Journal articles: 'Trend and cycle decomposition'

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황영진. "Trend/Cycle Decomposition Using DSGE Models." KDI Journal of Economic Policy 34, no. 4 (November 2012): 117–56. http://dx.doi.org/10.23895/kdijep.2012.34.4.117.

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Morley, James, and Jeremy Piger. "Trend/cycle decomposition of regime-switching processes." Journal of Econometrics 146, no. 2 (October 2008): 220–26. http://dx.doi.org/10.1016/j.jeconom.2008.08.009.

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Basnet, Hem C., Puneet Vatsa, and Subhash Sharma. "Common Trends and Common Cycles in Oil Price and Real Exchange Rate." Global Economy Journal 14, no. 2 (April 5, 2014): 249–63. http://dx.doi.org/10.1515/gej-2013-0042.

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This study explores the long- and short-run movement between oil prices and the real exchange rates of two large oil-exporting countries – Canada and Norway. Cointegration and serial correlation common features tests are jointly used to identify the long-term common trend and short-term common cycles. Our test results find that oil prices and the real exchange rates of the Canadian Dollar and the Norwegian Krone have two shared trends and one shared cycle. The trend–cycle decomposition shows a great deal of positive comovement among the trend and cyclical components. The two currencies show economic dynamics very similar to crude oil prices. They do not exhibit any qualitative differences in the trajectory of the trend and cycles when controlling for different crude oil prices. Our results indicate that oil price fluctuations play significant role in explaining the exchange rate movements of oil-exporting countries.

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Fukuda, Kosei. "Flexible trend-cycle decomposition of nonstationary multivariate time series." Applied Economics 40, no. 2 (January 2008): 135–47. http://dx.doi.org/10.1080/00036840600749573.

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Özbek, Levent, and Ümit Özlale. "Analysis of real oil prices via trend-cycle decomposition." Energy Policy 38, no. 7 (July 2010): 3676–83. http://dx.doi.org/10.1016/j.enpol.2010.02.045.

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Dungey, Mardi, Jan P. A. M. Jacobs, Jing Tian, and Simon van Norden. "TREND IN CYCLE OR CYCLE IN TREND? NEW STRUCTURAL IDENTIFICATIONS FOR UNOBSERVED-COMPONENTS MODELS OF U.S. REAL GDP." Macroeconomic Dynamics 19, no. 4 (June 13, 2014): 776–90. http://dx.doi.org/10.1017/s1365100513000606.

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A well-documented property of the Beveridge–Nelson trend–cycle decomposition is the perfect negative correlation between trend and cycle innovations. We show how this may be consistent with a structural model where permanent innovations enter the cycle or transitory innovations enter the trend, and that identification restrictions are necessary to make this structural distinction. A reduced-form unrestricted version is compatible with either option, but cannot distinguish which is relevant. We discuss economic interpretations and implications using U.S. real GDP data.

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Kyo, Koki, and Genshiro Kitagawa. "Hyper-trend method for seasonal adjustment and trend-cycle decomposition of time series containing long-period cycles." Asian J. of Management Science and Applications 6, no. 2 (2021): 134. http://dx.doi.org/10.1504/ajmsa.2021.120434.

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Dungey, M., J. P. A. M. Jacobs, J. Tian, and S. van Norden. "On the correspondence between data revision and trend-cycle decomposition." Applied Economics Letters 20, no. 4 (March 2013): 316–19. http://dx.doi.org/10.1080/13504851.2012.697118.

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Joo, Young Jin, and Duk Bin Jun. "State space trend-cycle decomposition of the ARIMA(1,1,1) process." Journal of Forecasting 16, no. 6 (November 1997): 411–24. http://dx.doi.org/10.1002/(sici)1099-131x(199711)16:6<411::aid-for659>3.0.co;2-g.

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Votinov, A. I. "The effects of additional non-stationary processes on the properties of DSGE-models." Journal of the New Economic Association 55, no. 3 (2022): 28–43. http://dx.doi.org/10.31737/2221-2264-2022-55-3-2.

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DSGE models are based on the trend-cycle decomposition. The standard approach implies an out-of-model decomposition of the data, in which the trend component is discarded, and the parameters of the model are estimated on the cyclic one. This approach can lead to the loss of statistical information and reduce the quality of the model, which is crucial for practical purposes. The study suggests adding several sector-specifi c exogenous non-stationary processes to the model, which complement the standard DSGE model. The in-model detrending is described, and an approach to GMM-estimation of the non-stationary processes’ parameters is proposed. Several results are obtained. First, the inclusion of such non-stationary processes in the model increases the marginal density and improves the accuracy of forecasting within the sample. This result is robust to the inclusion of measurement errors in the model. Secondly, it is shown that the addition of exogenous trends allows obtaining a more plausible decomposition of data into a trend and a cycle. Finally, the use of the GMM approach to estimating the trends’ parameters makes possible to increase the marginal density. The results obtained in the paper can be used to create practice-oriented DSGE models.

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Morley, James C. "THE TWO INTERPRETATIONS OF THE BEVERIDGE–NELSON DECOMPOSITION." Macroeconomic Dynamics 15, no. 3 (June 10, 2010): 419–39. http://dx.doi.org/10.1017/s1365100510000118.

