41 results
Head and Neck Cancer: United Kingdom National Multidisciplinary Guidelines, Sixth Edition
- Jarrod J Homer, Stuart C Winter, Elizabeth C Abbey, Hiba Aga, Reshma Agrawal, Derfel ap Dafydd, Takhar Arunjit, Patrick Axon, Eleanor Aynsley, Izhar N Bagwan, Arun Batra, Donna Begg, Jonathan M Bernstein, Guy Betts, Colin Bicknell, Brian Bisase, Grainne C Brady, Peter Brennan, Aina Brunet, Val Bryant, Linda Cantwell, Ashish Chandra, Preetha Chengot, Melvin L K Chua, Peter Clarke, Gemma Clunie, Margaret Coffey, Clare Conlon, David I Conway, Florence Cook, Matthew R Cooper, Declan Costello, Ben Cosway, Neil J A Cozens, Grant Creaney, Daljit K Gahir, Stephen Damato, Joe Davies, Katharine S Davies, Alina D Dragan, Yong Du, Mark R D Edmond, Stefano Fedele, Harriet Finze, Jason C Fleming, Bernadette H Foran, Beth Fordham, Mohammed M A S Foridi, Lesley Freeman, Katherine E Frew, Pallavi Gaitonde, Victoria Gallyer, Fraser W Gibb, Sinclair M Gore, Mark Gormley, Roganie Govender, J Greedy, Teresa Guerrero Urbano, Dorothy Gujral, David W Hamilton, John C Hardman, Kevin Harrington, Samantha Holmes, Jarrod J Homer, Deborah Howland, Gerald Humphris, Keith D Hunter, Kate Ingarfield, Richard Irving, Kristina Isand, Yatin Jain, Sachin Jauhar, Sarra Jawad, Glyndwr W Jenkins, Anastasios Kanatas, Stephen Keohane, Cyrus J Kerawala, William Keys, Emma V King, Anthony Kong, Fiona Lalloo, Kirsten Laws, Samuel C Leong, Shane Lester, Miles Levy, Ken Lingley, Gitta Madani, Navin Mani, Paolo L Matteucci, Catriona R Mayland, James McCaul, Lorna K McCaul, Pádraig McDonnell, Andrew McPartlin, Valeria Mercadante, Zoe Merchant, Radu Mihai, Mufaddal T Moonim, John Moore, Paul Nankivell, Sonali Natu, A Nelson, Pablo Nenclares, Kate Newbold, Carrie Newland, Ailsa J Nicol, Iain J Nixon, Rupert Obholzer, James T O'Hara, S Orr, Vinidh Paleri, James Palmer, Rachel S Parry, Claire Paterson, Gillian Patterson, Joanne M Patterson, Miranda Payne, L Pearson, David N Poller, Jonathan Pollock, Stephen Ross Porter, Matthew Potter, Robin J D Prestwich, Ruth Price, Mani Ragbir, Meena S Ranka, Max Robinson, Justin W G Roe, Tom Roques, Aleix Rovira, Sajid Sainuddin, I J Salmon, Ann Sandison, Andy Scarsbrook, Andrew G Schache, A Scott, Diane Sellstrom, Cherith J Semple, Jagrit Shah, Praveen Sharma, Richard J Shaw, Somiah Siddiq, Priyamal Silva, Ricard Simo, Rabin P Singh, Maria Smith, Rebekah Smith, Toby Oliver Smith, Sanjai Sood, Francis W Stafford, Neil Steven, Kay Stewart, Lisa Stoner, Steve Sweeney, Andrew Sykes, Carly L Taylor, Selvam Thavaraj, David J Thomson, Jane Thornton, Neil S Tolley, Nancy Turnbull, Sriram Vaidyanathan, Leandros Vassiliou, John Waas, Kelly Wade-McBane, Donna Wakefield, Amy Ward, Laura Warner, Laura-Jayne Watson, H Watts, Christina Wilson, Stuart C Winter, Winson Wong, Chui-Yan Yip, Kent Yip
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- Journal:
- The Journal of Laryngology & Otology / Volume 138 / Issue S1 / April 2024
- Published online by Cambridge University Press:
- 14 March 2024, pp. S1-S224
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- April 2024
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Human Rights Violations in Space: Assessing the External Validity of Machine-Geocoded versus Human-Geocoded Data
- Logan Stundal, Benjamin E. Bagozzi, John R. Freeman, Jennifer S. Holmes
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- Journal:
- Political Analysis / Volume 31 / Issue 1 / January 2023
- Published online by Cambridge University Press:
- 15 December 2021, pp. 81-97
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Political event data are widely used in studies of political violence. Recent years have seen notable advances in the automated coding of political event data from international news sources. Yet, the validity of machine-coded event data remains disputed, especially in the context of event geolocation. We analyze the frequencies of human- and machine-geocoded event data agreement in relation to an independent (ground truth) source. The events are human rights violations in Colombia. We perform our evaluation for a key, 8-year period of the Colombian conflict and in three 2-year subperiods as well as for a selected set of (non)journalistically remote municipalities. As a complement to this analysis, we estimate spatial probit models based on the three datasets. These models assume Gaussian Markov Random Field error processes; they are constructed using a stochastic partial differential equation and estimated with integrated nested Laplacian approximation. The estimated models tell us whether the three datasets produce comparable predictions, underreport events in relation to the same covariates, and have similar patterns of prediction error. Together the two analyses show that, for this subnational conflict, the machine- and human-geocoded datasets are comparable in terms of external validity but, according to the geostatistical models, produce prediction errors that differ in important respects.
