Gotowe bibliografie tematyczne / Forecast error variance decomposition

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Gotowa bibliografia na temat „Forecast error variance decomposition”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 29 stycznia 2023

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Artykuły w czasopismach na temat "Forecast error variance decomposition"

McKenzie, Andrew M., Harold L. Goodwin, and Rita I. Carreira. "Alternative Model Selection Using Forecast Error Variance Decompositions in Wholesale Chicken Markets." Journal of Agricultural and Applied Economics 41, no. 1 (April 2009): 227–40. http://dx.doi.org/10.1017/s1074070800002650.

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Although Vector Autoregressive models are commonly used to forecast prices, specification of these models remains an issue. Questions that arise include choice of variables and lag length. This article examines the use of Forecast Error Variance Decompositions to guide the econometrician's model specification. Forecasting performance of Variance Autoregressive models, generated from Forecast Error Variance Decompositions, is analyzed within wholesale chicken markets. Results show that the Forecast Error Variance Decomposition approach has the potential to provide superior model selections to t

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Gorodnichenko, Yuriy, and Byoungchan Lee. "Forecast Error Variance Decompositions with Local Projections." Journal of Business & Economic Statistics 38, no. 4 (July 18, 2019): 921–33. http://dx.doi.org/10.1080/07350015.2019.1610661.

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Lanne, Markku, and Henri Nyberg. "Generalized Forecast Error Variance Decomposition for Linear and Nonlinear Multivariate Models." Oxford Bulletin of Economics and Statistics 78, no. 4 (January 26, 2016): 595–603. http://dx.doi.org/10.1111/obes.12125.

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Staszewska-Bystrova, Anna. "Monte Carlo Analysis of Forecast Error Variance Decompositions under Alternative Model Identification Schemes." Acta Universitatis Lodziensis. Folia Oeconomica 5, no. 338 (September 28, 2018): 115–31. http://dx.doi.org/10.18778/0208-6018.338.07.

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The goal of the paper is to investigate the estimation precision of forecast error variance decomposition (FEVD) based on stable structural vector autoregressive models identified using short‑run and long‑run restrictions. The analysis is performed by means of Monte Carlo experiments. It is demonstrated that for processes with roots close to one, selected FEVD parameters can be estimated more accurately using recursive restrictions on the long‑run multipliers than under recursive restrictions on the impact effects of shocks. This finding contributes to the discussion of pros and cons of using

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God', N. A., and stime Osekhebhen Eigbiremolen. "Savings, investment and economic growth in Nigeria: a forecast error variance decomposition analysis." African J. of Economic and Sustainable Development 3, no. 2 (2014): 103. http://dx.doi.org/10.1504/ajesd.2014.064376.

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Zapata, Juan, and Juan Ciro. "The communication effects on inflation forecast errors: Empirical evidence from Colombia." Panoeconomicus, no. 00 (2020): 16. http://dx.doi.org/10.2298/pan180101016z.

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The purpose of this article is to explore the central bank's ability to management inflation forecast errors in Colombia. We present empirical evidence based on the Colombian experience with data from the period of 2008 to 2020. The communication channel selected for analysis is the press releases. The empirical evidence is divided into three steps: (i) regression analysis using an EGARCH model, (ii) use of VAR models, and (iii) variance decomposition analysis. The communications effects are significant for several months and that close to half of the forecast error variance can be explained b

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Lee, King Fuei. "An Empirical Study of Dividend Payout and Future Earnings in Singapore." Review of Pacific Basin Financial Markets and Policies 13, no. 02 (June 2010): 267–86. http://dx.doi.org/10.1142/s0219091510001949.

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The main purpose of this paper is to apply Johansen's vector error-correction model (VECM) to investigate the existence of the dividend signalling effect in the Singapore aggregate market through impulse response analysis, forecast error variance decomposition and Granger-causality test. Our findings show that a unit shock increase in dividend payout leads to a permanent increase in future earnings over time. These results imply that there exists informational/signalling content in dividend payout in the Singapore market over the long run. We further find that at least half of the forecast err

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Rena, Ravinder, and Albert V. Kamuinjo. "An Empirical Analysis of the Relationship Between Capital, Market Risks, and Liquidity Shocks in the Banking Industry." Studia Universitatis Babes-Bolyai Oeconomica 67, no. 2 (August 1, 2022): 67–83. http://dx.doi.org/10.2478/subboec-2022-0010.

