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The evolution of models

Metamorphosis of Europe 9-10 December 2015 Vienna, Austria Session: Econometrics-Merging with Political Processes and going Global Professor El Thalassinos University of Piraeus, Greece Editor ERSJ: www.ersj.eu.

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The evolution of models

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  1. Metamorphosis of Europe9-10 December 2015Vienna, AustriaSession: Econometrics-Merging with Political Processes and going GlobalProfessor El ThalassinosUniversity of Piraeus, GreeceEditor ERSJ: www.ersj.eu “The dramatic expansion of econometric and quantitative-modeling techniques has been one of the most significant trends throughout the social sciences since the 1990s”. The bigbadwolf in internet Metamorphosis of Europe, December 2015 Vienna, Austria

  2. Metamorphosis of Europe, December 2015 Vienna, Austria

  3. Metamorphosis of Europe, December 2015 Vienna, Austria

  4. Metamorphosis of Europe, December 2015 Vienna, Austria

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  6. The evolution of models Rational-choice framework of neo-classical economics, Mathematical models of risk-analysis, Game theory models, have now spread into the ‘political’ domains of military conflict, state forms and ethno-linguistic identities. The resulting discipline—part economics, part statistics, part quantitative political science—now plays a central role not only in scholarship, but in formulating policy options for global institutions. Metamorphosis of Europe, December 2015 Vienna, Austria

  7. The fundamental objection Econometrics rests on prior assumptions and post-hoc hypotheses which remain systematically unexamined. The recent state-of-the-art development theory centers: firstly, on mobilizing researchers to reduce complex social phenomena to quantifiable, comparable series of data—the process of reduction itself usually involving value judgments which are scarcely questioned; secondly, on the models themselves.  Metamorphosis of Europe, December 2015 Vienna, Austria

  8. Critical attention Critical attention is given to: the theoretical assumptions underpinning the ‘hypothetical leap’ between the statistical result; the researcher’s ultimate explanation of it. Metamorphosis of Europe, December 2015 Vienna, Austria

  9. The impact in social sciences Social, historical and political determinants have been reduced to a set of numbers at the beginning of the process. At its end-point they return, in disembedded form; or embedded only in the commonsense—which is to say, ideological—assumptions of the researcher.  Metamorphosis of Europe, December 2015 Vienna, Austria

  10. The use in social sciences Econometrics must recognize its place, as a lower-order set of tools which may generate correlations or discrepancies whose elucidation requires more richly theorized—more conceptually and empirically developed—forms of enquiry. Econometric analysis is used more than with its process. Anyone can take any piece of information and manipulated to their use. But used correctly and presented properly, its a useful tool. Metamorphosis of Europe, December 2015 Vienna, Austria

  11. (+) or (-) tool of analysis Alternatives: Other techniques not related to econometrics; Market analysis; Experiment; Social models applied to groups; Neither of the above. Metamorphosis of Europe, December 2015 Vienna, Austria

  12. Laws and Limits of Econometrics I “General weaknesses and limitations of the econometric approach. A template from sociology is used to formulate six laws that characterize mainstream activities of econometrics and their scientific limits. Proximity theorems that quantify by explicit bounds how close we can get to the generating mechanism of the data and the optimal forecasts of next period observations using a finite number of observations”. PETER C. B. PHILLIPS, COWLES FOUNDATION PAPER NO. 1081 Metamorphosis of Europe, December 2015 Vienna, Austria

  13. Laws and Limits of Econometrics II The magnitude of the bound depends on the characteristics of the model and trajectory of the data. The future of econometrics by using advanced econometric methods interactively with a web browser. A fundamental issue that bears on all practical economic analysis is the extent to which we can expect to understand economic phenomena by the process of developing a theory, taking observations and fitting a model. An especially relevant question in practice is whether there are limits on how well we can predict future observations using empirical models that are obtained by such processes. Metamorphosis of Europe, December 2015 Vienna, Austria

  14. Laws and Limits of Econometrics III The true model for any given dataset is unknown. The formulated model is correct but still depends on parameters that need to be estimated from data. Data are scarce relative to the number of parameters that need to be estimated. Models have some functional representation that necessitates the use of nonparametric or semi-parametric methods. The empirical limitations on modeling are greater than in finite parameter models. Metamorphosis of Europe, December 2015 Vienna, Austria

  15. Laws and Limits of Econometrics IV All models are wrong. The models developed in economic theory are metaphors of reality, sometimes amounting to a very basic set of relations that are easily rejected by the data. Models continue to be used, often because they contain a kernel of truth that is perceived as an underlying ‘economic law’. It is advantageous to use this information in crafting an empirical model even though it is at best only approximately true. It is better than using an entirely unrestricted system or an arbitrarily restricted one. Metamorphosis of Europe, December 2015 Vienna, Austria

