Time Series Analysis.

Untitled work

(2001) · RePEc: Research Papers in Economics · Cited by 27,577

Testing for unit roots in heterogeneous panels

Im, K. S., Pesaran, M. H., & Shin, Y. (2003) · Journal of Econometrics · doi:10.1016/s0304-4076(03)00092-7 · Cited by 15,435

A simple panel unit root test in the presence of cross‐section dependence

Pesaran, M. H. (2007) · Journal of Applied Econometrics · doi:10.1002/jae.951 · Cited by 12,787

Consistent Covariance Matrix Estimation with Spatially Dependent Panel Data

Driscoll, J. C., & Kraay, A. C. (1998) · Review of Economics and Statistics · doi:10.1162/003465398557825 · Cited by 6,865

Estimation and Inference of Impulse Responses by Local Projections

Jordà, Ò. (2005) · American Economic Review · doi:10.1257/0002828053828518 · Cited by 4,677

A critique of the cross-lagged panel model

Hamaker, E. L., Kuiper, R. M., & Grasman, R. P. P. P. (2015) · Psychological Methods · doi:10.1037/a0038889 · Cited by 4,005

The determinants and implications of corporate cash holdings

Opler, T. (1999) · Journal of Financial Economics · doi:10.1016/s0304-405x(99)00003-3 · Cited by 3,914

Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework

Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014) · Festschrift in Honor of Peter Schmidt · doi:10.1007/978-1-4899-8008-3_9 · Cited by 3,835

Segmented regression analysis of interrupted time series studies in medication use research

Wagner, A. K., Soumerai, S. B., Zhang, F., & Ross-Degnan, D. (2002) · Journal of Clinical Pharmacy and Therapeutics · doi:10.1046/j.1365-2710.2002.00430.x · Cited by 3,279

Is All That Talk Just Noise? The Information Content of Internet Stock Message Boards

Antweiler, W., & Frank, M. Z. (2004) · The Journal of Finance · doi:10.1111/j.1540-6261.2004.00662.x · Cited by 2,373

Measuring the macroeconomic impact of monetary policy at the zero lower bound

WU, J. C., & XIA, F. D. (2016) · Journal of Money, Credit and Banking · doi:10.1111/jmcb.12300 · Cited by 1,842

Consciousness and Complexity

Tononi, G. (1998) · Science · doi:10.1126/science.282.5395.1846 · Cited by 1,561

What to do about missing values in time series cross- section data

Honaker, J., & King, G. (2010) · American Journal of Political Science · doi:10.1111/j.1540-5907.2010.00447.x · Cited by 966

THE ESTIMATION OF CAUSAL EFFECTS FROM OBSERVATIONAL DATA

Winship, C., & Morgan, S. L. (1999) · Annual Review of Sociology · doi:10.1146/annurev.soc.25.1.659 · Cited by 890

Modeling Dynamics in Time-Series–Cross-Section Political Economy Data

Beck, N., & Katz, J. N. (2011) · Annual Review of Political Science · doi:10.1146/annurev-polisci-071510-103222 · Cited by 825

“Did you have a nice evening?” A day-level study on recovery experiences, sleep, and affect

Sonnentag, S., Binnewies, C., & Mojza, E. J. (2008) · Journal of Applied Psychology · doi:10.1037/0021-9010.93.3.674 · Cited by 741

Interpreting Neuronal Population Activity by Reconstruction: Unified Framework With Application to Hippocampal Place Cells

Zhang, K., Ginzburg, I., McNaughton, B. L., & Sejnowski, T. J. (1998) · Journal of Neurophysiology · doi:10.1152/jn.1998.79.2.1017 · Cited by 741

Single-case experimental designs: a systematic review of published research and current standards

Smith, J. D. (2012) · Psychological Methods · doi:10.1037/a0029312 · Cited by 630