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Abstract This study explores the relation between capital, market risks and banks’ liquidity conditions. In estimating the SVAR regression model, Granger causality, impulse-response functions and forecast error variance decomposition were employed and used for estimation of the results. The data sample comprised of commercial banks over the 2009 to 2018 period. The empirical results showed that liquidity shocks are caused by a combination of structural shocks. The Granger causality, impulse-response functions and forecast error variance decomposition documented that sensitivity to market risk

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Kamuinjo, Albert V., Ravinder Rena, and Andrew Maredza. "Impact of credit risk and profitability on liquidity shocks of Namibian banks: an application of the structural VAR model." Journal of Life Economics 8, no. 3 (July 31, 2021): 349–59. http://dx.doi.org/10.15637/jlecon.8.3.07.

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The main purpose of this paper was to investigate the relationship between banks’ credit risk and profitability and liquidity shocks in Namibia for the period 2009 to 2018 using the SVAR model. In estimating the SVAR regression model, granger causality, impulse-response functions and forecast error variance decomposition were employed and evaluated. The sample consisted of Namibian commercial banks. By auditing liquidity data between 2009 and 2018, empirical results showed that liquidity risk is caused by a combination of structural shocks. The granger causality, impulse-response functions and

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Smith, Kenneth L., Joe Brocato, and Russell E. Dabbs. "Professional forecast error as a function of a variable forecast horizon: A decomposition analysis." International Journal of Forecasting 7, no. 2 (August 1991): 155–63. http://dx.doi.org/10.1016/0169-2070(91)90050-6.

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Rozprawy doktorskie na temat "Forecast error variance decomposition"

Agbenyegah, Benjamin K. "An econometric approach to measuring productivity: Australia as a case study." Thesis, Curtin University, 2007. http://hdl.handle.net/20.500.11937/219.

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Seminal papers of Solow (1957) and Swan (1956) stimulated debate among economists on the role of technical change in productivity improvements and for that matter economic growth. The consensus is that technological change accounts for a significant proportion of gross national product (GNP) growth in industrialised economies. In the case of Australia, the aggregate productivity performance was poor in the 1970s and 1980s, but picked up very strongly by the 1990s, and was above the OECD average growth level for the first time in its productivity growth history. However, this high productivity

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Rafiq, Shuddhasattwa. "Oil consumption, pollutant emission, oil proce volatility and economic activities in selected Asian Developing Economies." Thesis, Curtin University, 2009. http://hdl.handle.net/20.500.11937/693.

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It is now well established in the literature that oil consumption, oil price shocks, and oil price volatility may impact the economic activities negatively. Studies identifying the relationship between energy and/or oil consumption and output primarily take two different approaches. One approach includes energy or oil consumption in addition to output, labour, and capital. The other approach takes energy and/or oil, output and prices. Based on these two models most of the previous studies suggest energy conservation policies for different economies. However, none of the previous studies consid

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Agbenyegah, Benjamin Komla. "An econometric approach to measuring productivity : Australia as a case study /." Curtin University of Technology, School of Economics and Finance, 2007. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=17375.

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Lanagan, Gareth Daniel Edward. "Weather forecast error decomposition using rearrangements of functions." Thesis, Aberystwyth University, 2012. http://hdl.handle.net/2160/b489892f-7607-4125-90fb-46d8376edf8f.

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This thesis applies rearrangement and optimal mass transfer theory to weather forecast error decomposition. Errors in weather forecasting are often due to displacement of key features; conventional error scores do not necessarily favour good forecasts, nor are they descriptive of how the forecast failed. We study forecast error decomposition, where error is split into an error due to displacement and an error due to differences in qualitative features. In its simple formulation, we seek re-arrangements of the forecast which are a best fit to the actual data, and then find the “least kinetic en

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Wolff, Laion. "RELAÇÃO ENTRE AS DEZ PRINCIPAIS BOLSAS DE VALORES DO MUNDO E SUAS CO-INTEGRAÇÕES." Universidade Federal de Santa Maria, 2011. http://repositorio.ufsm.br/handle/1/8207.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior<br>Globalization provoked in financial markets by means stock exchanges an interchange among the markets over the world. The aim of this study was to examine the relationship of the ten major main economic index of the world represented in New York (DJIA, S&P500 e Nasdaq), Tokyo (NIKKEI 225), London (FSTE 100), São Paulo (IBOV), Shanghai (SSE180), Paris (CAC-40), Frankfurt (DAX-30) and Buenos Aires (Merval) and looking for its co-integration, to demonstrate the behavior of these indexes and the long run equilibrium, from January of 20

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Singh, Shiu Raj. "Dynamics of macroeconomic variables in Fiji : a cointegrated VAR analysis." Diss., Lincoln University, 2008. http://hdl.handle.net/10182/774.

