015): 123–45. http://dx.doi.org/10.30541/v54i2pp.123-145.

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This study employs the Mundell (1963) and Fleming (1962) traditional flow model of exchange rate to examine the long run behaviour of rupee/US $ exchange rate for Pakistan economy over the period 1982:Q1 to 2010:Q2. This study investigates the effect of output levels, interest rates and prices and different shocks on exchange rate. Hylleberg, Engle, Granger, and Yoo (HEGY) (1990) unit root test confirms the presence of non-seasonal unit root and finds no evidence of biannual and annual frequency unit root in the level of series. Johansen and Juselious (1988, 1992) likelihood ratio test indicates three long-run cointegrating vectors. Cointegrating vectors are uniquely identified by imposing structural economic restrictions on purchasing power parity (PPP), uncovered interest parity (UIP) and current account balance. Finally, the short-run dynamic error correction model is estimated on the basis of identified cointegrated vectors. The speed of adjustment coefficient indicates that 17 percent of divergence from long-run equilibrium exchange rate path is being corrected in each quarter. US war with Afghanistan has significant impact on rupee in short run because of high inflows of US aid to Pakistan after 9/11. Finally, the parsimonious short run dynamic error correction model is able to beat the naïve random walk model at out of sample forecasting horizons. JEL Classification: F31, F37, F47 Keywords: Exchange Rate Determination, Keynesian Model, Cointegration, Out of Sample Forecasting, Random Walk Model

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Sivri, Uğur. "Is Inflation Rate of Turkey Stationary? Evidence from Unit Root Tests with and Without Structural Breaks." Review of Economic and Business Studies 10, no. 2 (December 1, 2017): 29–52. http://dx.doi.org/10.1515/rebs-2017-0053.

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AbstractTurkey has high inflation experience and in order to bring inflation rate down as well as maintaining macroeconomic stability many policy changes and reforms have been implemented. Despite some success, decreasing inflation rate is still an aim of monetary policy and price stability is still faraway. This article investigates time series properties of Turkish CPI inflation rate in both seasonally unadjusted and adjusted forms. Results of various unit root tests without structural breaks generally show that inflation rate is a nonstationary variable. This article also uses one and two breaks minimum LM unit root tests due to Lee and Strazicich (2004, 2003), respectively. In this case, test results show that inflation rate is a stationary variable with breaks. Although selected break points differ with respect to models and variables to some extent, it is observed that one break occurred around March 1994, and the second break occurred around April 2001.

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Braimllari (Spaho), Alma, and Oltiana Toshkollari. "Econometric Modeling and Forecasting of Food Exports in Albania." European Journal of Multidisciplinary Studies 1, no. 2 (April 30, 2016): 33. http://dx.doi.org/10.26417/ejms.v1i2.p33-41.

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Exports of goods and services represent one of the most important sources of foreign exchange income that alleviate the pressure on the balance payment and create employment opportunities. Exports’ opportunities in Albania for food products have been increasing significantly, but still they are far from their real capacity. This study is an attempt to model and forecast the monthly export of food, beverages and tobacco products of Albania, using the seasonal autoregressive integrated moving average (SARIMA) methodology. The data used are covering the period 2005:M1-2015:M12, and are taken from the database of National Institute of Statistics of Albania. Unit root tests are used to test the stationarity of the series. Autocorrelation and partial autocorrelation functions are used to identify the most suitable SARIMA model, in explaining the time series and forecasting the future monthly food exports. The residuals of the best fitted model are used for the diagnostic checking. The results indicate that the best fitted model is SARIMA (3,1,1)x(1,1,0)12. The best identified model for the data in the study is used to forecast monthly food exports up to the year 2017. These findings are useful for customers, producers and policymakers.

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Safarpour, S., K. Abdullah, H. S. Lim, and M. Dadras. "SPATIAL INTERPOLATION OF AEROSOL OPTICAL DEPTH POLLUTION: COMPARISON OF METHODS FOR THE DEVELOPMENT OF AEROSOL DISTRIBUTION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W4 (September 27, 2017): 237–44. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w4-237-2017.