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The Beveridge–Nelson decomposition calculates trend and cycle for an integrated time series. However, there are two ways to interpret the results from the decomposition. One interpretation is that the optimal long-run forecast (minus any deterministic drift) used to calculate the Beveridge–Nelson trend corresponds to an estimate of an unobserved permanent component. The other interpretation is that the optimal long-run forecast defines an observable permanent component. This paper examines some issues surrounding these two interpretations and provides empirical support for interpreting the Beveridge–Nelson trend as an estimate when considering macroeconomic data.

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Proietti, Tommaso. "Trend–Cycle Decompositions with Correlated Components." Econometric Reviews 25, no. 1 (June 2006): 61–84. http://dx.doi.org/10.1080/07474930500545496.

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Enders, Walter, and Jing Li. "Trend-cycle decomposition allowing for multiple smooth structural changes in the trend of US real GDP." Journal of Macroeconomics 44 (June 2015): 71–81. http://dx.doi.org/10.1016/j.jmacro.2015.02.002.

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Luginbuhl, Rob, and Siem Jan Koopman. "Convergence in European GDP series: a multivariate common converging trend–cycle decomposition." Journal of Applied Econometrics 19, no. 5 (September 2004): 611–36. http://dx.doi.org/10.1002/jae.785.

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Beltran, Daniel O., Mohammad R. Jahan-Parvar, and Fiona A. Paine. "Optimizing Credit Gaps for Predicting Financial Crises: Modelling Choices and Tradeoffs." International Finance Discussion Paper 2021, no. 1307 (January 6, 2021): 1–40. http://dx.doi.org/10.17016/ifdp.2021.1307.

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Credit gaps are good predictors for financial crises, and banking regulators recommend using them to inform countercyclical capital buffers for banks. Researchers typically create credit gap measures using trend-cycle decomposition methods, which require many modelling choices, such as the method used, and the smoothness of the underlying trend. Other choices hinge on the tradeoffs implicit in how gaps are used as early warning indicators (EWIs) for predicting crises, such as the preference over false positives and false negatives. We evaluate how the performance of credit-gap-based EWIs for predicting crises is influenced by these modelling choices. For the most common trend-cycle decomposition methods used to recover credit gaps, we find that optimally smoothing the trend enhances out-of-sample prediction. We also show that out-of sample performance improves further when we consider a preference for robustness of the credit gap estimates to the arrival of new information, which is important as any EWI should work in real-time. We offer several practical implications.

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Andrić, Vladimir, and Sanja Nenadović. "Laplace, Hansen-Sargent and Beveridge-Nelson a note towards unified business application." Serbian Journal of Management 16, no. 1 (2021): 39–47. http://dx.doi.org/10.5937/sjm16-30877.

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The paper derives the equivalence condition between the Beveridge-Nelson decomposition and the Hansen-Sargent prediction formula in continuous time using Laplace transforms. The results presented show how the Hansen-Sargent prediction formula is relevant not just for present value discounting but also for the trend-cycle decomposition of cash flow streams in corporate finance.

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Anastasiou, Dimitrios, Konstantinos Drakos, and Stylianos Giannoulakis. "Are Bank Credit Standards Affected by the Business Cycle? Evidence from the Euro Area." Special Issue on Applied Macroeconomics, Finance, and Banking 64, no. 1 (January 1, 2018): 5–16. http://dx.doi.org/10.3790/aeq.64.1.5.

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Abstract The purpose of this study is to investigate the link between bank credit standards (CS hereafter) and business cycle fluctuations. This is the first empirical study which attempts to examine whether business cycle affects bank CS. We use quarterly survey-data on CS taken from the Bank Lending Survey from 2003Q1 to 2016Q1, for 14 Euro-area countries. We find that business cycle and GDP growth trend exert a negative influence on CS, and thus business cycle and trend are two major drivers of the tightening or easing of the CS. We also find that the two components (cyclical and trend) of the real GDP decomposition affect in a symmetric way CS. Moreover, symmetry of impacts was found between the CS and the business cycle and trend for large vs. small firms. Our findings could be helpful for both the European bank regulatory authorities and for the banks’ loan officers when they are designing macroprudential policies. JEL classifications: E30, E32, E44, G21 Keywords: credit standards, business cycle, bank lending survey, loan supply

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Tan, Zhengxun, Juan Liu, and Peng Chen. "The trend and cycle components of China’s housing prices: a new decomposition method." Applied Economics 53, no. 28 (February 9, 2021): 3288–305. http://dx.doi.org/10.1080/00036846.2021.1877254.

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Lippi, M., and L. Reichlin. "Diffusion of Technical Change and the Decomposition of Output into Trend and Cycle." Review of Economic Studies 61, no. 1 (January 1, 1994): 19–30. http://dx.doi.org/10.2307/2297874.

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Fukuda, Kosei. "The validity of trend-cycle decomposition using unobserved component model: Monte Carlo evidence." Applied Economics Letters 15, no. 5 (April 2008): 367–69. http://dx.doi.org/10.1080/13504850600706164.

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CAMMAROTA, CAMILLO, and MARIO CURIONE. "TIME DELAY BETWEEN RR AND RT HEART BEAT INTERVALS ASSESSED BY TREND EXTRACTION OF EXERCISE TEST DATA." Fluctuation and Noise Letters 11, no. 04 (December 2012): 1250019. http://dx.doi.org/10.1142/s0219477512500198.