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Abstract of thesis submitted in partial fulfilment of the requirements for the Degree of Master of Commerce and Management Dynamics of macroeconomic variables in Fiji : a cointegrated VAR analysis By Shiu Raj Singh The objective of this study is to examine how macroeconomic variables of Fiji inter-relate with aggregate demand and co-determine one another using a vector autoregression (VAR) approach. This study did not use a prior theoretical framework but instead used economic justification for selection of variables. It was found that fiscal policy, which is generally used as a stabili

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Golab, Anna. "An investigation into the volatility and cointegration of emerging European stock markets." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2013. https://ro.ecu.edu.au/theses/572.

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This dissertation examines the interaction between European Emerging markets including cointegration, volatility, correlation and spillover effects. This study is also concerned with the process of the enlargement of the European Union and how this affects the emerging markets of newcomers. The twelve emerging markets studied are Bulgaria, the Czech Republic, Cyprus, Estonia, Hungry, Latvia, Lithuania, Malta, Poland, Romania, Slovakia and Slovenia, which are all progressing very rapidly in their reforms and domestic economic stability. The majority of prior studies on stock market comovements

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Akpan, Nkereuwem I. "The Impact of External Shocks on Nigeria’s GDP Performance within the Context of the Global Financial Crisis." Thesis, University of Bradford, 2018. http://hdl.handle.net/10454/17454.

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This research examines the impact of external shocks on Nigeria’s output performance for the period 1981 – 2015. It aims to bring to the fore the importance of considering external shocks during policy design and implementation. The multivariate VAR and VECM frameworks were used to evaluate the impact of the shock variables on Nigeria’s output performance and to achieve the stated objectives. Findings show that the external shock and domestic policy variables have short-run effects on Nigeria’s output performance. Also, all the measures of external shocks and domestic policies display some via

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Gonçalves, Daniel Fernandes. "Business cycle dynamics across Europe: a cluster analysis." Master's thesis, 2016. http://hdl.handle.net/10071/13216.

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JEL Classification: E32, E37<br>This dissertation aims to analyze the dynamics of business cycles across European countries between 1960Q1 and 2016Q1. For such purpose we identify country-groups of national deviation cycles through Hierarchical Agglomerative Clustering with the Ward’s method. The clustering technique suggests the existence of three country-groups, which include, aside from other countries, France and Spain in Cluster 1, United Kingdom and Denmark in Cluster 2 and Germany and Italy in Cluster 3. We execute an extensive analysis on business cycle stylized facts, synchroniz

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Xu, Jin. "Essays in Financial Econometric Investigations of Farmland Valuations." Thesis, 2013. http://hdl.handle.net/1969.1/150974.

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This dissertation consists of three essays wherein tools of financial econometrics are used to study the three aspects of farmland valuation puzzle: short-term boom-bust cycles, overpricing of farmland, and inconclusive effects of direct government payments. Essay I addresses the causes of unexplained short-term boom-bust cycles in farmland values in a dynamic land pricing model (DLPM). The analysis finds that gross return rate of farmland asset decreases as the farmland asset level increases, and that the diminishing return function of farmland asset contributes to the boom-bust cycles in fa

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Części książek na temat "Forecast error variance decomposition"

Amisano, Gianni, and Carlo Giannini. "Impulse response analysis and forecast error variance decomposition in SVAR modelling." In Topics in Structural VAR Econometrics, 60–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-60623-6_5.

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Giannini, Carlo. "Impulse Response Analysis and Forecast Error Variance Decomposition in SVAR Modeling." In Lecture Notes in Economics and Mathematical Systems, 44–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-662-02757-8_5.

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Agapitos, Alexandros, Anthony Brabazon, and Michael O’Neill. "Controlling Overfitting in Symbolic Regression Based on a Bias/Variance Error Decomposition." In Lecture Notes in Computer Science, 438–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32937-1_44.