The effect of temporal variation in feed quality and quantity on the diurnal feeding behaviour of dairy cows
- A. J. John, S. C. Garcia, K. L. Kerrisk, M. J. Freeman, M. R. Islam, C. E. F. Clark
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The diurnal feeding patterns of dairy cows affects the 24 h robot utilisation of pasture-based automatic milking systems (AMS). A decline in robot utilisation between 2400 and 0600 h currently occurs in pasture-based AMS, as cow feeding activity is greatly reduced during this time. Here, we investigate the effect of a temporal variation in feed quality and quantity on cow feeding behaviour between 2400 and 0600 h as a potential tool to increase voluntary cow trafficking in an AMS at night. The day was allocated into four equal feeding periods (0600 to 1200, 1200 to 1800, 1800 to 2400 and 2400 to 0600 h). Lucerne hay cubes (CP = 19.1%, water soluble carbohydrate = 3.8%) and oat, ryegrass and clover hay cubes with 20% molasses (CP = 11.8%, water soluble carbohydrate = 10.7%) were offered as the ‘standard’ and ‘preferred’ (preference determined previously) feed types, respectively. The four treatments were (1) standard feed offered ad libitum (AL) throughout 24 h; (2) as per AL, with preferred feed replacing standard feed between 2400 and 0600 h (AL + P); (3) standard feed offered at a restricted rate, with quantity varying between each feeding period (20:10:30:60%, respectively) as a proportion of the (previously) measured daily ad libitum intake (VA); (4) as per VA, with preferred feed replacing standard feed between 2400 and 0600 h (VA + P). Eight non-lactating dairy cows were used in a 4 × 4 Latin square design. During each experimental period, treatment cows were fed for 7 days, including 3 days habituation and 4 days data collection. Total daily intake was approximately 8% greater (P < 0.001) for the AL and AL + P treatments (23.1 and 22.9 kg DM/cow) as compared with the VA and VA + P treatments (21.6 and 20.9 kg DM/cow). The AL + P and VA treatments had 21% and 90% greater (P < 0.001) dry matter intake (DMI) between 2400 and 0600 h, respectively, compared with the AL treatment. In contrast, the VA + P treatment had similar DMI to the VA treatment. Our experiment shows ability to increase cow feeding activity at night by varying feed type and quantity, though it is possible that a penalty to total DMI may occur using VA. Further research is required to determine if the implementation of variable feed allocation on pasture-based AMS farms is likely to improve milking robot utilisation by increasing cow feeding activity at night.
The Prevalence and Severity of Underreporting Bias in Machine- and Human-Coded Data
- Benjamin E. Bagozzi, Patrick T. Brandt, John R. Freeman, Jennifer S. Holmes, Alisha Kim, Agustin Palao Mendizabal, Carly Potz-Nielsen
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- Journal:
- Political Science Research and Methods / Volume 7 / Issue 3 / July 2019
- Published online by Cambridge University Press:
- 05 March 2018, pp. 641-649
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Textual data are plagued by underreporting bias. For example, news sources often fail to report human rights violations. Cook et al. propose a multi-source estimator to gauge, and to account for, the underreporting of state repression events within human codings of news texts produced by the Agence France-Presse and Associated Press. We evaluate this estimator with Monte Carlo experiments, and then use it to compare the prevalence and seriousness of underreporting when comparable texts are machine coded and recorded in the World-Integrated Crisis Early Warning System dataset. We replicate Cook et al.’s investigation of human-coded state repression events with our machine-coded events, and validate both models against an external measure of human rights protections in Africa. We then use the Cook et al. estimator to gauge the seriousness and prevalence of underreporting in machine and human-coded event data on human rights violations in Colombia. We find in both applications that machine-coded data are as valid as human-coded data.
Modeling Macro-Political Dynamics
- Patrick T. Brandt, John R. Freeman
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- Journal:
- Political Analysis / Volume 17 / Issue 2 / Spring 2009
- Published online by Cambridge University Press:
- 04 January 2017, pp. 113-142
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Analyzing macro-political processes is complicated by four interrelated problems: model scale, endogeneity, persistence, and specification uncertainty. These problems are endemic in the study of political economy, public opinion, international relations, and other kinds of macro-political research. We show how a Bayesian structural time series approach addresses them. Our illustration is a structurally identified, nine-equation model of the U.S. political-economic system. It combines key features of the model of Erikson, MacKuen, and Stimson (2002) of the American macropolity with those of a leading macroeconomic model of the United States (Sims and Zha, 1998; Leeper, Sims, and Zha, 1996). This Bayesian structural model, with a loosely informed prior, yields the best performance in terms of model fit and dynamics. This model 1) confirms existing results about the countercyclical nature of monetary policy (Williams 1990); 2) reveals informational sources of approval dynamics: innovations in information variables affect consumer sentiment and approval and the impacts on consumer sentiment feed-forward into subsequent approval changes; 3) finds that the real economy does not have any major impacts on key macropolity variables; and 4) concludes, contrary to Erikson, MacKuen, and Stimson (2002), that macropartisanship does not depend on the evolution of the real economy in the short or medium term and only very weakly on informational variables in the long term.