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The RR and RT time intervals extracted from the electrocardiogram measure respectively the duration of cardiac cycle and repolarization. The series of these intervals recorded during the exercise test are characterized by two trends: A decreasing one during the stress phase and an increasing one during the recovery, separated by a global minimum. We model these series as a sum of a deterministic trend and random fluctuations, and estimate the trend using methods of curve extraction: Running mean, polynomial fit, multi scale wavelet decomposition. We estimate the minimum location from the trend. Data analysis performed on a group of 20 healthy subjects provides evidence that the minimum of the RR series precedes the minimum of the RT series, with a time delay of about 19 seconds.

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Kapounek, S., and J. Poměnková. "The endogeneity of optimum currency area criteria in the context of financial crisis: Evidence from the time-frequency domain analysis." Agricultural Economics (Zemědělská ekonomika) 59, No. 9 (October 4, 2013): 389–95. http://dx.doi.org/10.17221/9/2013-agricecon.

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We provide the wavelet analysis of the economic cycle synchronization during the recent financial crisis. However, the global financial crisis caused economic cycles in most European countries to become more strongly synchronized without increasing of the real convergence process. Our contribution is an application of the singular value decomposition to identify and remove the long-term trend including outliers appearing in the year 2007–2010. We found that the historically greater integration provides more highly synchronized cycles in the core Euro area member countries.  

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Handcock, Mark S., and Marilyn N. Raphael. "Modeling the annual cycle of daily Antarctic sea ice extent." Cryosphere 14, no. 7 (July 2, 2020): 2159–72. http://dx.doi.org/10.5194/tc-14-2159-2020.

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Abstract. The total Antarctic sea ice extent (SIE) experiences a distinct annual cycle, peaking in September and reaching its minimum in February. In this paper we propose a mathematical and statistical decomposition of this temporal variation in SIE. Each component is interpretable and, when combined, gives a complete picture of the variation in the sea ice. We consider timescales varying from the instantaneous and not previously defined to the multi-decadal curvilinear trend, the longest. Because our representation is daily, these timescales of variability give precise information about the timing and rates of advance and retreat of the ice and may be used to diagnose physical contributors to variability in the sea ice. We define a number of annual cycles each capturing different components of variation, especially the yearly amplitude and phase that are major contributors to SIE variation. Using daily sea ice concentration data, we show that our proposed invariant annual cycle explains 29 % more of the variation in daily SIE than the traditional method. The proposed annual cycle that incorporates amplitude and phase variation explains 77 % more variation than the traditional method. The variation in phase explains more of the variability in SIE than the amplitude. Using our methodology, we show that the anomalous decay of sea ice in 2016 was associated largely with a change of phase rather than amplitude. We show that the long term trend in Antarctic sea ice extent is strongly curvilinear and the reported positive linear trend is small and dependent strongly on a positive trend that began around 2011 and continued until 2016.

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Kimball, J. S., M. Zhao, A. D. McGuire, F. A. Heinsch, J. Clein, M. Calef, W. M. Jolly, et al. "Recent Climate-Driven Increases in Vegetation Productivity for the Western Arctic: Evidence of an Acceleration of the Northern Terrestrial Carbon Cycle." Earth Interactions 11, no. 4 (February 1, 2007): 1–30. http://dx.doi.org/10.1175/ei180.1.

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Abstract Northern ecosystems contain much of the global reservoir of terrestrial carbon that is potentially reactive in the context of near-term climate change. Annual variability and recent trends in vegetation productivity across Alaska and northwest Canada were assessed using a satellite remote sensing–based production efficiency model and prognostic simulations of the terrestrial carbon cycle from the Terrestrial Ecosystem Model (TEM) and BIOME–BGC (BioGeoChemical Cycles) model. Evidence of a small, but widespread, positive trend in vegetation gross and net primary production (GPP and NPP) is found for the region from 1982 to 2000, coinciding with summer warming of more than 1.8°C and subsequent relaxation of cold temperature constraints to plant growth. Prognostic model simulation results were generally consistent with the remote sensing record and also indicated that an increase in soil decomposition and plant-available nitrogen with regional warming was partially responsible for the positive productivity response. Despite a positive trend in litter inputs to the soil organic carbon pool, the model results showed evidence of a decline in less labile soil organic carbon, which represents approximately 75% of total carbon storage for the region. These results indicate that the regional carbon cycle may accelerate under a warming climate by increasing the fraction of total carbon storage in vegetation biomass and more rapid turnover of the terrestrial carbon reservoir.

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WADA, TATSUMA. "THE REAL EXCHANGE RATE AND REAL INTEREST DIFFERENTIALS: THE ROLE OF THE TREND-CYCLE DECOMPOSITION." Economic Inquiry 50, no. 4 (July 3, 2011): 968–87. http://dx.doi.org/10.1111/j.1465-7295.2011.00387.x.

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Shirvani, Hassan, and Barry Wilbratte. "The permanent income hypothesis in five major industrial countries: a multivariate trend-cycle decomposition test." Journal of Economics and Finance 33, no. 1 (July 16, 2008): 43–59. http://dx.doi.org/10.1007/s12197-008-9053-3.

