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Relevant bibliographies by topics / Mixed frequency time series

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Academic literature on the topic 'Mixed frequency time series'

Author: Grafiati

Published: 10 December 2022

Last updated: 19 July 2025

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Journal articles on the topic "Mixed frequency time series"

1

Götz, Thomas B., Alain Hecq, and Jean-Pierre Urbain. "Forecasting Mixed-Frequency Time Series with ECM-MIDAS Models." Journal of Forecasting 33, no. 3 (2014): 198–213. http://dx.doi.org/10.1002/for.2286.

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Dou, Jiaxin, Yaling Xun, Haifeng Yang, Jianghui Cai, Yanfeng Li, and Shuo Han. "Multivariate time series forecasting based on time–frequency transform mixed convolution." Knowledge-Based Systems 325 (September 2025): 113912. https://doi.org/10.1016/j.knosys.2025.113912.

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Ghysels, Eric, and J. Isaac Miller. "Testing for Cointegration with Temporally Aggregated and Mixed-Frequency Time Series." Journal of Time Series Analysis 36, no. 6 (2015): 797–816. http://dx.doi.org/10.1111/jtsa.12129.

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Tank, A., E. B. Fox, and A. Shojaie. "Identifiability and estimation of structural vector autoregressive models for subsampled and mixed-frequency time series." Biometrika 106, no. 2 (2019): 433–52. http://dx.doi.org/10.1093/biomet/asz007.

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Summary Causal inference in multivariate time series is challenging because the sampling rate may not be as fast as the time scale of the causal interactions, so the observed series is a subsampled version of the desired series. Furthermore, series may be observed at different sampling rates, yielding mixed-frequency series. To determine instantaneous and lagged effects between series at the causal scale, we take a model-based approach that relies on structural vector autoregressive models. We present a unifying framework for parameter identifiability and estimation under subsampling and mixed

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Miller, J. Isaac. "Conditionally Efficient Estimation of Long-Run Relationships Using Mixed-Frequency Time Series." Econometric Reviews 35, no. 6 (2014): 1142–71. http://dx.doi.org/10.1080/07474938.2014.976527.

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Seong, Byeongchan. "Smoothing and forecasting mixed-frequency time series with vector exponential smoothing models." Economic Modelling 91 (September 2020): 463–68. http://dx.doi.org/10.1016/j.econmod.2020.06.020.

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Götz, Thomas B., and Klemens Hauzenberger. "Large mixed-frequency VARs with a parsimonious time-varying parameter structure." Econometrics Journal 24, no. 3 (2021): 442–61. http://dx.doi.org/10.1093/ectj/utab001.

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Summary In order to simultaneously consider mixed-frequency time series, their joint dynamics, and possible structural change, we introduce a time-varying parameter mixed-frequency vector autoregression (VAR). Time variation enters in a parsimonious way: only the intercepts and a common factor in the error variances can vary. Computational complexity therefore remains in a range that still allows us to estimate moderately large VARs in a reasonable amount of time. This makes our model an appealing addition to any suite of forecasting models. For eleven U.S. variables, we show the competitivene

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Grebenkina, A. M., and E. V. Sinelnikova-Muryleva. "Inflation Forecasting in Time Series Models Using High Frequency Data." Economic Policy 20, no. 2 (2025): 34–55. https://doi.org/10.18288/1994-5124-2025-2-34-55.

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The article examines ways to improve inflation forecasting by using high frequency consumer price data in time series models. The purpose of increasing the number of observations available at a higher frequency is to increase the accuracy of inflation forecasts. The theoretical part of the paper considers the advantages and disadvantages of using high frequency price data in ADL, VAR and MIDAS inflation models with both single and mixed data frequency. The empirical section traces out the effects of including an online price index available at a daily or weekly frequency during the period from

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Belovas, Igoris, Leonidas Sakalauskas, Vadimas Starikovičius, and Edward W. Sun. "Mixed-Stable Models: An Application to High-Frequency Financial Data." Entropy 23, no. 6 (2021): 739. http://dx.doi.org/10.3390/e23060739.