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Air pollution is a growing problem arising from domestic heating, high density of vehicle traffic, electricity production, and expanding commercial and industrial activities, all increasing in parallel with urban population. Monitoring and forecasting of air quality parameters are important due to health impact. One widely available metric of aerosol abundance is the aerosol optical depth (AOD). The AOD is the integrated light extinction coefficient over a vertical atmospheric column of unit cross section, which represents the extent to which the aerosols in that vertical profile prevent the transmission of light by absorption or scattering. Seasonal aerosol optical depth (AOD) values at 550 nm derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard NASA’s Terra satellites, for the 10 years period of 2000 - 2010 were used to test 7 different spatial interpolation methods in the present study. The accuracy of estimations was assessed through visual analysis as well as independent validation based on basic statistics, such as root mean square error (RMSE) and correlation coefficient. Based on the RMSE and R values of predictions made using measured values from 2000 to 2010, Radial Basis Functions (RBFs) yielded the best results for spring, summer and winter and ordinary kriging yielded the best results for fall.

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Thies, Walter G., Douglas J. Westlind, and Mark Loewen. "Season of prescribed burn in ponderosa pine forests in eastern Oregon: impact on pine mortality." International Journal of Wildland Fire 14, no. 3 (2005): 223. http://dx.doi.org/10.1071/wf04051.

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A study of the effects of season of prescribed burn on tree mortality was established in mixed-age ponderosa pine (Pinus ponderosa Dougl. ex Laws.) at the south end of the Blue Mountains near Burns, Oregon. Each of six previously thinned stands was subdivided into three experimental units and one of three treatments was randomly assigned to each: fall 1997 burn, spring 1998 burn, and no burning (control). Burns were conducted as operational prescribed burns. Trees within six 0.2-ha circular plots on each experimental unit were observed for four post-burn growing seasons to determine fire damage and to detect immediate and delayed mortality and occurrence of black stain root disease (BSRD). There were 5321 tagged ponderosa pines alive at the time of the burns. The percentage of ponderosa pine dying was higher after fall burns than after spring burns. Differences in percentages of fire-caused mortality may be because fall burns are inherently more severe than spring burns. Although present in many trees, BSRD appeared to have little impact on mortality. The lion’s-tail appearance, thought to be a symptom of BSRD, was found to be an unreliable indicator of BSRD in the six test stands.

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Mustafa, Ghulam, Azhar Abbas, Bader Alhafi Alotaibi, and Fahd O. Aldosri. "Do Erratic Rainfalls Hamper Grain Production? Analysis of Supply Response of Rice to Price and Non-Price Factors." Agronomy 11, no. 8 (July 22, 2021): 1463. http://dx.doi.org/10.3390/agronomy11081463.

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Increasing rice production has become one of the ultimate goals for South Asian countries. The yield and area under rice production are also facing threats due to the consequences of climate change such as erratic rainfall and seasonal variation. Thus, the main aim of this work was to find out the supply response of rice in Malaysia in relation to both price and non-price factors. To achieve this target, time series analysis was conducted on data from 1970 to 2014 using cointegration, unit root test, and the vector error correction model. The results showed that the planted area and rainfall have a significant effect on rice production; however, the magnitude of the impact of rainfall is less conspicuous for off-season (season 2) rice as compared to main-season rice (season 1). The speed of adjustment from short-run to long-run for season-1 rice production is almost two-and-a-half years (five production seasons), while for season-2 production, it is only about one-and-a-half year (three production seasons). Consequently, the study findings imply the supply of water to be enhanced through better water infrastructure for both seasons. Moreover, the area under season 2 is continuously declining to the point where the government has to make sure that farmers are able to cultivate the same area for rice production by providing uninterrupted supply of critical inputs, particularly water, seed and fertilizers.

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Handoko, Rudi. "TAX REVENUE AND ECONOMIC ACTIVITY: SEASONALITY, COINTEGRATION AND CAUSALITY ANALYSIS." Kajian Ekonomi dan Keuangan 3, no. 1 (June 27, 2019): 1–17. http://dx.doi.org/10.31685/kek.v3i1.401.

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The role of taxation is very important for the financing of development in Indonesia. However, the performance of tax revenue has not been optimally indicated by the low ratio of tax to Gross Domestic Product (GDP) which in 2015 the ratio is 10.7% of GDP. This tax ratio is still lower than the tax ratio of neighboring countries such as the Philippines (13.6%), Malaysia (14.3%) and Thailand (16.5%). Even in 2016 the tax ratio declined slightly to 10.3% of GDP.With the scope of the study limited to non-oil and gas income tax and value added tax which contributes more than 80 percent of total tax revenue, this study objective is to understand the relationship between tax revenue and economic activity that can provide insight into the effectiveness of tax policy.This study uses monthly realization of tax revenue both total and sectoral. For economic activity data, we use data derived from national account data or GDP based on expenditure and production approach (sectoral). Monthly tax revenue data is then aggregated into quarterly data to match the frequency of GDP data. Period of data being studied is Q1 2010 - Q4 2017. Tax revenue and economic activity data needs to be seasonal adjusted and then separated into trends and cycles. To examine the relationship between tax revenue and economic activity, we performed several tests such as unit root test, cointegration test and causality test.The main contribution of this study are the empirical testing on the lingkage between tax revenue collection and economic activity and the policy implication of this study which in order to increase the tax revenue collection, government should formulate policy to increase production, investment and consumption because production, investment and consumption will increase tax base.