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SHANG, PENGJIAN, KEQIANG DONG, LINGLING ZHAO, and SANTI KAMAE. "EMPIRICAL MODE DECOMPOSITION AND CORRELATION PROPERTIES OF TRAFFIC FLUCTUATION." Fluctuation and Noise Letters 09, no. 02 (June 2010): 167–78. http://dx.doi.org/10.1142/s0219477510000113.

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This work describes the application of an important tool which can extract periodic information from the time-series of fluctuating traffic data. Compared to the traditional approach using FFT techniques, the nonlinear empirical mode decomposition (EMD) method has a number of advantages. This method is adaptive and therefore highly efficient at identifying embedded structures, even those with small amplitudes. Using this analysis, the traffic time series can be completely decomposed into five temporal modes including a 24-hour cycle, a 1-week cycle and a trend. Simultaneity, long-range correlations in traffic time series are investigated by detrended fluctuation analysis (DFA). In order to accurately capture the scaling exponents, EMD analysis is performed for DFA of the traffic records. The results of DFA for the data cleaned by subtracting the first intrinsic mode functions (IMF) are apparently improved, although the DFA curves are not entirely straight on the log-log plot.

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Qian, Cheng, Congbin Fu, and Zhaohua Wu. "Changes in the Amplitude of the Temperature Annual Cycle in China and Their Implication for Climate Change Research." Journal of Climate 24, no. 20 (October 15, 2011): 5292–302. http://dx.doi.org/10.1175/jcli-d-11-00006.1.

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Abstract Climate change is not only reflected in the changes in annual means of climate variables but also in the changes in their annual cycles (seasonality), especially in the regions outside the tropics. In this study, the ensemble empirical mode decomposition (EEMD) method is applied to investigate the nonlinear trend in the amplitude of the annual cycle (which contributes 96% of the total variance) of China’s daily mean surface air temperature for the period 1961–2007. The results show that the variation and change in the amplitude are significant, with a peak-to-peak annual amplitude variation of 13% (1.8°C) of its mean amplitude and a significant linear decrease in amplitude by 4.6% (0.63°C) for this period. Also identified is a multidecadal change in amplitude from significant decreasing (−1.7% decade−1 or −0.23°C decade−1) to significant increasing (2.2% decade−1 or 0.29°C decade−1) occurring around 1993 that overlaps the systematic linear trend. This multidecadal change can be mainly attributed to the change in surface solar radiation, from dimming to brightening, rather than to a warming trend or an enhanced greenhouse effect. The study further proposes that the combined effect of the global dimming–brightening transition and a gradual increase in greenhouse warming has led to a perceived warming trend that is much larger in winter than in summer and to a perceived accelerated warming in the annual mean since the early 1990s in China. It also notes that the deseasonalization method (considering either the conventional repetitive climatological annual cycle or the time-varying annual cycle) can also affect trend estimation.

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Lima, Carlos Eduardo Sousa, Marx Vinicius Maciel da Silva, Sofia Midauar Godim Rocha, and Cleiton da Silva Silveira. "Anthropic Changes in Land Use and Land Cover and Their Impacts on the Hydrological Variables of the São Francisco River Basin, Brazil." Sustainability 14, no. 19 (September 26, 2022): 12176. http://dx.doi.org/10.3390/su141912176.

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The growing impact of human activities on the environment has increased their influence on the planet’s natural cycles, especially in relation to the hydrological cycle of watersheds. The fundamental processes for its water and energy balance have been affected, which influences water availability and surface streamflow. This study sought to evaluate the anthropogenic impacts on the hydrological cycle of the São Francisco River Basin (SFRB), Brazil, between 1985 and 2015. The study area comprised SFRB and 10 sub-basins for general and specific analyses, respectively. Analyzed data consisted of Land Use and Land Cover (LULC), precipitation, streamflow, and temperature. The methodology incorporated: (i) assessment of LULC dynamics; (ii) trend analysis with the Mann–Kendall method and Sen’s Slope; and (iii) decomposition of total streamflow variation via Budyko’s hypothesis and climate elasticity of streamflow. As a result, it was possible to detect an anthropic modification of SFRB, which is the main component of its streamflow variation, in addition to increased streamflow sensitivity to climate variations. In addition, the divergent behavior in the trends of hydrological variables suggests a change in the streamflow response to precipitation. Therefore, the results allowed us to identify and quantify the impacts of anthropic modifications on the hydrological cycle of the SFRB.

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Lan, Wen-Hau, Chung-Yen Kuo, Li-Ching Lin, and Huan-Chin Kao. "Annual Sea Level Amplitude Analysis over the North Pacific Ocean Coast by Ensemble Empirical Mode Decomposition Method." Remote Sensing 13, no. 4 (February 17, 2021): 730. http://dx.doi.org/10.3390/rs13040730.