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The paper extends the study of applying the mixed-stable models to the analysis of large sets of high-frequency financial data. The empirical data under review are the German DAX stock index yearly log-returns series. Mixed-stable models for 29 DAX companies are constructed employing efficient parallel algorithms for the processing of long-term data series. The adequacy of the modeling is verified with the empirical characteristic function goodness-of-fit test. We propose the smart-Δ method for the calculation of the α-stable probability density function. We study the impact of the accuracy of

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Khiabani, Naser, and Fateme Rajabi. "Modeling with Mixed Frequency Variables: A Review of Recently Extended Methods in Time Series Econometrics." Journal of Planning and Budgeting 24, no. 2 (2019): 3–30. http://dx.doi.org/10.29252/jpbud.24.2.3.

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Dissertations / Theses on the topic "Mixed frequency time series"

1

Wohlrabe, Klaus. "Forecasting with mixed-frequency time series models." Diss., lmu, 2009. http://nbn-resolving.de/urn:nbn:de:bvb:19-96817.

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Marsilli, Clément. "Mixed-Frequency Modeling and Economic Forecasting." Thesis, Besançon, 2014. http://www.theses.fr/2014BESA2023/document.

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La prévision macroéconomique à court terme est un exercice aussi complexe qu’essentiel pour la définition de la politique économique et monétaire. Les crises financières récentes ainsi que les récessions qu’ont endurées et qu’endurent aujourd’hui encore, en ce début d’année 2014, nombre de pays parmi les plus riches, témoignent de la difficulté d’anticiper les fluctuations économiques, même à des horizons proches. Les recherches effectuées dans le cadre de la thèse de doctorat qui est présentée dans ce manuscrit se sont attachées à étudier, analyser et développer des modélisations pour la prév

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Pacce, Matías José. "Essays on Business Cycles Fluctuations and Forecasting Methods." Doctoral thesis, Universidad de Alicante, 2017. http://hdl.handle.net/10045/71346.

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This doctoral dissertation proposes methodologies which, from a linear or a non-linear approach, accommodate to the information flow and can deal with a large amount of data. The empirical application of the proposed methodologies contributes to answer some of the questions that have emerged or that it has potentiated after the 2008 global crisis. Thus, essential aspects of the macroeconomic analysis are studied, like the identification and forecast of business cycles turning points, the business cycles interactions between countries or the development of tools able to forecast the evolution o

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Elayouty, Amira Sherif Mohamed. "Time and frequency domain statistical methods for high-frequency time series." Thesis, University of Glasgow, 2017. http://theses.gla.ac.uk/8061/.

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Advances in sensor technology enable environmental monitoring programmes to record and store measurements at high-temporal resolution over long time periods. These large volumes of high-frequency data promote an increasingly comprehensive picture of many environmental processes that would not have been accessible in the past with monthly, fortnightly or even daily sampling. However, benefiting from these increasing amounts of high-frequency data presents various challenges in terms of data processing and statistical modeling using standard methods and software tools. These challenges are attri

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Lundbergh, Stefan. "Modelling economic high-frequency time series." Doctoral thesis, Handelshögskolan i Stockholm, Ekonomisk Statistik (ES), 1999. http://urn.kb.se/resolve?urn=urn:nbn:se:hhs:diva-637.

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Mui, Chi Seong. "Frequency domain approach to time series analysis." Thesis, University of Macau, 2000. http://umaclib3.umac.mo/record=b1446676.

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Papagni, Francesca <1993&gt. "Frequency domain analysis of stationary time series." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amsdottorato.unibo.it/9850/1/Papagni_Francesca_tesi.pdf.

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This thesis provides a necessary and sufficient condition for asymptotic efficiency of a nonparametric estimator of the generalised autocovariance function of a Gaussian stationary random process. The generalised autocovariance function is the inverse Fourier transform of a power transformation of the spectral density, and encompasses the traditional and inverse autocovariance functions. Its nonparametric estimator is based on the inverse discrete Fourier transform of the same power transformation of the pooled periodogram. The general result is then applied to the class of Gaussian stationar

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Terwilleger, Erin. "Multidimensional time-frequency analysis /." free to MU campus, to others for purchase, 2002. http://wwwlib.umi.com/cr/mo/fullcit?p3052223.