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Peter Eze, Gbalam, and Tonprebofa Waikumo Okotori. "Exchange Rate Volatility and Monetary Policy Shocks." In Macroeconomics [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.99606.

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The study investigated the influence of innovations in monetary policy on the rate of exchange volatility in Nigeria. The research adopted vector error correction model as well as impulse response function and forecast error variance decomposition function in the estimation using two models derived in the study. Monthly data between the periods 2009 and 2019 were adopted for the research. Our findings show that in the long run; all the monetary policy variables have a significant long run correlation with volatility in the exchange rate; but that money supply and the rate of exchange seem to have significant short run impact on volatility in the exchange rate, the other variables such as liquidity ratio or monetary policy rate did not show a significant short run relationship with the volatility in the exchange rate. Further findings on the volatility impulse response and the forecast error variance decomposition suggest a significant link between volatility in the exchange rate and money supply though the link was much more pronounced. The use of monthly data shows that the managed exchange rate regime by the CBN seems to have the desired effect in exchange rate volatility and thus having a critical impact on inflationary spikes.

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Gylych, Jelilov, Abdullahi Ahmad Jibrin, Bilal Celik, and Abdurrahman Isik. "Impact of Oil Price Fluctuation on the Economy of Nigeria, the Core Analysis for Energy Producing Countries." In Energy Management Systems in Process Industries - Current Practice and Challenges in Era of Industry 4.0 [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.94055.

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The study aims to find the short-run empirical analyses of the impact of oil price fluctuation on the monetary instrument (Exchange rate, Inflation, Interest rate) in Nigeria. We explored the frequently used Toda–Yamamoto model (TY) model, by adopting the TY Modified Wald (MWALD) test approach to causality, Forecast Error Variance Decomposition (FEVD) and Impulse Response Functions (IRFs).The study covered the period 1995 to 2018 (monthly basis), and our findings from MWALD test indicated that there is a uni-directional causality of the log of oil price (lnoilpr) to log of the exchange rate (lnexchr) at 10% level of significance, also there is a contemporaneous response of log of consumer price index (lncpi) to log of exchange rate (lnexchr) and log of interest rate (lnintr), and jointly (lnoilpr, lncpi and lnintr) granger cause lncpi. Also at 5% level of significance lnintr responded due to positive change in lnoilpr and lnexchr, and jointly causes lnintr at 5% level of significance. This is complimented with our findings in FEVDs, and IRFs. The empirical analyses shows that oil price is a strong determining factor of exchange rate, cost of borrowing and directly influences inflationary or deflationary tendencies in Nigeria..

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Ozer, Mustafa, and A. Erinç Yeldan. "The Relationship between Current Account Deficits and Unemployment in Turkey." In Handbook of Research on Comparative Economic Development Perspectives on Europe and the MENA Region, 492–510. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9548-1.ch020.

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In this chapter, we test the nature of the variety of empirical relationships between current account deficits and unemployment in Turkey over 2000Q1–2012Q1. Our working hypothesis in this paper is that the meager job creation in Turkey over 2000s is the direct symptom of a speculative-led growth environment (Grabel, 1995) together with an excessively open and unregulated capital account in the age of relatively cheap and abundant global finance. Based on the vector error correction model (VECM), we found that there is a unidirectional causality running from current account deficits to unemployment. Both Impulse Response and Variance Decomposition analyses are quite consistent with results of VECM. We interpret these findings as evidence of the structural characteristics of unemployment, reflected in output elasticities, being embedded under the deepening external fragility of the Turkish economy over the 2000s.

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Polyak, Ilya. "Variability of ARMA Processes." In Computational Statistics in Climatology. Oxford University Press, 1996. http://dx.doi.org/10.1093/oso/9780195099997.003.0006.