Symposium on Models of Path Dependence
- John R. Freeman, John E. Jackson
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- Journal:
- Political Analysis / Volume 20 / Issue 2 / Spring 2012
- Published online by Cambridge University Press:
- 04 January 2017, pp. 137-145
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The symposium develops statistical models and methods for the study of path dependence. In this introductory essay, the connections between key areas in the path dependence and statistical literatures are illuminated. And some ways in which familiar time series and regression models embody these ideas are explained. The arguments in the articles in the symposium then are summarized and compared. Finally, directions for additional, statistically grounded research on path dependence are discussed.
Systematic Sampling, Temporal Aggregation, and the Study of Political Relationships
- John R. Freeman
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- Journal:
- Political Analysis / Volume 1 / 1989
- Published online by Cambridge University Press:
- 04 January 2017, pp. 61-98
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Systematic sampling and temporal aggregation are the practices of sampling a time series at regular intervals and of summing or averaging time series observations over a time interval, respectively. Both practices are a source of statistical error and faulty inference. The problems that systematic sampling and temporal aggregation create for the construction of strongly specified and weakly specified models are discussed. The seriousness of these problems then is illustrated with respect to the debate about superpower rivalry. The debate is shown to derive, in part, from the fact that some researchers employ highly temporally aggregated measures of U.S. and Soviet foreign policy behavior. The larger methodological lessons are that we need to devote more time to determining the natural time unit of our theories and to conducting robustness checks across levels of temporal aggregation.
Advances in Bayesian Time Series Modeling and the Study of Politics: Theory Testing, Forecasting, and Policy Analysis
- Patrick T. Brandt, John R. Freeman
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- Journal:
- Political Analysis / Volume 14 / Issue 1 / Winter 2006
- Published online by Cambridge University Press:
- 04 January 2017, pp. 1-36
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Bayesian approaches to the study of politics are increasingly popular. But Bayesian approaches to modeling multiple time series have not been critically evaluated. This is in spite of the potential value of these models in international relations, political economy, and other fields of our discipline. We review recent developments in Bayesian multi-equation time series modeling in theory testing, forecasting, and policy analysis. Methods for constructing Bayesian measures of uncertainty of impulse responses (Bayesian shape error bands) are explained. A reference prior for these models that has proven useful in short- and medium-term forecasting in macroeconomics is described. Once modified to incorporate our experience analyzing political data and our theories, this prior can enhance our ability to forecast over the short and medium terms complex political dynamics like those exhibited by certain international conflicts. In addition, we explain how contingent Bayesian forecasts can be constructed, contingent Bayesian forecasts that embody policy counterfactuals. The value of these new Bayesian methods is illustrated in a reanalysis of the Israeli-Palestinian conflict of the 1980s.
Progress in the Study of Nonstationary Political Time Series: A Comment
- John R. Freeman
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- Journal:
- Political Analysis / Volume 24 / Issue 1 / Winter 2016
- Published online by Cambridge University Press:
- 04 January 2017, pp. 50-58
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Cointegration was introduced to our discipline by Renée Smith and Charles Ostrom Jr. and by Robert Durr more than two decades ago at political methodology meetings in Washington University�St. Louis and Florida State University. Their articles, along with comments by Neal Beck and John T. Williams, were published in a symposium like this one in the fourth volume of Political Analysis. Keele, Lin, and Webb (2016; hereafter KLW) and Grant and Lebo (2016; hereafter GL) show how, in the years that followed, cointegration was further evaluated by political scientists, and the related idea of error correction subsequently was applied.
Have the last twenty-plus years witnessed significant progress in modeling nonstationary political time series? In some respects, the answer is yes. The present symposium represents progress in understanding equation balance, analyzing bounded variables, and decomposing short- and longterm causal effects. In these respects KLW's and GL's articles deserve wide dissemination. But KLW and GL leave important methodological issues unresolved. They do not address some critical methodological challenges. From a historical perspective, the present symposium shows that we have made relatively little progress in modeling nonstationary political time series.