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Erkan, Ibrahim. "Mixed Effects Models For Time Series Gene Expression Data." Phd thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613913/index.pdf.

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The experimental factors such as the cell type and the treatment may have different impact on expression levels of individual genes which are quantitative measurements from microarrays. The measurements can be collected at a few unevenly spaced time points with replicates. The aim of this study is to consider cell type, treatment and short time series attributes and to infer about their effects on individual genes. A mixed effects model (LME) was proposed to model the gene expression data and the performance of the model was validated by a simulation study. Realistic data sets were generated p

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Åsbrink, Stefan E. "Nonlinearities and regime shifts in financial time series /." Stockholm : Economic Research Institute, Stockholm School of Economics [Ekonomiska forskningsinstitutet vid Handelshögsk.] (EFI), 1997. http://www.hhs.se/efi/summary/439.htm.

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Books on the topic "Mixed frequency time series"

1

Cohen, Leon. Time-frequency analysis. Prentice Hall PTR, 1995.

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Flandrin, Patrick. Time-frequency/time scale analysis. Academic Press, 1999.

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Tolimieri, Richard. Time-frequency representations. Birkhauser Boston, 1997.

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Qian, Shie. Joint time-frequency analysis: Methods and applications. PTR Prentice Hall, 1996.

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Christian, Dunis, and Zhou Bin 1956-, eds. Nonlinear modelling of high frequency financial time series. Wiley, 1998.

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1940-, Cohen Leon, and Loughlin Patrick, eds. Recent developments in time-frequency analysis. Kluwer Academic Publishers, 1998.

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Privalʹskiĭ, V. E. Time series analysis package: Autoregressive time and frequency domains analysis of scalar and multi-variate time series. Utah Climate Center, Utah State University, 1993.

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8

Mueller, Uli. Testing models of low-frequency variability. National Bureau of Economic Research, 2006.

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9

Gröchenig, Karlheinz. Foundation of time-frequency analysis: With 15 figures. Birkhäuser, 2001.

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Qian, Shie. Introduction to time-frequency and wavelet transforms. Prentice Hall PTR, 2002.

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Book chapters on the topic "Mixed frequency time series"

1

Kundert, Kenneth S., Jacob K. White, and Alberto Sangiovanni-Vincentelli. "Mixed Frequency-Time Method." In Steady-State Methods for Simulating Analog and Microwave Circuits. Springer US, 1990. http://dx.doi.org/10.1007/978-1-4757-2081-5_7.

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Prado, Raquel, Marco A. R. Ferreira, and Mike West. "The frequency domain." In Time Series, 2nd ed. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781351259422-3.

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Hlawatsch, Franz. "Time-Frequency Filters and Time-Frequency Expansions." In The Kluwer International Series in Engineering and Computer Science. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-2815-6_5.

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Ramachandra Rao, A., Khaled H. Hamed, and Huey-Long Chen. "Time-Frequency Analysis." In Nonstationarities in Hydrologic and Environmental Time Series. Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-010-0117-5_5.

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Riley, Michael D. "Time-frequency filtering." In The Kluwer International Series in Engineering and Computer Science. Springer US, 1989. http://dx.doi.org/10.1007/978-1-4613-1079-2_3.

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Woodward, Wayne A., Bivin P. Sadler, and Stephen D. Robertson. "The Frequency Domain." In Time Series for Data Science. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003089070-4.

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de Gosson, Maurice. "On the Purity and Entropy of Mixed Gaussian States." In Landscapes of Time-Frequency Analysis. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05210-2_5.

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Hirschboeck, Katherine K. "Hydroclimatically-Defined Mixed Distributions in Partial Duration Flood Series." In Hydrologic Frequency Modeling. Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3953-0_13.

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Lindsey, James K. "Time Series: The Frequency Domain." In The Analysis of Stochastic Processes using GLIM. Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2888-2_7.

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Christensen, Ronald. "Frequency Analysis of Time Series." In Springer Texts in Statistics. Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3847-6_4.