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In this chapter, the numerical and pictorial interpretation of the dependence of the standard deviation of the forecast error for the different types and orders of univariate autoregressive-moving average (ARMA) processes on the lead time and on the autocorrelations (in the domains of the permissible autocorrelations) are given. While the convenience of fitting a stochastic model enables us to estimate its accuracy for the only time series under consideration, the graphs in this chapter demonstrate such accuracy for all possible models of the first and second order. Such a study can help in evaluating the appropriateness of the presupposed model, in earring out the model identification procedure, in designing an experiment, and in optimally organizing computations (or electing not to do so). A priori knowledge of the theoretical values of a forecast’s accuracy indicates the reasonable limits of complicating the model and facilitates evaluation of the consequences of certain preliminary decisions concerning its application. The approach applied is similar to the methodology developed in Chapters 1 and 2. Because the linear process theory has been thoroughly discussed in the statistical literature (see, for example, Box and Jenkins, 1976; Kashyap and Rao, 1976; and so on), its principal concepts are presented in recipe form with the minimum of details necessary for understanding the computational aspects of the subject. Consider a discrete stationary random process zt with null expected value [E(zt) = 0] and autocovariance function . . . M(T) = σ2 ρ(T), (4.1) . . . where σ2 is the variance and ρ(T) is the autocorrelation function of zt. Let at be a discrete white noise process with a zero mean and a variance σ2a. Let us assume that processes zt and at are normally distributed and that their cross-covariance function Mza(T) = 0 if T > 0.

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Duell, Peter, and Xin Yao. "Implementing Negative Correlation Learning in Evolutionary Ensembles with Suitable Speciation Techniques." In Pattern Recognition Technologies and Applications, 344–69. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-807-9.ch016.

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Negative correlation learning (NCL) is a technique that attempts to create an ensemble of neural networks whose outputs are accurate but negatively correlated. The motivation for such a technique can be found in the bias-variance-covariance decomposition of an ensemble of learner’s generalization error. NCL is also increasingly used in conjunction with an evolutionary process, which gives rise to the possibility of adapting the structures of the networks at the same time as learning the weights. This chapter examines the motivation and characteristics of the NCL algorithm. Some recent work relating to the implementation of NCL in a single objective evolutionary framework for classification tasks is presented, and we examine the impact of two speciation techniques: implicit fitness sharing and an island model population structure. The choice of such speciation techniques can have a detrimental effect on the ability of NCL to produce accurate and diverse ensembles and should therefore be chosen carefully. This chapter also provides an overview of other researchers’ work with NCL and gives some promising future research directions.

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Pang, Kwok Pan. "Time Series Analysis and Structural Change Detection." In Dynamic and Advanced Data Mining for Progressing Technological Development, 377–95. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-908-3.ch015.

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Most research on time series analysis and forecasting is normally based on the assumption of no structural change, which implies that the mean and the variance of the parameter in the time series model are constant over time. However, when structural change occurs in the data, the time series analysis methods based on the assumption of no structural change will no longer be appropriate; and thus there emerges another approach to solving the problem of structural change. Almost all time series analysis or forecasting methods always assume that the structure is consistent and stable over time, and all available data will be used for the time series prediction and analysis. When any structural change occurs in the middle of time series data, any analysis result and forecasting drawn from full data set will be misleading. Structural change is quite common in the real world. In the study of a very large set of macroeconomic time series that represent the ‘fundamentals’ of the US economy, Stock and Watson (1996) has found evidence of structural instability in the majority of the series. Besides, ignoring structural change reduces the prediction accuracy. Persaran and Timmermann (2003), Hansen (2001) and Clement and Hendry (1998, 1999) showed that structural change is pervasive in time series data, ignoring structural breaks which often occur in time series significantly reduces the accuracy of the forecast, and results in misleading or wrong conclusions. This chapter mainly focuses on introducing the most common time series methods. The author highlights the problems when applying to most real situations with structural changes, briefly introduce some existing structural change methods, and demonstrate how to apply structural change detection in time series decomposition.

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Li, Peilin, Sang-Heon Lee, and Hung-Yao Hsu. "Use of Bi-Camera and Fusion of Pairwise Real Time Citrus Fruit Image for Classification Application." In Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies, 54–81. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-6030-4.ch004.

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In this chapter, the use of two images, the near infrared image and the color image, from a bi-camera machine vision system is investigated to improve the detection of the citrus fruits in the image. The application has covered the design of the bi-camera vision system to align two CCD cameras, the online acquisition of the citrus fruit tree image, and the fusion of two aligned images. In the system, two cameras have been registered with alignment to ensure the fusion of two images. A fusion method has been developed based on the Multiscale Decomposition Analysis (MSD) with a Discrete Wavelet Transform (DWT) application for the two dimensional signal. In the fusion process, two image quality issues have been addressed. One is the detail noise from the background, which is bounded with the envelope spectra and with similar spectra to orange citrus fruit and spatial variance property. The second is the enhancement of the fundamental envelope spectra using two source images. With level of MSD estimated, the noise is reduced by zeroing the high pass coefficients in DWT while the fundamental envelope spectra from the color image are enhanced by an arithmetic pixel level fusion rule. To evaluate the significant improvement of the image quality, some major classification methods are applied to compare the classified results from the fused image with the results from the types of color image. The misclassification error is measured by the empirical type errors using the manual segmentation reference image.