Contributors
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- By Mitchell Aboulafia, Frederick Adams, Marilyn McCord Adams, Robert M. Adams, Laird Addis, James W. Allard, David Allison, William P. Alston, Karl Ameriks, C. Anthony Anderson, David Leech Anderson, Lanier Anderson, Roger Ariew, David Armstrong, Denis G. Arnold, E. J. Ashworth, Margaret Atherton, Robin Attfield, Bruce Aune, Edward Wilson Averill, Jody Azzouni, Kent Bach, Andrew Bailey, Lynne Rudder Baker, Thomas R. Baldwin, Jon Barwise, George Bealer, William Bechtel, Lawrence C. Becker, Mark A. Bedau, Ernst Behler, José A. Benardete, Ermanno Bencivenga, Jan Berg, Michael Bergmann, Robert L. Bernasconi, Sven Bernecker, Bernard Berofsky, Rod Bertolet, Charles J. Beyer, Christian Beyer, Joseph Bien, Joseph Bien, Peg Birmingham, Ivan Boh, James Bohman, Daniel Bonevac, Laurence BonJour, William J. Bouwsma, Raymond D. Bradley, Myles Brand, Richard B. Brandt, Michael E. Bratman, Stephen E. Braude, Daniel Breazeale, Angela Breitenbach, Jason Bridges, David O. Brink, Gordon G. Brittan, Justin Broackes, Dan W. Brock, Aaron Bronfman, Jeffrey E. Brower, Bartosz Brozek, Anthony Brueckner, Jeffrey Bub, Lara Buchak, Otavio Bueno, Ann E. Bumpus, Robert W. Burch, John Burgess, Arthur W. Burks, Panayot Butchvarov, Robert E. Butts, Marina Bykova, Patrick Byrne, David Carr, Noël Carroll, Edward S. Casey, Victor Caston, Victor Caston, Albert Casullo, Robert L. Causey, Alan K. L. Chan, Ruth Chang, Deen K. Chatterjee, Andrew Chignell, Roderick M. Chisholm, Kelly J. Clark, E. J. Coffman, Robin Collins, Brian P. Copenhaver, John Corcoran, John Cottingham, Roger Crisp, Frederick J. Crosson, Antonio S. Cua, Phillip D. Cummins, Martin Curd, Adam Cureton, Andrew Cutrofello, Stephen Darwall, Paul Sheldon Davies, Wayne A. Davis, Timothy Joseph Day, Claudio de Almeida, Mario De Caro, Mario De Caro, John Deigh, C. F. Delaney, Daniel C. Dennett, Michael R. DePaul, Michael Detlefsen, Daniel Trent Devereux, Philip E. Devine, John M. Dillon, Martin C. Dillon, Robert DiSalle, Mary Domski, Alan Donagan, Paul Draper, Fred Dretske, Mircea Dumitru, Wilhelm Dupré, Gerald Dworkin, John Earman, Ellery Eells, Catherine Z. Elgin, Berent Enç, Ronald P. Endicott, Edward Erwin, John Etchemendy, C. Stephen Evans, Susan L. Feagin, Solomon Feferman, Richard Feldman, Arthur Fine, Maurice A. Finocchiaro, William FitzPatrick, Richard E. Flathman, Gvozden Flego, Richard Foley, Graeme Forbes, Rainer Forst, Malcolm R. Forster, Daniel Fouke, Patrick Francken, Samuel Freeman, Elizabeth Fricker, Miranda Fricker, Michael Friedman, Michael Fuerstein, Richard A. Fumerton, Alan Gabbey, Pieranna Garavaso, Daniel Garber, Jorge L. A. Garcia, Robert K. Garcia, Don Garrett, Philip Gasper, Gerald Gaus, Berys Gaut, Bernard Gert, Roger F. Gibson, Cody Gilmore, Carl Ginet, Alan H. Goldman, Alvin I. Goldman, Alfonso Gömez-Lobo, Lenn E. Goodman, Robert M. Gordon, Stefan Gosepath, Jorge J. E. Gracia, Daniel W. Graham, George A. Graham, Peter J. Graham, Richard E. Grandy, I. Grattan-Guinness, John Greco, Philip T. Grier, Nicholas Griffin, Nicholas Griffin, David A. Griffiths, Paul J. Griffiths, Stephen R. Grimm, Charles L. Griswold, Charles B. Guignon, Pete A. Y. Gunter, Dimitri Gutas, Gary Gutting, Paul Guyer, Kwame Gyekye, Oscar A. Haac, Raul Hakli, Raul Hakli, Michael Hallett, Edward C. Halper, Jean Hampton, R. James Hankinson, K. R. Hanley, Russell Hardin, Robert M. Harnish, William Harper, David Harrah, Kevin Hart, Ali Hasan, William Hasker, John Haugeland, Roger Hausheer, William Heald, Peter Heath, Richard Heck, John F. Heil, Vincent F. Hendricks, Stephen Hetherington, Francis Heylighen, Kathleen Marie Higgins, Risto Hilpinen, Harold T. Hodes, Joshua Hoffman, Alan Holland, Robert L. Holmes, Richard Holton, Brad W. Hooker, Terence E. Horgan, Tamara Horowitz, Paul Horwich, Vittorio Hösle, Paul Hoβfeld, Daniel Howard-Snyder, Frances Howard-Snyder, Anne Hudson, Deal W. Hudson, Carl A. Huffman, David L. Hull, Patricia Huntington, Thomas Hurka, Paul Hurley, Rosalind Hursthouse, Guillermo Hurtado, Ronald E. Hustwit, Sarah Hutton, Jonathan Jenkins Ichikawa, Harry A. Ide, David Ingram, Philip J. Ivanhoe, Alfred L. Ivry, Frank Jackson, Dale Jacquette, Joseph Jedwab, Richard Jeffrey, David Alan Johnson, Edward Johnson, Mark D. Jordan, Richard Joyce, Hwa Yol Jung, Robert Hillary Kane, Tomis Kapitan, Jacquelyn Ann K. Kegley, James A. Keller, Ralph Kennedy, Sergei Khoruzhii, Jaegwon Kim, Yersu Kim, Nathan L. King, Patricia Kitcher, Peter D. Klein, E. D. Klemke, Virginia Klenk, George L. Kline, Christian Klotz, Simo Knuuttila, Joseph J. Kockelmans, Konstantin Kolenda, Sebastian Tomasz Kołodziejczyk, Isaac Kramnick, Richard