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Hope, H. J., R. Maamari, S. Séguin, R. I. Hamilton, L. M. Dwyer, and R. P. White. "Low temperature emergence potential of short season corn hybrids grown under controlled environment and plot conditions." Canadian Journal of Plant Science 72, no. 1 (January 1, 1992): 83–91. http://dx.doi.org/10.4141/cjps92-009.

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One important reason for limited progress in selecting corn (Zea mays L.) with superior cold tolerance during germination and early growth is the unpredictable occurrence of cold, wet test springs. Breeders need an efficient laboratory test paralleling field emergence under such climatic conditions. Laboratory screening data from 30 corn hybrids with under 2400 corn heat unit ratings were compared to field emergence data in Atlantic Canada to evaluate the usefulness of a laboratory test as an indicator of cold tolerance. Hybrids were ranked by mean percent emergence using 1985–1989 field data collected from seven locations in the Atlantic region. From daily observations on seed germinated in a germinator at 11 °C, mean times to 50% root and coleoptile initiation, and 1-cm coleoptile were calculated using probit analyses. Field emergence ranked from 1 (highest percent) to 30 was compared to ranking of time to 50% 1-cm coleoptile in the laboratory (one represented most rapid growth). Of the top 15 hybrids (by 1-cm coleoptile), 11 fell in the top half of the hybrids ranked by field emergence. Removal of hybrids with low numbers of field observations, all made during the unusually warm spring of 1989, improved the comparison. These results indicate that time to 50% production of a 1-cm coleoptile is a useful screening parameter to select corn hybrids with superior cold tolerance during germination and early growth.Key words: Corn, maize, low temperature tolerance, emergence

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Harvey, David I., Stephen J. Leybourne, and Paul Newbold. "Seasonal unit root tests with seasonal mean shifts." Economics Letters 76, no. 2 (July 2002): 295–302. http://dx.doi.org/10.1016/s0165-1765(02)00057-5.

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Smith, Richard J., A. M. Robert Taylor, and Tomas del Barrio Castro. "REGRESSION-BASED SEASONAL UNIT ROOT TESTS." Econometric Theory 25, no. 2 (April 2009): 527–60. http://dx.doi.org/10.1017/s0266466608090166.

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The contribution of this paper is threefold. First, a characterization theorem of the subhypotheses comprising the seasonal unit root hypothesis is presented that provides a precise formulation of the alternative hypotheses associated with regression- based seasonal unit root tests. Second, it proposes regression-based tests for the seasonal unit root hypothesis that allow a general seasonal aspect for the data and are similar both exactly and asymptotically with respect to initial values and seasonal drift parameters. Third, limiting distribution theory is given for these statistics where, in contrast to previous papers in the literature, in doing so it is not assumed that unit roots hold at all of the zero and seasonal frequencies. This is shown to alter the large-sample null distribution theory for regression t-statistics for unit roots at the complex frequencies, but interestingly to not affect the limiting null distributions of the regression t-statistics for unit roots at the zero and Nyquist frequencies and regression F-statistics for unit roots at the complex frequencies. Our results therefore have important implications for how tests of the seasonal unit root hypothesis should be conducted in practice. Associated simulation evidence on the size and power properties of the statistics presented in this paper is given that is consonant with the predictions from the large-sample theory.

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34

del Barrio Castro, Tomás, Paulo M. M. Rodrigues, and A. M. Robert Taylor. "SEMI-PARAMETRIC SEASONAL UNIT ROOT TESTS." Econometric Theory 34, no. 2 (April 9, 2017): 447–76. http://dx.doi.org/10.1017/s0266466617000135.