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Streszczenia konferencji na temat "Forecast error variance decomposition"

Charaeva, Marina V., Marina A. Kuznetsova, and Song Yansong. "The impact of commodity market volatility on China's stock market." In Sustainable and Innovative Development in the Global Digital Age. Dela Press Publishing House, 2022. http://dx.doi.org/10.56199/dpcsebm.zmib9194.

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The article examines individual industry data series on the Chinese stock market and international commodity markets based on the application of the method of decomposition of generalized variance of forecast errors to build a secondary volatility index and overflow network. The DCC-GARCH model proposed by the author is used to study the effect of hedging wholesale goods on the Chinese stock market. The results show that in every industry in China, industry and consumer industry are the main risk-taking market, and the energy industry and financial industry are the main export risk market.

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De Giorgi, Maria Grazia, Marco Tarantino, and Antonio Ficarella. "A New Hybrid Method for Wind Power Forecasting Based on Wavelet Decomposition and Artificial Neural Networks." In ASME 2011 Turbo Expo: Turbine Technical Conference and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/gt2011-46382.

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Depending on their input, wind power forecasting models are classified as physical or statistical approaches or a combination of both. Physical models use physical considerations, as meteorological information (Numerical Weather Prediction) and technical characteristics of the wind turbines (hub height, power curve, thrust coefficient). Statistical models use explanatory variables and online measurements, usually employing recursive techniques, like recursive least squares or artificial neural networks (ANNs) which perform a non-linear mapping and provide a robust approach for wind prediction.

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Silva, Ramon Gomes, Matheus Henrique Dal Molin Ribeiro, José Henrique Kleinubing Larcher, Viviana Cocco Mariani, and Leandro dos Santos Coelho. "Artificial Intelligence and Signal Decomposition Approach Applied to Retail Sales Forecasting." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2021. http://dx.doi.org/10.21528/cbic2021-25.

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Sales forecasting is essential for decision-making and are crucial in many areas of a firm, such as planning and scheduling, resource management, marketing, logistics, and supply chain. Due to the fluctuations in retail sales, prediction with high accuracy is a challenging task. In this context, this study proposes a framework that combines ensemble empirical mode decomposition (EEMD) based on artificial intelligence models to forecast the retail sales of a Rossmann Store, using a multi-step-ahead forecasting strategy, in the task of time series forecasting with one, seven, and fourteen-days-a

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Raporty organizacyjne na temat "Forecast error variance decomposition"

Clark, Todd E., Gergely Ganics, and Elmar Mertens. Constructing fan charts from the ragged edge of SPF forecasts. Federal Reserve Bank of Cleveland, November 2022. http://dx.doi.org/10.26509/frbc-wp-202236.

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We develop a model that permits the estimation of a term structure of both expectations and forecast uncertainty for application to professional forecasts such as the Survey of Professional Forecasters (SPF). Our approach exactly replicates a given data set of predictions from the SPF (or a similar forecast source) without measurement error. Our model captures fixed horizon and fixed-event forecasts, and can accommodate changes in the maximal forecast horizon available from the SPF. The model casts a decomposition of multi-period forecast errors into a sequence of forecast updates that may be

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Álvarez Florens Odendahl, Luis J., and Germán López-Espinosa. Data outliers and Bayesian VARs in the euro area. Madrid: Banco de España, November 2022. http://dx.doi.org/10.53479/23552.

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We propose a method to adjust for data outliers in Bayesian Vector Autoregressions (BVARs), which allows for different outlier magnitudes across variables and rescales the reduced form error terms. We use the method to document several facts about the effect of outliers on estimation and out-of-sample forecasting results using euro area macroeconomic data. First, the COVID-19 pandemic led to large swings in macroeconomic data that distort the BVAR estimation results. Second, these swings can be addressed by rescaling the shocks’ variance. Third, taking into account outliers before 2020 leads t

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