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Understanding spatial and temporal changes of seasonal sea level cycles is important because of direct influence on coastal systems. The annual sea level cycle is substantially larger than semi-annual cycle in most parts of the ocean. Ensemble empirical mode decomposition (EEMD) method has been widely used to study tidal component, long-term sea level rise, and decadal sea level variation. In this work, EEMD is used to analyze the observed monthly sea level anomalies and detect annual cycle characteristics. Considering that the variations of the annual sea level variation in the Northeast Pacific Ocean are poorly studied, the trend and characteristics of annual sea level amplitudes and related mechanisms in the North Pacific Ocean are investigated using long-term tide gauge records covering 1950–2016. The average annual amplitude of coastal sea level exhibits interannual-to-decadal variability within the range of 14–220 mm. The largest value of ~174 mm is observed in the west coast of South China Sea. In the other coastal regions of North Pacific Ocean, the mean annual amplitude is relatively low between 77 and 124 mm for the western coast and 84 and 87 mm for the eastern coast. The estimated trend values for annual sea level amplitudes in the western coastal areas of South China Sea and Northeast Pacific Ocean have statistically decreased over 1952–2014 with a range of −0.77 mm·yr−1 to −0.11 mm·yr−1. Our results suggested that the decreasing annual amplitude in the west coast of South China Sea is in good agreement with the annual mean wind stress associated with the Pacific Decadal Oscillation (PDO). This wind phenomenon also explains the temporal variations of annual sea level amplitude in Northeast Pacific Ocean, especially the high correlations since 1980 (R = 0.61−0.72).

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Ansary, M. Abdullah, and M. Alamgir Hossain. "Climatic change of Bangladesh over a cycle of period 35 years." Journal of Bangladesh Academy of Sciences 39, no. 1 (June 14, 2015): 73–82. http://dx.doi.org/10.3329/jbas.v39i1.23661.

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Bangladesh has been undergoing hazardous climatic changes. Monsoon is changing its pattern. Variations of temperature, humidity, rainfall are remarkable. The seasonal changes of Bangladesh such as the transition from winter to spring, summer to autumn in temperate zone, and from wet to dry in the tropics are remarkable to take into consideration. The climatic changes have great influence on the distribution of plants and animals and in the formation of soils through the weathering of geological materials and the decomposition or preservation of organic matter. Various weather parameters over a cycle were studied and determined the coefficient of variations to compare the past, present and future situation in Bangladesh. The frequency distribution and the shift in distribution of monsoon rainfall were determined and analyzed to show the trend of climate changes and their impact on environment. The important findings are the surface temperature rise by 0.60C and the rainfall in monsoon likely to be increased by 12% in the year 2030. However, the seasonal rainfall trend is decreasingJournal of Bangladesh Academy of Sciences, Vol. 39, No. 1, 73-82, 2015

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Schumacher, Christian. "Trend and Cycle in the Euro-Area: A Permanent-Transitory Decomposition Using a Cointegrated VAR Model." Vierteljahrshefte zur Wirtschaftsforschung 70, no. 3 (July 2001): 352–63. http://dx.doi.org/10.3790/vjh.70.3.352.

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GRANT, ANGELIA L., and JOSHUA C. C. CHAN. "A Bayesian Model Comparison for Trend-Cycle Decompositions of Output." Journal of Money, Credit and Banking 49, no. 2-3 (March 2017): 525–52. http://dx.doi.org/10.1111/jmcb.12388.

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CHANNON, D., M. COLE, and L. COLE. "A long-term study of Rattus norvegicus in the London Borough of Enfield using baiting returns as an indicator of sewer population levels." Epidemiology and Infection 125, no. 2 (October 2000): 441–45. http://dx.doi.org/10.1017/s095026889900446x.

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This is a long-term study that investigates the dynamics of a population of Rattus norvegicus (Berk) inhabiting a sewerage system in London. Thirteen years (1986/7–1998/9) of data from sewer baiting records were analysed (a total of 35478 records). Manholes were baited with the anticoagulant Brodifacoum (0·005%) on a pinhead oatmeal bait base. Time series analysis was conducted on the data set to determine the underlying trend of the data and the population fluctuations about this trend. An exponential curve was found to give an accurate and realistic fit to the data and indicated that the rat population had decreased over the study period. Decomposition analysis indicated a 5-year cycle best described fluctuations around this trend.

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Nelson, Charles R. "Spurious trend and cycle in the state space decomposition of a time series with a unit root." Journal of Economic Dynamics and Control 12, no. 2-3 (June 1988): 475–88. http://dx.doi.org/10.1016/0165-1889(88)90051-6.

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Lippi, Marco, and Lucrezia Reichlin. "Trend-Cycle Decompositions and Measures of Persistence: Does Time Aggregation Matter?" Economic Journal 101, no. 405 (March 1991): 314. http://dx.doi.org/10.2307/2233821.

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Guérin, Pierre, Laurent Maurin, and Matthias Mohr. "TREND-CYCLE DECOMPOSITION OF OUTPUT AND EURO AREA INFLATION FORECASTS: A REAL-TIME APPROACH BASED ON MODEL COMBINATION." Macroeconomic Dynamics 19, no. 2 (October 9, 2013): 363–93. http://dx.doi.org/10.1017/s1365100513000461.

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This paper estimates univariate and multivariate trend-cycle decomposition models of GDP and considers the novel possibility of regime switches in the growth of potential output. We compute both ex post and real-time estimates of the output gap to check the stability of our estimates to GDP data revisions. We find some evidence of regime changes in the growth of potential output during the recessions experienced by the euro area. We also run a forecasting experiment to evaluate the predictive power of the output gap for inflation. The benchmark autoregressive model tends to obtain the best forecasts for one-quarter-ahead forecasts, but the output gap measures help to forecast inflation for longer horizons.