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We extend the ${\cal M}$ class of unit root tests introduced by Stock (1999, Cointegration, Causality and Forecasting. A Festschrift in Honour of Clive W.J. Granger. Oxford University Press), Perron and Ng (1996, Review of Economic Studies 63, 435–463) and Ng and Perron (2001, Econometrica 69, 1519–1554) to the seasonal case, thereby developing semi-parametric alternatives to the regression-based augmented seasonal unit root tests of Hylleberg, Engle, Granger, and Yoo (1990, Journal of Econometrics 44, 215–238). The success of this class of unit root tests to deliver good finite sample size control even in the most problematic (near-cancellation) case where the shocks contain a strong negative moving average component is shown to carry over to the seasonal case as is the superior size/power trade-off offered by these tests relative to other available tests.

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Eroğlu, Burak Alparslan, Kemal Çağlar Göğebakan, and Mirza Trokić. "Powerful nonparametric seasonal unit root tests." Economics Letters 167 (June 2018): 75–80. http://dx.doi.org/10.1016/j.econlet.2018.03.011.

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Fong, Pak Wing, and Wai Keung Li. "On time series with randomized unit root and randomized seasonal unit root." Computational Statistics & Data Analysis 43, no. 3 (July 2003): 369–95. http://dx.doi.org/10.1016/s0167-9473(02)00298-0.

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Maekawa, Koichi. "Prewhitened unit root test." Economics Letters 45, no. 2 (June 1994): 145–53. http://dx.doi.org/10.1016/0165-1765(94)90126-0.

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McDougall, R. Stuart. "Seasonal unit root characteristics of disaggregated output." Applied Economics Letters 3, no. 12 (December 1996): 749–53. http://dx.doi.org/10.1080/135048596355529.

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Hassler, Uwe, and Paulo M. M. Rodrigues. "Seasonal Unit Root Tests Under Structural Breaks*." Journal of Time Series Analysis 25, no. 1 (January 2004): 33–53. http://dx.doi.org/10.1111/j.1467-9892.2004.00336.x.

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Burridge, Peter, and A. M. Robert Taylor. "Bootstrapping the HEGY seasonal unit root tests." Journal of Econometrics 123, no. 1 (November 2004): 67–87. http://dx.doi.org/10.1016/j.jeconom.2003.10.029.

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Choi, Bo-Seung, Jin-Uk Woo, and You-Sung Park. "Locally Powerful Unit-Root Test." Communications for Statistical Applications and Methods 15, no. 4 (July 16, 2008): 531–42. http://dx.doi.org/10.5351/ckss.2008.15.4.531.

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Taylor, A. M. Robert, and Richard J. Smith. "Tests of the Seasonal Unit-Root Hypothesis Against Heteroscedastic Seasonal Integration." Journal of Business & Economic Statistics 19, no. 2 (April 2001): 192–207. http://dx.doi.org/10.1198/073500101316970412.

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Dickey, David A., and Ying Zhang. "Seasonal unit root tests in long periodicity cases." Journal of the Korean Statistical Society 39, no. 3 (September 2010): 271–79. http://dx.doi.org/10.1016/j.jkss.2010.02.006.

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Coelho, C. H. M., and M. Tenenblat. "Trading days, seasonal unit root, and variance change." Long Range Planning 26, no. 1 (February 1993): 155. http://dx.doi.org/10.1016/0024-6301(93)90326-b.

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Coelho, Carlos Henrique Motta, and Moyses Tenenblat. "Trading days, seasonal unit root, and variance change." International Journal of Forecasting 8, no. 1 (June 1992): 61–67. http://dx.doi.org/10.1016/0169-2070(92)90007-v.

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Diaz-Emparanza, Ignacio. "Numerical distribution functions for seasonal unit root tests." Computational Statistics & Data Analysis 76 (August 2014): 237–47. http://dx.doi.org/10.1016/j.csda.2013.03.006.

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Rodrigues, Paulo M. M., and A. M. Robert Taylor. "Efficient tests of the seasonal unit root hypothesis." Journal of Econometrics 141, no. 2 (December 2007): 548–73. http://dx.doi.org/10.1016/j.jeconom.2006.10.007.

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Zou, Nan, and Dimitris N. Politis. "Linear process bootstrap unit root test." Statistics & Probability Letters 145 (February 2019): 74–80. http://dx.doi.org/10.1016/j.spl.2018.08.006.

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Shelef, Amit. "A Gini-based unit root test." Computational Statistics & Data Analysis 100 (August 2016): 763–72. http://dx.doi.org/10.1016/j.csda.2014.08.012.

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Rodrigues, Paulo M. M., and Denise R. Osborn. "Performance of seasonal unit root tests for monthly data." Journal of Applied Statistics 26, no. 8 (December 1999): 985–1004. http://dx.doi.org/10.1080/02664769921981.

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Journal articles on the topic 'Seasonal Unit Root Test'

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