  16. Laws and Limits of Econometrics V From the Sargan Lecture, The Economic Journal, 113 March 2003 “Laws of Econometrics” These laws of econometrics are not intended as universal truths. They purport to express the essence of what is being done in econometrics and to characterize some of the difficulties that the econometric approach encounters in explaining and predicting economic phenomena. Related views about modeling have been suggested in Cartwright (1999) and Hoover (2001). Metamorphosis of Europe, December 2015 Vienna, Austria

  17. Laws and Limits of Econometrics VI Cartwright: Models can be interpreted as machines that generate laws (so-called nomological machines). The laws that may emerge from modeling are analogous to the morals that we draw from storytelling fables. Hoover: Economic modeling is useful to the extent that it sheds light on empirical relationships. Formal laws seems to do nothing for economics – ‘even accumulated falsifications or anomalies do not cause scientists to abandon an approach unless there is the prospect of a better approach on offer’. Metamorphosis of Europe, December 2015 Vienna, Austria

  18. Laws and Limits of Econometrics VII Rissanen (1986, 1989): Argues against the concept of a true model and sees statistics as a ‘language for expressing the regular features of the data’. Some proximity theorems that measure how close an empirical model can get (in terms of its likelihood ratio) to the true model in some parametric family. Metamorphosis of Europe, December 2015 Vienna, Austria

  19. Laws and Limits of Econometrics VIII Ploberger and Phillips (2001, 2002): The bounds in these proximity theorems depend on the data as well as on the model being used. The bounds are greater for trending data than when the data are stationary. These theorems allow for finite parameter families and families with local misspecifications. Modeling algorithms allow for gross misspecification within family groups. Proximity theorems for prediction are also provided in this approach, quantifying limits on empirical forecasting capability that are relevant in empirical work where specification is suspect. Metamorphosis of Europe, December 2015 Vienna, Austria

  20. The Six Laws of Econometrics I The six laws of econometrics are not intended as universal scientific truths. They are laws that characterize the activities and limitations of econometrics. Metamorphosis of Europe, December 2015 Vienna, Austria

  21. First law C29 Laws and Limits of Econometrics, the Royal Economic Society 2003 1. Some methods work, some don’t Econometrics has in large part been concerned with the development of statistical machinery that is appropriate for economic models and economic data. The process occurs because sometimes the usual statistical methods work well and sometimes they do not. The process is well illustrated by the steady progression of modeling practice and econometric methodology. (Fisher,1907; Koopmans, 1937; Tinbergen, 1939; Sargan, 1958 and 1959; Hansen 1982; Johansen, 1988; Phillips and Hansen, 1990; Jeganathan, 1997; Kim and Phillips, 1999; Robinson and Marinucci, 1998 and 2001; Shimotsu and Phillips 2002 and more) Metamorphosis of Europe, December 2015 Vienna, Austria

  22. Second law 2. It’s different on infinite dimensional spaces Econometrics is about trying to achieve generality wherever that is possible with regard to aspects of a model about which there is little prior knowledge. On the other hand, where a model connects most closely with some underlying economic hypothesis, we often seek to retain specificity through direct parameterization. These considerations have led to a flowering of work on nonparametric and semiparametric estimation. (Hardle and Linton, 1994; Baillie, 1996; Linton, 1996; Horowitz, 1998; Xiao and Phillips 1998 and 2002; Bandi and Phillips 2002 and more) Metamorphosis of Europe, December 2015 Vienna, Austria

  23. Third law 3. Unit roots always cause trouble Unit roots are the new hill people of econometrics. Unit roots inevitably cause trouble because of the nonstandard limit distributions. The discontinuities that arise in the limit theory as the autoregressive parameter passes through unity. (Chan and Wei, 1987; Park and Phillips, 1988 and 1989; Sims and Uhlig, 1991; Kim, 1994; Phillips and Ploberger, 1996; Phillips and Xiao,1998; Phillips et al., 2001; Phillips and Sul, 2002 and more) Metamorphosis of Europe, December 2015 Vienna, Austria

  24. Fourth law 4. Cross section dependence also causes trouble It is convenient and has for long been common econometric practice to assume cross section independence in panel modeling up to a time specific effect. Cross section dependence is often to be expected in microeconomic applications of firm and individual behaviour. It is almost always present in regional or cross country macroeconomic panels. It is widely acknowledged as a major characteristic of financial panels. Cross section dependence is a rapidly growing field of study in panel data analysis. There are many limitations to the models being used and unresolved difficulties for empirical workers. A primary difficulty arises because there is no natural ordering of cross section data, making it hard to characterise and model dependence across section. (Maddala, 1993; Stock and Watson, 1998 and 1999; Bai and Ng, 2001; Moon and Perron, 2001; Phillips and Sul, 2002 and more) Metamorphosis of Europe, December 2015 Vienna, Austria