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SEMENYCHEV, Valerii K., Galina A. KHMELEVA, and Anastasiya A. KOROBETSKAYA. "Polymodeling of time series structures, neighborhood of residuals distribution, wavelet transformation for meso-dynamics assessment." Economic Analysis: Theory and Practice 20, no. 10 (October 29, 2021): 1951–72. http://dx.doi.org/10.24891/ea.20.10.1951.

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Subject. The article provides the results of meso-dynamics analysis of main twelve industries, based on monthly data for 82 Russian regions, from January 2005 till December 2020. Objectives. The purpose of the study is to address the problem of balanced and stable spatial development of Russia’s regions and Russia, which requires modeling of adequate tools and forecasting nonlinear mesodynamics. Methods. The study follows the econophysics methodology. Results. We consider additive and multiplicative interactions of regular time series components between each other and the residuals, thus expanding the scope of tools application for the variety of considered industries and their models. Using the common and new trend models, we analyze structural changes, introduce the topological measure of proximity to the neighborhood of residuals with heavy-tailed distribution, which is estimated by median values of trends and cycles for regular components. The traditional time series decomposition (by trend, cycle, seasonality, and residual) is supplemented by our unique complex of wavelet transformations, which forms the models of cycles, using auto regression. We obtained representative and time-synchronized analytical estimates of regular components of industries’ dynamics for meso- and macro-indicators of the Russian economy that have higher accuracy than the known results for the accuracy of modeling and forecasting. Conclusions. The offered methodology and tools enable a more adequate analysis of non-linear dynamics of regions’ middle-term development. They help shift to growth point identification, create the atlas of economic industrial cycles, analyze stages of bifurcations and scenario predictive planning.

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Fuchs, Johann, and Enzo Weber. "A new look at the discouragement and the added worker hypotheses: applying a trend–cycle decomposition to unemployment." Applied Economics Letters 20, no. 15 (October 2013): 1374–78. http://dx.doi.org/10.1080/13504851.2013.812777.

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Busetti, Fabio, and Michele Caivano. "The trend–cycle decomposition of output and the Phillips curve: Bayesian estimates for Italy and the Euro area." Empirical Economics 50, no. 4 (July 23, 2015): 1565–87. http://dx.doi.org/10.1007/s00181-015-0982-3.

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Boleti, Eirini, Christoph Hueglin, Stuart K. Grange, André S. H. Prévôt, and Satoshi Takahama. "Temporal and spatial analysis of ozone concentrations in Europe based on timescale decomposition and a multi-clustering approach." Atmospheric Chemistry and Physics 20, no. 14 (July 30, 2020): 9051–66. http://dx.doi.org/10.5194/acp-20-9051-2020.

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Abstract. Air quality measures that were implemented in Europe in the 1990s resulted in reductions of ozone precursor concentrations. In this study, the effect of these reductions on ozone is investigated by analyzing surface measurements of this pollutant for the time period between 2000 and 2015. Using a nonparametric timescale decomposition methodology, the long-term, seasonal and short-term variation in ozone observations were extracted. A clustering algorithm was applied to the different timescale variations, leading to a classification of sites across Europe based on the temporal characteristics of ozone. The clustering based on the long-term variation resulted in a site-type classification, while a regional classification was obtained based on the seasonal and short-term variations. Long-term trends of deseasonalized mean and meteo-adjusted peak ozone concentrations were calculated across large parts of Europe for the time period 2000–2015. A multidimensional scheme was used for a detailed trend analysis, based on the identified clusters, which reflect precursor emissions and meteorological influence either on the inter-annual or the short-term timescale. Decreasing mean ozone concentrations at rural sites and increasing or stabilizing at urban sites were observed. At the same time, downward trends for peak ozone concentrations were detected for all site types. In addition, a reduction of the amplitude in the seasonal cycle of ozone and a shift in the occurrence of the seasonal maximum towards earlier time of the year were observed. Finally, a reduced sensitivity of ozone to temperature was identified. It was concluded that long-term trends of mean and peak ozone concentrations are mostly controlled by precursor emissions changes, while seasonal cycle trends and changes in the sensitivity of ozone to temperature are among other factors driven by regional climatic conditions.

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Vecchio, A., M. Laurenza, M. Storini, and V. Carbone. "New Insights on Cosmic Ray Modulation through a Joint Use of Nonstationary Data-Processing Methods." Advances in Astronomy 2012 (2012): 1–9. http://dx.doi.org/10.1155/2012/834247.

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The time variability of the cosmic ray (CR) intensity, recorded by the Climax neutron monitor and covering the period 1953–2004, has been analyzed by the joint application of the wavelet and the empirical mode decomposition (EMD) analyses. Dominant time scales of variability are found at~11 yr,~22 yr,~6 yr and in the range of the quasi-biennial oscillations (QBOs). The combination of the 11 yr cycle and QBOs explains the Gnevychev Gap (GG) phenomenon and many step-like decreases characterizing the CR modulation. The additional scales of variability at~22 yr and~6 yr are responsible for other features of the long-term CR trend, such as the intensity flat-topped profile, following the maxima of even-numbered cycles during positive polarity state of the heliosphere (). Comparison with basic time scales of variability derived from the sunspot area (SA) allows the association of the 11 yr cycle and QBOs with solar activity variations, whereas the other two modes with the drift effects govern the CR entrance in the heliosphere.