Kraut, Fred Kroon, Manfred Kuehn, Steven T. Kuhn, Henry E. Kyburg, John Lachs, Jennifer Lackey, Stephen E. Lahey, Andrea Lavazza, Thomas H. Leahey, Joo Heung Lee, Keith Lehrer, Dorothy Leland, Noah M. Lemos, Ernest LePore, Sarah-Jane Leslie, Isaac Levi, Andrew Levine, Alan E. Lewis, Daniel E. Little, Shu-hsien Liu, Shu-hsien Liu, Alan K. L. Chan, Brian Loar, Lawrence B. Lombard, John Longeway, Dominic McIver Lopes, Michael J. Loux, E. J. Lowe, Steven Luper, Eugene C. Luschei, William G. Lycan, David Lyons, David Macarthur, Danielle Macbeth, Scott MacDonald, Jacob L. Mackey, Louis H. Mackey, Penelope Mackie, Edward H. Madden, Penelope Maddy, G. B. Madison, Bernd Magnus, Pekka Mäkelä, Rudolf A. Makkreel, David Manley, William E. Mann (W.E.M.), Vladimir Marchenkov, Peter Markie, Jean-Pierre Marquis, Ausonio Marras, Mike W. Martin, A. P. Martinich, William L. McBride, David McCabe, Storrs McCall, Hugh J. McCann, Robert N. McCauley, John J. McDermott, Sarah McGrath, Ralph McInerny, Daniel J. McKaughan, Thomas McKay, Michael McKinsey, Brian P. McLaughlin, Ernan McMullin, Anthonie Meijers, Jack W. Meiland, William Jason Melanson, Alfred R. Mele, Joseph R. Mendola, Christopher Menzel, Michael J. Meyer, Christian B. Miller, David W. Miller, Peter Millican, Robert N. Minor, Phillip Mitsis, James A. Montmarquet, Michael S. Moore, Tim Moore, Benjamin Morison, Donald R. Morrison, Stephen J. Morse, Paul K. Moser, Alexander P. D. Mourelatos, Ian Mueller, James Bernard Murphy, Mark C. Murphy, Steven Nadler, Jan Narveson, Alan Nelson, Jerome Neu, Samuel Newlands, Kai Nielsen, Ilkka Niiniluoto, Carlos G. Noreña, Calvin G. Normore, David Fate Norton, Nikolaj Nottelmann, Donald Nute, David S. Oderberg, Steve Odin, Michael O’Rourke, Willard G. Oxtoby, Heinz Paetzold, George S. Pappas, Anthony J. Parel, Lydia Patton, R. P. Peerenboom, Francis Jeffry Pelletier, Adriaan T. Peperzak, Derk Pereboom, Jaroslav Peregrin, Glen Pettigrove, Philip Pettit, Edmund L. Pincoffs, Andrew Pinsent, Robert B. Pippin, Alvin Plantinga, Louis P. Pojman, Richard H. Popkin, John F. Post, Carl J. Posy, William J. Prior, Richard Purtill, Michael Quante, Philip L. Quinn, Philip L. Quinn, Elizabeth S. Radcliffe, Diana Raffman, Gerard Raulet, Stephen L. Read, Andrews Reath, Andrew Reisner, Nicholas Rescher, Henry S. Richardson, Robert C. Richardson, Thomas Ricketts, Wayne D. Riggs, Mark Roberts, Robert C. Roberts, Luke Robinson, Alexander Rosenberg, Gary Rosenkranz, Bernice Glatzer Rosenthal, Adina L. Roskies, William L. Rowe, T. M. Rudavsky, Michael Ruse, Bruce Russell, Lilly-Marlene Russow, Dan Ryder, R. M. Sainsbury, Joseph Salerno, Nathan Salmon, Wesley C. Salmon, Constantine Sandis, David H. Sanford, Marco Santambrogio, David Sapire, Ruth A. Saunders, Geoffrey Sayre-McCord, Charles Sayward, James P. Scanlan, Richard Schacht, Tamar Schapiro, Frederick F. Schmitt, Jerome B. Schneewind, Calvin O. Schrag, Alan D. Schrift, George F. Schumm, Jean-Loup Seban, David N. Sedley, Kenneth Seeskin, Krister Segerberg, Charlene Haddock Seigfried, Dennis M. Senchuk, James F. Sennett, William Lad Sessions, Stewart Shapiro, Tommie Shelby, Donald W. Sherburne, Christopher Shields, Roger A. Shiner, Sydney Shoemaker, Robert K. Shope, Kwong-loi Shun, Wilfried Sieg, A. John Simmons, Robert L. Simon, Marcus G. Singer, Georgette Sinkler, Walter Sinnott-Armstrong, Matti T. Sintonen, Lawrence Sklar, Brian Skyrms, Robert C. Sleigh, Michael Anthony Slote, Hans Sluga, Barry Smith, Michael Smith, Robin Smith, Robert Sokolowski, Robert C. Solomon, Marta Soniewicka, Philip Soper, Ernest Sosa, Nicholas Southwood, Paul Vincent Spade, T. L. S. Sprigge, Eric O. Springsted, George J. Stack, Rebecca Stangl, Jason Stanley, Florian Steinberger, Sören Stenlund, Christopher Stephens, James P. Sterba, Josef Stern, Matthias Steup, M. A. Stewart, Leopold Stubenberg, Edith Dudley Sulla, Frederick Suppe, Jere Paul Surber, David George Sussman, Sigrún Svavarsdóttir, Zeno G. Swijtink, Richard Swinburne, Charles C. Taliaferro, Robert B. Talisse, John Tasioulas, Paul Teller, Larry S. Temkin, Mark Textor, H. S. Thayer, Peter Thielke, Alan Thomas, Amie L. Thomasson, Katherine Thomson-Jones, Joshua C. Thurow, Vzalerie Tiberius, Terrence N. Tice, Paul Tidman, Mark C. Timmons, William Tolhurst, James E. Tomberlin, Rosemarie Tong, Lawrence Torcello, Kelly Trogdon, J. D. Trout, Robert E. Tully, Raimo Tuomela, John Turri, Martin M. Tweedale, Thomas Uebel, Jennifer Uleman, James Van Cleve, Harry van der Linden, Peter van Inwagen, Bryan W. Van Norden, René van Woudenberg, Donald Phillip Verene, Samantha Vice, Thomas Vinci, Donald Wayne Viney, Barbara Von Eckardt, Peter B. M. Vranas, Steven J. Wagner, William J. Wainwright, Paul E. Walker, Robert E. Wall, Craig Walton, Douglas Walton, Eric Watkins, Richard A. Watson, Michael V. Wedin, Rudolph H. Weingartner, Paul Weirich, Paul J. Weithman, Carl Wellman, Howard Wettstein, Samuel C. Wheeler, Stephen A. White, Jennifer Whiting, Edward R. Wierenga, Michael Williams, Fred Wilson, W. Kent Wilson, Kenneth P. Winkler, John F. Wippel, Jan Woleński, Allan B. Wolter, Nicholas P. Wolterstorff, Rega Wood, W. Jay Wood, Paul Woodruff, Alison Wylie, Gideon Yaffe, Takashi Yagisawa, Yutaka Yamamoto, Keith E. Yandell, Xiaomei Yang, Dean Zimmerman, Günter Zoller, Catherine Zuckert, Michael Zuckert, Jack A. Zupko (J.A.Z.)