Rising food prices, food price volatility, and social unrest

Bellemare, M. F. (2015) · American Journal of Agricultural Economics · doi:10.1093/ajae/aau038 · Cited by 636

At the Frontiers of Modeling Intensive Longitudinal Data: Dynamic Structural Equation Models for the Affective Measurements from the COGITO Study

Hamaker, E. L., Asparouhov, T., Brose, A., Schmiedek, F., & Muthén, B. (2018) · Multivariate Behavioral Research · doi:10.1080/00273171.2018.1446819 · Cited by 549

On recent advances in PV output power forecast

Raza, M. Q., Nadarajah, M., & Ekanayake, C. (2016) · Solar Energy · doi:10.1016/j.solener.2016.06.073 · Cited by 544

Detecting change-point, trend, and seasonality in satellite time series data to track abrupt changes and nonlinear dynamics: A Bayesian ensemble algorithm

Zhao, K., Wulder, M. A., Hu, T., Bright, R., Wu, Q., Qin, H., Li, Y., Toman, E., Mallick, B., Zhang, X., et al. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.04.034 · Cited by 514

“Is the U.S. Aggregate Production Function Cobb-Douglas? New Estimates of the Elasticity of Substitution”

Antràs, P. (2004) · Contributions in Macroeconomics · doi:10.2202/1534-6005.1161 · Cited by 470

Improving Consumer Mindset Metrics and Shareholder Value through Social Media: The Different Roles of Owned and Earned Media

Colicev, A., Malshe, A., Pauwels, K., & O'Connor, P. (2018) · Journal of Marketing · doi:10.1509/jm.16.0055 · Cited by 370

Measuring investor sentiment with mutual fund flows

Ben-Rephael, A., Kandel, S., & Wohl, A. (2012) · Journal of Financial Economics · doi:10.1016/j.jfineco.2010.08.018 · Cited by 358

Strengthening of Top-Down Frontal Cognitive Control Networks Underlying the Development of Inhibitory Control: A Functional Magnetic Resonance Imaging Effective Connectivity Study

Hwang, K., Velanova, K., & Luna, B. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.2825-10.2010 · Cited by 314

Electrophysiological Imaging of Brain Activity and Connectivity—Challenges and Opportunities

He, B., Yang, L., Wilke, C., & Yuan, H. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2139210 · Cited by 300

Pysteps: an open-source Python library for probabilistic precipitation nowcasting (v1.0)

Pulkkinen, S., Nerini, D., Pérez Hortal, A. A., Velasco-Forero, C., Seed, A., Germann, U., & Foresti, L. (2019) · Geoscientific Model Development · doi:10.5194/gmd-12-4185-2019 · Cited by 304

Practical Measures of Integrated Information for Time-Series Data

Barrett, A. B., & Seth, A. K. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1001052 · Cited by 225

Dangers and uses of cross-correlation in analyzing time series in perception, performance, movement, and neuroscience: The importance of constructing transfer function autoregressive models

Dean, R. T., & Dunsmuir, W. T. M. (2016) · Behavior Research Methods · doi:10.3758/s13428-015-0611-2 · Cited by 215

Optimal Mortgage Refinancing: A Closed‐Form Solution

AGARWAL, S., DRISCOLL, J. C., & LAIBSON, D. I. (2013) · Journal of Money, Credit and Banking · doi:10.1111/jmcb.12017 · Cited by 185

Drawing conclusions from cross-lagged relationships: Re-considering the role of the time-interval

Kuiper, R. M., & Ryan, O. (2018) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705511.2018.1431046 · Cited by 155

Advances in nowcasting influenza-like illness rates using search query logs

Lampos, V., Miller, A. C., Crossan, S., & Stefansen, C. (2015) · Scientific Reports · doi:10.1038/srep12760 · Cited by 143

Revisiting economic shocks and coups

Kim, N. K. (2016) · Journal of Conflict Resolution · doi:10.1177/0022002713520531 · Cited by 128