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Conference papers on the topic "Mixed frequency time series"

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Khayati, Armin, Mohammad Taheri, and Koorush Ziarati. "Real-Time Forecasting Using Mixed Frequency Time-Series Data." In 2024 14th International Conference on Computer and Knowledge Engineering (ICCKE). IEEE, 2024. https://doi.org/10.1109/iccke65377.2024.10874478.

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Huo, Weigang, Yuting Wang, and Lishou Ye. "A Mixed Augmentations Method for Multivariate Time Series Contrastive Learning." In 2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA). IEEE, 2024. https://doi.org/10.1109/ispa63168.2024.00243.

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Yuan, Anni, Chunming Zou, Yong Wang, and Jinming Hu. "Multivariate Time Series Anomaly Detection Based on Time-Frequency Dynamic Analysis." In 2024 13th International Conference on Communications, Circuits and Systems (ICCCAS). IEEE, 2024. http://dx.doi.org/10.1109/icccas62034.2024.10652754.

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Hao, Yanwei, Zhefeng Hu, Zihao Li, et al. "Coherent dual optical frequency combs generation system based on series-parallel mixed modulators." In 6th International Conference on Optoelectronic Materials and Devices (ICOMD24), edited by Tingchao He and Ching Yern Chee. SPIE, 2025. https://doi.org/10.1117/12.3058936.

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Liu, Yun, and Jinhong Li. "FAformer: Frequency Analysis Transformer for Multivariate Time Series Forecasting." In 2024 IEEE 7th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2024. http://dx.doi.org/10.1109/itnec60942.2024.10733231.

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Zhang, Jihao, Zhijiang Wang, and Chunna Zhao. "MSFI: Multi-Scale Frequency Interpolation for Time Series Forecasting." In 2024 12th International Conference on Information Systems and Computing Technology (ISCTech). IEEE, 2024. https://doi.org/10.1109/isctech63666.2024.10845556.

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Luo, Yu, and Xiang Xu. "Time-Frequency Analysis for Non-Stationary Time Series Based on Adaptive Window." In 2024 International Conference on Information Technology, Communication Ecosystem and Management (ITCEM). IEEE, 2024. https://doi.org/10.1109/itcem65710.2024.00033.

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Qiu, Xiaohong, and Zhuofeng Sun. "TFGAN: A time-series Generation Adversarial Networks based on Time-Frequency Consistency." In 2025 5th International Symposium on Computer Technology and Information Science (ISCTIS). IEEE, 2025. https://doi.org/10.1109/isctis65944.2025.11065489.

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Crespillo, Omar García, Steven Langel, and Mathieu Joerger. "Frequency Domain Overbounding with Multiple Time Series and PSD Estimators." In 37th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2024). Institute of Navigation, 2024. http://dx.doi.org/10.33012/2024.19704.

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Chen, Haonan, Hongzuo Xu, Songlei Jian, Ruyi Zhang, Xingming Li, and Zibo Yi. "Frequency-enhanced Comprehensive Dependency Attention for Time Series Anomaly Detection." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10888204.

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Reports on the topic "Mixed frequency time series"

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Koop, Gary, Stuart McIntyre, James Mitchell, Aubrey Poon, and Ping Wu. Incorporating short data into large mixed-frequency VARs for regional nowcasting. Federal Reserve Bank of Cleveland, 2023. http://dx.doi.org/10.26509/frbc-wp-202309.

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Interest in regional economic issues coupled with advances in administrative data is driving the creation of new regional economic data. Many of these data series could be useful for nowcasting regional economic activity, but they suffer from a short (albeit constantly expanding) time series which makes incorporating them into nowcasting models problematic. Regional nowcasting is already challenging because the release delay on regional data tends to be greater than that at the national level, and "short" data imply a "ragged edge" at both the beginning and the end of regional data sets, which

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Diakonova, Marina, Corinna Ghirelli, Luis Molina, and Javier J. Pérez. The economic impact of conflict-related and policy uncertainty shocks: the case of Russia. Banco de España, 2022. http://dx.doi.org/10.53479/23707.