- Edited by Robert Audi, University of Notre Dame, Indiana
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- Book:
- The Cambridge Dictionary of Philosophy
- Published online:
- 05 August 2015
- Print publication:
- 27 April 2015, pp ix-xxx
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Contributors
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- By Rony A. Adam, Gloria Bachmann, Nichole M. Barker, Randall B. Barnes, John Bennett, Inbar Ben-Shachar, Jonathan S. Berek, Sarah L. Berga, Monica W. Best, Eric J. Bieber, Frank M. Biro, Shan Biscette, Anita K. Blanchard, Candace Brown, Ronald T. Burkman, Joseph Buscema, John E. Buster, Michael Byas-Smith, Sandra Ann Carson, Judy C. Chang, Annie N. Y. Cheung, Mindy S. Christianson, Karishma Circelli, Daniel L. Clarke-Pearson, Larry J. Copeland, Bryan D. Cowan, Navneet Dhillon, Michael P. Diamond, Conception Diaz-Arrastia, Nicole M. Donnellan, Michael L. Eisenberg, Eric Eisenhauer, Sebastian Faro, J. Stuart Ferriss, Lisa C. Flowers, Susan J. Freeman, Leda Gattoc, Claudine Marie Gayle, Timothy M. Geiger, Jennifer S. Gell, Alan N. Gordon, Victoria L. Green, Jon K. Hathaway, Enrique Hernandez, S. Paige Hertweck, Randall S. Hines, Ira R. Horowitz, Fred M. Howard, William W. Hurd, Fidan Israfilbayli, Denise J. Jamieson, Carolyn R. Jaslow, Erika B. Johnston-MacAnanny, Rohna M. Kearney, Namita Khanna, Caroline C. King, Jeremy A. King, Ira J. Kodner, Tamara Kolev, Athena P. Kourtis, S. Robert Kovac, Ertug Kovanci, William H. Kutteh, Eduardo Lara-Torre, Pallavi Latthe, Herschel W. Lawson, Ronald L. Levine, Frank W. Ling, Larry I. Lipshultz, Steven D. McCarus, Robert McLellan, Shruti Malik, Suketu M. Mansuria, Mohamed K. Mehasseb, Pamela J. Murray, Saloney Nazeer, Farr R. Nezhat, Hextan Y. S. Ngan, Gina M. Northington, Peggy A. Norton, Ruth M. O'Regan, Kristiina Parviainen, Resad P. Pasic, Tanja Pejovic, K. Ulrich Petry, Nancy A. Phillips, Ashish Pradhan, Elizabeth E. Puscheck, Suneetha Rachaneni, Devon M. Ramaeker, David B. Redwine, Robert L. Reid, Carla P. Roberts, Walter Romano, Peter G. Rose, Robert L. Rosenfield, Shon P. Rowan, Mack T. Ruffin, Janice M. Rymer, Evis Sala, Ritu Salani, Joseph S. Sanfilippo, Mahmood I. Shafi, Roger P. Smith, Meredith L. Snook, Thomas E. Snyder, Mary D. Stephenson, Thomas G. Stovall, Richard L. Sweet, Philip M. Toozs-Hobson, Togas Tulandi, Elizabeth R. Unger, Denise S. Uyar, Marion S. Verp, Rahi Victory, Tamara J. Vokes, Michelle J. Washington, Katharine O'Connell White, Paul E. Wise, Frank M. Wittmaack, Miya P. Yamamoto, Christine Yu, Howard A. Zacur
- Edited by Eric J. Bieber, Joseph S. Sanfilippo, University of Pittsburgh, Ira R. Horowitz, Emory University, Atlanta, Mahmood I. Shafi
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- Book:
- Clinical Gynecology
- Published online:
- 05 April 2015
- Print publication:
- 23 April 2015, pp viii-xiv
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8 - Concluding Thoughts for the Time Series Analyst
- Janet M. Box-Steffensmeier, Ohio State University, John R. Freeman, University of Minnesota, Matthew P. Hitt, Louisiana State University, Jon C. W. Pevehouse, University of Wisconsin, Madison
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- Book:
- Time Series Analysis for the Social Sciences
- Published online:
- 05 December 2014
- Print publication:
- 22 December 2014, pp 214-218
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Summary
We began this book by suggesting that scholars in the social sciences are often interested in how processes – whether political, economic, or social – changeover time. Throughout, we have emphasized that although many of our theories discuss that change, often our empirical models do not give the concept of change the same pride of place. Time series elements in data are often treated as a nuisance – something to cleanse from otherwise meaningful information – rather than part and parcel of the data-generating process that we attempt to describe with our theories.