  25. Fifth law 5. No one understands trends In spite of the importance of trends in macroeconomic research, particularly in the study of economic growth and growth convergence, economic theory provides little guidance for empirical research on the formulation of trend functions. This partly explains the rather impoverished class of trend formulations that are in use in econometrics. Most commonly, these are polynomial time trends, simple trend break polynomials, and stochastic trends, which include unit root models, near unit root models and fractional processes. When the focus is on trend elimination (for instance, in the extraction of the cyclical component of a series for studying business cycles), smoothing methods are popular. (Whittaker, 1923; Schoenberg, 1964; Wahba, 1978; Hodrick-Prescott, 1980; Baxter and King, 1999; Corbae et al., 2002 and more) Metamorphosis of Europe, December 2015 Vienna, Austria

  26. Sixth law 6. Spurious regression has become universal and carries a pejorative connotation that generally makes empirical researches anxious to show that their relationships are validated by some procedure such as a test for cointegration The deterministic trend functions can be used as a coordinate system for measuring the trend behaviour of an observed variable. A set of functions can be used as a coordinate basis for studying another function. Continuous stochastic processes such as Brownian motion and diffusions also have representations in terms of functions with coefficients that are random variables rather than constant Fourier coefficients. In a similar way, we can write trending data in terms of coordinates comprised of other trends, like time polynomials, random walks or other observed trends. (Hendry, 1980; Phillips, 1998; Clements and Hendry, 1999 and 2001; Phillips, 2002 and more) Metamorphosis of Europe, December 2015 Vienna, Austria

  27. Concluding remarks Be aware of problems and limitations in Econometrics. Use the best possible methodology applied in your case. Compare and refer to other similar studies. Be careful in interpretations and explanation of results. Every science has its own gray holes. The simple is beautiful. Data collection is a very difficult task. Metamorphosis of Europe, December 2015 Vienna, Austria

  28. Metamorphosis of Europe9-10 December 2015Vienna, AustriaSession: Data collectionProfessor El ThalassinosUniversity of Piraeus, GreeceEditor ERSJ: www.ersj.eu “I define statistical data editing (SDE) as those methods that are used to edit (i.e., clean-up) and impute (fill-in) missing or contradictory data. The end result of SDE is data that can be used for intended analytic purposes. These include primary purposes such as estimation of totals and subtotals for publications that are free of self-contradictory information”. STATE OF STATISTICAL DATA EDITING AND CURRENT RESEARCH PROBLEMS William E. Winkler Metamorphosis of Europe, December 2015 Vienna, Austria

  29. Initial problems for “big data” A set of doubts regarding “big data” collection: “Big data” is bringing statistics out into the mainstream (even if they don’t call it statistics) and it creating lots of opportunities for people with statistics training. Problems with respect to hardware infrastructure and algorithm design. “Small big data” is the dataset that is collected by an individual whose data collection skills are far superior to his/her data analysis skills. The person who collected the data is often not qualified/prepared to analyze it. If the data collector didn’t arrange beforehand to have someone analyze the data, then they’re often stuck. The grant that paid for the data collection didn’t budget (enough) for the analysis of the data. Metamorphosis of Europe, December 2015 Vienna, Austria

  30. Statistical Data Editing (SDE) SDE can be used in all phases of survey processing: frame development; form design; proposed analytic purposes for which the data are collected; quality assurance. The main goal of SDE might be improved procedures and greater automation to enhance the ability of survey managers and analysts to provide accurate published estimates and micro-data. Metamorphosis of Europe, December 2015 Vienna, Austria

  31. SDE subcategories 1. Fellegi-Holt (FH) methods and systems: FH systems are based on the Fellegi-Holt model (1976) of editing and typically add various options for imputation. Data files are edited using custom software that incorporates if-then-else rules developed by subject-matter specialists. If the specialists are unable to develop the full logic needed for the edit rules, then the subsequent edit software can be in error. 2. General methods and systems: General methods are all other methods. Metamorphosis of Europe, December 2015 Vienna, Austria

  32. FH model and systems I FH provided the theoretical basis of such a system that had three goals that are: 1. The data in each record should be made to satisfy all edits by changing the fewest possible variables (fields). 2. Imputation rules should derive automatically from edit rules. 3. When imputation is necessary, it should maintain the joint distribution of variables. The key to the FH approach is to understand the underpinnings of goal 1. Metamorphosis of Europe, December 2015 Vienna, Austria