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We show how policy uncertainty and conflict-related shocks impact the dynamics of economic activity (GDP) in Russia. We use alternative indicators of “conflict”, relating to specific aspects of this general concept: geopolitical risk, social unrest, outbreaks of political violence and escalations into internal armed conflict. For policy uncertainty we employ the workhorse economic policy uncertainty (EPU) indicator. We use two distinct but complementary empirical approaches. The first is based on a time series mixed-frequency forecasting model. We show that the indicators provide useful inform

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Schorfheide, Frank, and Dongho Song. Real-Time Forecasting with a Mixed-Frequency VAR. National Bureau of Economic Research, 2013. http://dx.doi.org/10.3386/w19712.

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Rosen, Michael, C. Matthew Stewart, Hadi Kharrazi, et al. Potential Harms Resulting From Patient-Clinician Real-Time Clinical Encounters Using Video-based Telehealth: A Rapid Evidence Review. Agency for Healthcare Research and Quality (AHRQ), 2023. http://dx.doi.org/10.23970/ahrqepc_mhs4telehealth.

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Objectives. To review the evidence on harms associated with patient-clinician real time encounters using video-based telehealth and determine the effectiveness of any related patient safety practices (PSPs). PSPs are interventions, strategies, or approaches intended to prevent or mitigate unintended consequences of healthcare delivery and improve patient safety. This review provides information that clinicians and health system leaders need to determine how to minimize harms from increasing real-time use of telehealth. Methods. We followed rapid review processes of the Agency for Healthcare Re

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Schorfheide, Frank, and Dongho Song. Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic. National Bureau of Economic Research, 2021. http://dx.doi.org/10.3386/w29535.

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McCracken, Michael W., Michael T. Owyang, and Tatevik Sekhposyan. Real-Time Forecasting and Scenario Analysis using a Large Mixed-Frequency Bayesian VAR. Federal Reserve Bank of St. Louis, 2015. http://dx.doi.org/10.20955/wp.2015.030.

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Chiu, Shean-Tsong. Weighted Least Squares Estimators on the Frequency Domain for the Parameters of a Time Series. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada455201.

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Соловйов, Володимир Миколайович, V. Saptsin, and D. Chabanenko. Markov chains applications to the financial-economic time series predictions. Transport and Telecommunication Institute, 2011. http://dx.doi.org/10.31812/0564/1189.

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In this research the technology of complex Markov chains is applied to predict financial time series. The main distinction of complex or high-order Markov Chains and simple first-order ones is the existing of after-effect or memory. The technology proposes prediction with the hierarchy of time discretization intervals and splicing procedure for the prediction results at the different frequency levels to the single prediction output time series. The hierarchy of time discretizations gives a possibility to use fractal properties of the given time series to make prediction on the different freque

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Rebucci, Alessandro, Joong Shik Kang, and Alessandro Prati. Aid, Exports, and Growth: A Time-Series Perspective on the Dutch Disease Hypothesis. Inter-American Development Bank, 2010. http://dx.doi.org/10.18235/0010989.

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The available evidence on the effects of aid on growth is notoriously mixed. We use a novel empirical methodology, a heterogeneous panel vector-autoregression model identified through factor analysis, to study the dynamic response of exports, imports, and per capita GDP growth to a "global" aid shock (the common component of individual country aid-to-GDP ratios). We find that the estimated cumulative resposive of exports and per capita GDP growth to a global aid shock are strongly positively correlated, and both responses are inversely related to exchange rate overvaluation measures. We interp

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Kindt, Roeland, Ian K Dawson, Jens-Peter B Lillesø, Alice Muchugi, Fabio Pedercini, and James M Roshetko. The one hundred tree species prioritized for planting in the tropics and subtropics as indicated by database mining. World Agroforestry, 2021. http://dx.doi.org/10.5716/wp21001.pdf.

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A systematic approach to tree planting and management globally is hindered by the limited synthesis of information sources on tree uses and species priorities. To help address this, the authors ‘mined’ information from 23 online global and regional databases to assemble a list of the most frequent tree species deemed useful for planting according to database mentions, with a focus on tropical regions. Using a simple vote count approach for ranking species, we obtained a shortlist of 100 trees mentioned in at least 10 of our data sources (the ‘top-100’ species). A longer list of 830 trees that

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