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Gawel, Aleksandra, and Agnieszka Głodowska. "On the Relationship between Economic Dynamics and Female Entrepreneurship: Reflections for the Visegrad Countries." Administrative Sciences 11, no. 3 (August 24, 2021): 88. http://dx.doi.org/10.3390/admsci11030088.

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The gender gap in entrepreneurship has been observed for a long time, explained by both female-specific and gender-neutral factors, but none of these explanations is generally accepted. The aim of the paper is to assess the effect of internal economic dynamics on female entrepreneurship. Economic dynamics is a persistent process affected simultaneously by both endogenous and exogenous factors of a different time horizon, with the development trend and the business cycle as the most important time perspectives. The decomposition procedure of time series is implemented to extract trend and cyclical fluctuations, after which the Vector Error Correction Model (VECM) method is used to estimate models showing the impact of economic dynamics on female entrepreneurship in the long- and medium-run. The study concerns the countries of the Visegrad Group, including Czechia, Hungary, Poland, and Slovakia, and is based on quarterly data from the years 1998 to 2020. The results show that, although the economic dynamics impact female entrepreneurship, to some extent, it is not the most dominant factor. The impact of economic dynamics on female entrepreneurship is much stronger in the trend perspective than in the business cycle perspective. The nature of the effect of economic dynamics on female entrepreneurship is also country-specific.

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Kim, Jaeho, and Sora Chon. "Why are Bayesian trend-cycle decompositions of US real GDP so different?" Empirical Economics 58, no. 3 (September 5, 2018): 1339–54. http://dx.doi.org/10.1007/s00181-018-1554-0.

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KOBAYASHI-KIRSCHVINK, KOSEKI J., KING-FAI LI, RUN-LIE SHIA, and YUK L. YUNG. "FUNDAMENTAL MODES OF ATMOSPHERIC CFC-11 FROM EMPIRICAL MODE DECOMPOSITION." Advances in Adaptive Data Analysis 04, no. 04 (October 2012): 1250024. http://dx.doi.org/10.1142/s1793536912500240.

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Following an initial growth, the concentrations of chlorofluorocarbon-11 (CFC-11) in the atmosphere started to decline in the 1990's due to world-wide legislative control on emissions. The amplitude of the annual cycle of CFC-11 was much larger in the earlier period compared with that in the later period. We apply here the Ensemble Empirical Mode Decomposition (EEMD) analysis to the CFC-11 data obtained by the U.S. National Oceanic and Atmospheric Administration. The sum of the second and third intrinsic mode functions (IMFs) represents the annual cycle, which shows that the annual cycle of CFC-11 has varied by a factor of 2–3 from the mid-1970's to the present over polar regions. The results provide an illustration of the power of the EEMD method in extracting a variable annual cycle from data dominated by increasing and decreasing trends. Finally, we compare the annual cycle obtained by the EEMD analysis to that obtained using conventional methods such as Fourier transforms and running averages.

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Lytvynenko, Iaroslav, Serhii Lupenko, Oleh Nazarevych, Hryhorii Shymchuk, and Volodymyr Hotovych. "Additive mathematical model of gas consumption process." Scientific journal of the Ternopil national technical university 104, no. 4 (2021): 87–97. http://dx.doi.org/10.33108/visnyk_tntu2021.04.087.

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The problem of construction of a new mathematical model of the gas consumption process is considered in this paper. The new mathematical model is presented as an additive mixture of three components: cyclic random process, trend component and stochastic residue. The process of obtaining three components is carried out on the basis of caterpillar method, thus obtaining ten components of singular decomposition. In this approach, the cyclic component is formed from the sum of nine components of the schedule, which have one thing in common – repeated deployment over time. The trend component of the new mathematical model is the second component of singular decomposition, and the stochastic residue is formed on the basis of the difference between the values of the studied gas consumption process and the sum of cyclic and trend components. Two approaches to stochastic processing of cyclic gas consumption process based on the known model of stochastic-periodic random process and cyclic random process as models of the cyclic component are used in this paper. Application of mathematical model of cyclic component in the form of cyclic random process with cyclic structure makes it possible to obtain estimation of variance on cycle of gas consumption process, provided segmentation of cyclic component on depressions, much less in comparison of obtained variance estimation for indicating greater accuracy in the study of the gas consumption process and will use the obtained stochastic estimates when modeling the gas consumption process in further studies.

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Da Silva, Ligia A., and Prakki Satyamurty. "Evolution of the Lorenz Energy Cycle in the Intertropical Convergence Zone in the South American Sector of the Atlantic Ocean." Journal of Climate 26, no. 10 (May 8, 2013): 3466–81. http://dx.doi.org/10.1175/jcli-d-11-00426.1.