Simultaneous Confidence Regions for Impulse Responses

Jordà, Ò. (2009) · Review of Economics and Statistics · doi:10.1162/rest.91.3.629 · Cited by 121

Linguistic Markers Indicating Therapeutic Outcomes of Social Media Disclosures of Schizophrenia

Ernala, S. K., Rizvi, A. F., Birnbaum, M. L., Kane, J. M., & De Choudhury, M. (2017) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3134678 · Cited by 99

The heart of the story: Peripheral physiology during narrative exposure predicts charitable giving

Barraza, J. A., Alexander, V., Beavin, L. E., Terris, E. T., & Zak, P. J. (2015) · Biological Psychology · doi:10.1016/j.biopsycho.2015.01.008 · Cited by 90

Public Perceptions On Water Reuse Options: The Case Of Sulaibiya Wastewater Treatment Plant In Kuwait

Alhumoud, J. M., & Madzikanda, D. (2010) · International Business & Economics Research Journal (IBER) · doi:10.19030/iber.v9i1.515 · Cited by 71

A Continuous-Time Approach to Intensive Longitudinal Data: What, Why, and How?

Ryan, O., Kuiper, R. M., & Hamaker, E. L. (2018) · Continuous Time Modeling in the Behavioral and Related Sciences · doi:10.1007/978-3-319-77219-6_2 · Cited by 67

Multiscale Analysis of Intensive Longitudinal Biomedical Signals and Its Clinical Applications

Nakamura, T., Kiyono, K., Wendt, H., Abry, P., & Yamamoto, Y. (2016) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2491979 · Cited by 64

The Differential Time-Varying Effect Model (DTVEM): A tool for diagnosing and modeling time lags in intensive longitudinal data

Jacobson, N. C., Chow, S.-M., & Newman, M. G. (2019) · Behavior Research Methods · doi:10.3758/s13428-018-1101-0 · Cited by 63

Measuring the Reliability of Reinforcement Learning Algorithms

Chan, S. C. Y., Fishman, S., Canny, J., Korattikara, A., & Guadarrama, S. (2019) · arXiv · doi:10.48550/arxiv.1912.05663 · Cited by 57

Sleep Enhances Off-line Spatial and Temporal Motor Learning After Stroke

Siengsukon, C., & Boyd, L. A. (2009) · Neurorehabilitation and Neural Repair · doi:10.1177/1545968308326631 · Cited by 49

Short-term passenger demand forecasting using univariate time series theory

Cyprich, O., Konečný, V., & Kiliánová, K. (2013) · Promet - Traffic&Transportation · doi:10.7307/ptt.v25i6.338 · Cited by 30

Hate in Word and Deed: The Temporal Association Between Online and Offline Islamophobia

Wiedlitzka, S., Prati, G., Brown, R., Smith, J., & Walters, M. A. (2023) · Journal of Quantitative Criminology · doi:10.1007/s10940-021-09530-9 · Cited by 27

Syndromic surveillance for health information system failures: a feasibility study

Ong, M.-S., Magrabi, F., & Coiera, E. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-001144 · Cited by 22

For incapacitation, there is no time like the present: The lagged effects of prisoner reentry on property and violent crime rates

DeFina, R., & Hannon, L. (2010) · Social Science Research · doi:10.1016/j.ssresearch.2010.08.001 · Cited by 22

Honto? Search: Estimating Trustworthiness of Web Information by Search Results Aggregation and Temporal Analysis

Yamamoto, Y., Tezuka, T., Jatowt, A., & Tanaka, K. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-72524-4_28 · Cited by 14

Is Fertility a Leading Economic Indicator?

Buckles, K., Hungerman, D., & Lugauer, S. (2018) · doi:10.3386/w24355 · Cited by 14

Joint Inference and Counterfactual Experimentation for Impulse Response Functions By Local Projections

Jorda, O. (2006) · doi:10.2139/ssrn.930508 · Cited by 1