We hope this book is an antidote to this thinking. Social dynamics are crucial to all of the social sciences. We have tried to provide some tools to model and therefore understand some of these social dynamics. Rather than treat temporal dynamics as a nuisance or a problem to be ameliorated, we have emphasized that the diagnosis, modeling, and analysis of those dynamics are key to the substance of the social sciences. Knowing a unit root exists in a series tell us something about the data-generating process: shocks to the series permanently shift the series, integrating into it. Graphing the autocorrelation functions of a series can tell us whether there are significant dynamics at one lag (i.e., AR(1))or for more lags (e.g., an AR(3)). Again, this tells us something about the underlying nature of the data: how long does an event hold influence?
The substance of these temporal dynamics is even more important when thinking about the relationships between variables.
2 - Univariate Time Series Models
- Janet M. Box-Steffensmeier, Ohio State University, John R. Freeman, University of Minnesota, Matthew P. Hitt, Louisiana State University, Jon C. W. Pevehouse, University of Wisconsin, Madison
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UNDERSTANDING UNIVARIATE PROCESSES
The first class of time series models we investigate are univariate models called ARMA (autoregressive moving average) models. In the Appendix, we show how to gain significant insights into the dynamics of difference equations –the basis of time series econometrics – by simply solving them and plotting solutions over time. By stipulating a model based on our verbal theory and deriving its solution, we can note the conditions under which the processes we model return to equilibrium.
In the series of models discussed in this chapter, we turn this procedure round. We begin by studying the generic forms of patterns that could be created by particular datasets. We then analyze the data to see what dynamics are present in the data-generating process, which induce the underlying structure of the data. As a modeling process, ARMA models were perfected by Box and Jenkins (1970), who were attempting to come up with a better way than extrapolation or smoothing to predict the behavior of systems. Indeed, their method of examining the structures in a time series, filtering them from the data, and leaving a pure stochastic series improved predictive (i.e., forecasting)ability. Box-Jenkins modeling became quite popular, and as Kennedy notes,“for years the Box-Jenkins methodology was synonymous with time series analysis” (Kennedy, 2008, 297).
The intuition behind Box-Jenkins modeling is straightforward. Time series data redundent can be composed of multiple temporal processes.
Index
- Janet M. Box-Steffensmeier, Ohio State University, John R. Freeman, University of Minnesota, Matthew P. Hitt, Louisiana State University, Jon C. W. Pevehouse, University of Wisconsin, Madison
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3 - Dynamic Regression Models
- Janet M. Box-Steffensmeier, Ohio State University, John R. Freeman, University of Minnesota, Matthew P. Hitt, Louisiana State University, Jon C. W. Pevehouse, University of Wisconsin, Madison
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In Chapter 1 we discussed the distinction between strongly and weakly restricted time series models. A weakly restricted model uses techniques such as those we studied in Chapter 2, where one primarily infers from the data the structure of the data-generating process by assessing the AR and MA components of an observed univariate series. Extending the weakly restricted approach to multivariate models, which we do in subsequent chapters, leads to the use of vector autoregression (VAR) and error correction models (ECMs). Important modeling choices, such as how many lags of a variable to include, are inferred from the data rather than specified before the analysis. Recall as well that the quasi-experimental approach uses weakly restricted models, highlighting the problem of specification uncertainty.
In this chapter we discuss strongly restricted time series modeling, which assumes that we know much more about the functional forms of our data-generating process. Making these strong assumptions about a time series' functional form and proceeding directly to testing hypotheses about the relation-ships between variables encompass what we term the “time series regression tradition.” This approach is popular and widely used. It is appropriate whenever an analyst can comfortably and ably make the strong assumptions required for the technique.
We provide an overview of the basic components of time series regression models and explore tests for serial correlation in the residuals, which provide guidance to analysts regarding various types of serial correlation.