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Abstract The intertropical convergence zone (ITCZ) in the South American sector of the Atlantic Ocean is identified using outgoing longwave radiation (OLR) data in order to investigate the evolution of the Lorenz energy cycle in the region dominated by this large-scale feature. The NCEP reanalysis data are utilized to calculate the zonal and eddy components (denoted by Z and E, respectively) of kinetic energy K and available potential energy A (i.e., KZ, KE, AZ, and AE) and their conversions, on a daily basis. A wavelet decomposition of the time series is performed to detect long-term cycles/trends in the Atlantic ITCZ region. This work also investigates trends in sea surface temperature (SST) and sea level pressure (SLP) in the ITCZ region and connections between the ITCZ and the Southern Oscillation index (SOI). A strong annual cycle in all the energy components with high peaks in austral summer is observed. Approximately 91% of the zonal component of energy is contained on decadal or longer time scales. The annual and semiannual variabilities are significant and the synoptic-scale variability is also present. The zonal component of kinetic energy KZ presents a decreasing trend during the last 28 years, which means a weakening of trade winds in the region studied. The values of KZ and AE are significantly higher during the period 1982/83, indicating that the intense El Niño–Southern Oscillation (ENSO) and/or the El Chichón eruption may have affected the circulation in the ITCZ region. The 28-yr mean energy conversion and generation terms are in general weaker than in the hemispheric calculations but the energy conversions proceed in the same sense as in the hemispheric situation.

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Carvalho, Joana, Lucas Nascimento, Margarida Soares, Nádia Valério, André Ribeiro, Luciana Faria, André Silva, Nuno Pacheco, Jorge Araújo, and Cândida Vilarinho. "Life Cycle Assessment (LCA) of Biochar Production from a Circular Economy Perspective." Processes 10, no. 12 (December 13, 2022): 2684. http://dx.doi.org/10.3390/pr10122684.

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Climate change and environmental sustainability are among the most prominent issues of today. It is increasingly fundamental and urgent to develop a sustainable economy, capable of change the linear paradigm, actively promoting the efficient use of resources, highlighting product, component and material reuse. Among the many approaches to circular economy and zero-waste concepts, biochar is a great example and might be a way to push the economy to neutralize carbon balance. Biochar is a solid material produced during thermochemical decomposition of biomass in an oxygen-limited environment. Several authors have used life cycle assessment (LCA) method to evaluate the environmental impact of biochar production. Based on these studies, this work intends to critically analyze the LCA of biochar production from different sources using different technologies. Although these studies reveal differences in the contexts and characteristics of production, preventing direct comparison of results, a clear trend appears. It was proven, through combining life cycle assessment and circular economy modelling, that the application of biochar is a very promising way of contributing to carbon-efficient resource circulation, mitigation of climate change, and economic sustainability.

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Breaker, Laurence C., and Dustin Carroll. "A Closer Look at Power-Law Scaling Applied to Sea Surface Temperature from Scripps Pier Using Empirical Mode Decomposition." Journal of Atmospheric and Oceanic Technology 38, no. 4 (April 2021): 777–87. http://dx.doi.org/10.1175/jtech-d-20-0124.1.

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AbstractThe purpose of this study is to extract more information about the scaling exponents we obtain from sea surface temperature (SST) because their information content is limited to a single value. We examine the application of empirical mode decomposition (EMD) to power-law scaling using SST from Scripps Pier, California. The daily observations we employ extend from 1920 to 2009, a period of 90 years. The annual cycle and the long-term trend were first removed. The decomposition produced a total of 15 modes. The scaling exponents were then calculated separately for each mode from the EMD. We have examined the distribution of scaling exponents with respect to the ensemble, and then with respect to the individual modes for the oceanic processes that we may infer from them. The first three modes are antipersistent and contain about one-quarter of the total variance. The pattern of modes that was obtained is continuous and relatively smooth beyond mode 3 with increasing values up to mode 8 and generally decreasing values thereafter. The pattern exhibits intramodal correlation, as expected, and intermodal correlation as well. Intermodal correlation is likely due, for the most part, to long-range persistence. The annual cycle in SST at Scripps Pier is a dominant feature in the record and contains almost 70% of the variance. A method for removing the annual cycle that is not based on removing the mean value is introduced and is recommended for future use.

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Prompun, Poramate, Sakhon Ratchahat, Watchareeya Kaveevivitchai, and Wanida Kooamornpattana. "Carbon Nanotube (CNTs) Production From Waste Cooking Oil As Anode Material For Li-Ion Batteries." Journal of Physics: Conference Series 2175, no. 1 (January 1, 2022): 012041. http://dx.doi.org/10.1088/1742-6596/2175/1/012041.

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Abstract Carbon nanotube (CNTs) have received growing interest as anode material on Li-ion batteries due to high electrical conductivity, large surface area and strong structure. In this study, CNTs were synthesized from waste cooking oil (WCO) by chemical decomposition process with various reaction temperatures (800, 900 and 1000 °C) and using ferrocene as catalysts. The characteristics of CNTs were examined by X-ray diffraction (XRD), scanning electron microscopy (SEM) and gas adsorption analysis. Results show that the trend of pore size decreased, whereas the specific surface area of CNT increased with increasing reaction temperature. The electrochemical performance of CNTs samples as anode material for lithiumion batteries were investigated at C/4 rate. The results found that the highest specific discharge capacity was CNTs at reaction temperature 900 °C (474.34 mAh/g for first cycle and 213.75 mAh/g for 30 cycles) due to pore size and the specific surface area were suitable for Li-ion storage properties.

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Journal articles on the topic 'Trend and cycle decomposition'

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