7 - Selections on Time Series Analysis
- Janet M. Box-Steffensmeier, Ohio State University, John R. Freeman, University of Minnesota, Matthew P. Hitt, Louisiana State University, Jon C. W. Pevehouse, University of Wisconsin, Madison
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The analysis of time series data is a vast enterprise. With this fact in mind, the previous chapters introduced the core concepts and analytic tools that form a foundational understanding of time series analysis. This chapter presents four more advanced topics: fractional integration, heterogeneity, forecasting, and estimating and modeling with unknown structural breaks. Although by no means an exhaustive list, the topics presented in this chapter represent concerns of the contemporary literature: they extend some of the previously discussed concepts, provide additional means of evaluating time series models, and are a means through which time series analysis can inform policy.
Fractional integration is an extension of the preceding discussion of unit roots and of tests for unit roots. The first few chapters assumed that our time series data was stationary, but it was subsequently presented that this may not necessarily be the case; as a result, tests for unit roots or an integrated series were presented in detail in Chapter 5. However, as intuition may suggest, it may not always be the case in practice that every series can be appropriately characterized as either stationary or integrated, as shocks may enter the series, persist for a nontrivial amount of time, and eventually dissipate. In such a case, the series is neither stationary nor integrated, because the shocks do not rapidly exit the series, nor do they persist indefinitely.
Preface
- Janet M. Box-Steffensmeier, Ohio State University, John R. Freeman, University of Minnesota, Matthew P. Hitt, Louisiana State University, Jon C. W. Pevehouse, University of Wisconsin, Madison
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Our work has several motivations. We think that longitudinal analysis provides infinitely more insight than does examining any one slice of time. As we show throughout the book, longitudinal analysis is essential for the study of normatively important problems such as democratic accountability and international conflict. Given the importance of dynamic analysis in answering new questions and providing new answers to old questions, we want to get more social scientists thinking in dynamic terms. Time series is one of the most useful tools for dynamic analysis, and our goal is to provide a more accessible treatment for this approach. We are also motivated by the burgeoning supply of new social science time series data. Sometimes this causes the opposite problem of too much data and figuring out how to analyze it, but that is a problem we gladly embrace. The proliferation of new social science data requires techniques that are designed to handle complexity, and time series analysis is one of the most applicable tools. The incorporation of time series analysis into standard statistical packages such as STATA and R, as well as the existence of specialized packages such as RATS and Eviews, provides an additional motivation because it enables more scholars to easily use time series in their work.
We have found over our years of teaching time series that, although many social science students have the brain power to learn time series methods, they often lack the training and motivation to use the most well-known books on the topic.
Dedication
- Janet M. Box-Steffensmeier, Ohio State University, John R. Freeman, University of Minnesota, Matthew P. Hitt, Louisiana State University, Jon C. W. Pevehouse, University of Wisconsin, Madison
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5 - Univariate, Nonstationary Processes: Tests and Modeling
- Janet M. Box-Steffensmeier, Ohio State University, John R. Freeman, University of Minnesota, Matthew P. Hitt, Louisiana State University, Jon C. W. Pevehouse, University of Wisconsin, Madison
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STATIONARY DATA
Thus far, all of our models assumed that our data are stationary. A stationary series does not have statistical properties that depend on time. All shocks and past values in a stationary series eventually lose their influence on the value of the variable today. A stationary stochastic process is defined such that
• A stochastic process is stationary if the mean and variance are constant overtime and covariance between two time points depends only on the distance of the lag between the two time periods and not on the actual time that the covariances are computed.
• In other words, if a time series is stationary, its mean, variance, and auto-covariance (at various lags) remain the same, no matter when we measure them.
Why should analysts care if variables are stationary? Econometric problems may occur when we run a regression with variables that are not stationary. For example, in the Box-Jenkins identification stage, because of nonstationarity, we may fail to diagnose a higher order AR process. We need to diagnose and correctly account for the characteristics of the data-generating process.
Several other issues arise with nonstationary data, which we discuss in this and the following chapters. At a basic level, nonstationary data violate the invertibility condition for the value of φ (the AR process in our ARMA model)and bias our estimate of φ (that is, the extent to which past values of the dependent variable influence the current value).
Appendix - Time Series Models as Difference Equations
- Janet M. Box-Steffensmeier, Ohio State University, John R. Freeman, University of Minnesota, Matthew P. Hitt, Louisiana State University, Jon C. W. Pevehouse, University of Wisconsin, Madison
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INTRODUCTION
The material in this appendix is aimed at readers interested in the mathematical underpinnings of time series models. As with any statistical method, one can estimate time series models without such foundational knowledge. But the material here is critical for any reader who is interested in going beyond applying existing “off the shelf” models and conducting research in time series methodology.
Many social theories are formulated in terms of changes in time. We conceptualize social processes as mixes of time functions. In so doing, we use terms such as trend and cycle. A trend usually is a function of the form α × t where α is a constant and t is a time counter, a series of natural numbers that represents successive time points. When α is positive (negative), the trend is steadily increasing (decreasing). The time function sin αt could be used to represent asocial cycle, as could a positive constant times a negative integer raised to the time counter: α(−1)t. In addition, we argue that social processes experience sequences of random shocks and make assumptions about the distributions from which these shocks are drawn. For instance, we often assume that processes repeatedly experience a shock, ∈t, drawn independently across time from a normal distribution with mean zero and unit variance.
Social processes presumably are a combination of these trends, cycles, and shocks.