  33. FH model and systems II The FH ideas give formal ways of development that greatly facilitate creating sets of edits. The key features of a Fellegi-Holt system are: 1. Edit restraints reside in easily modified tables. 2. The logical consistency of the entire edit system can be checked prior to the receipt of data. 3. The main logic resides in reusable mathematical routines. 4. In one pass through the system, records satisfy edits. Implementations of FH systems have typically either been for discrete data (e.g., categorical) to which arbitrary edits are applied or for continuous data to which ratio or linear inequality edits are applied. Metamorphosis of Europe, December 2015 Vienna, Austria

  34. Misspecifications in data gathering Three research areas that have arisen in recent years and depend heavily on record linkage ideas. The first is microdata confidentiality and associated re-identification methods. The second is analytic linking as introduced by Scheuren and Winkler (1993, 1997). Analytic linking refers to the merging and proper analysis of data (quantitative and discrete) taken from two or more files. The analysis is intended to adjust for the biases due to linkage error. The third presents some of the methods of information retrieval and machine learning as used by computer scientists in web search engines and data mining applications. Metamorphosis of Europe, December 2015 Vienna, Austria

  35. Two sets of data Basic research problems in for comparison and computing weights: The basic research problems have been open since the work of Newcombe et al. (1959) and Fellegi and Sunter (1969). Partial progress in solving the problems has occurred. The major difficulties in all situations have been determining how identifying information can be used. What the relative value of a field is in matching in comparison with other fields. Metamorphosis of Europe, December 2015 Vienna, Austria

  36. Basic research problems I When can frequency-based matching improve over simple agree/disagree matching? (Newcombe et al.,1959; Fellegi and Sunter, 1969; Winkler, 1988 and1989) 2. What is the best method for estimating parameters under conditional independence? (Winkler, 1988 and1990; Nigam et al.,1999) Metamorphosis of Europe, December 2015 Vienna, Austria

  37. Basic research problems II 3. When does accounting for dependencies help in matching? (Smith and Newcombe,1975; Winkler,1993 and 1994; Thibaudeau,1993; Armstrong and Mayda,1993; Larsen and Rubin,1999; Gill, 1999) 4. What are (suitable) ways of estimating error rates? (Winkler and Thibaudeau, 1991; Rubin and Stern, 1993; Scheuren and Winkler, 1993; Winkler,1994; Belin and Rubin,1995; Larsen and Rubin, 1999) Metamorphosis of Europe, December 2015 Vienna, Austria

  38. Advanced research problems I . Methods and underlying models that are closely related to the basic ideas of record linkage. 1. Confidentiality of microdata is most closely related because record linkage methods can be used for evaluating the re-identification risk in public-use files. Since the quantitative data in a public-use file are typically masked, new metrics for comparing quantitative data can yield higher re-identification rates. 2. Analytic Linking is the methodology (Scheuren and Winkler 1997) for using not directly comparable data items to improve matching and to account for the effect of matching error in analyses. 3. Data mining and some models for information retrieval in computer science use Bayesian networks for classifying documents using free-form textual information. Metamorphosis of Europe, December 2015 Vienna, Austria

  39. Advanced research problems II 1. Confidentiality There is substantially increased need to supply researchers with large, general-purpose public-use files that can be used for a variety of analyses. Balancing the analytic needs are the requirements that agencies not release individually identifiable data. If a public-use file is created, then agencies must determine if the file meets analytic needs and is confidential. (Kim, 1986 and1989; Fuller 1993; Tendick and Matloff, 1994; Kim and Winkler,1995; De Waal and Willenborg, 1996 and 1998; Makov and Sanil, 1997; Winkler, 1998; Sweeney, 1999; Mateo-Sanz and Domingo-Ferrer, 1998; Fienberg, Fienberg, Makov, and Steele, 1998) Metamorphosis of Europe, December 2015 Vienna, Austria

  40. Advanced research problems III 2. Analytic Linking Researchers often have the need to analyze large amounts of data that result from the merger of two or more administrative files in which unique identifiers are unavailable. Scheuren and Winkler (1993) showed how regression analyses might be adjusted for biases due to linkage errors. In the simplest situation of two variables, the dependent variable might be taken from one file and the independent variable from another file. (Besag et al., 1974 and1986 and1995; Geman and Geman, 1984; Belin and Rubin, 1995; Scheuren and Winkler, 1997; Van Dyk,1999; Winkler, 1999) Metamorphosis of Europe, December 2015 Vienna, Austria

  41. Advanced research problems IV 3. Data Mining Machine learning algorithms that employ Bayesian networks are tools being applied to classify text into different groups. Bayesian networks are one of the standard tools in data mining. They are also used for information retrieval methods such as used in some of the web search engines. (Fellegi and Sunter,1969; Winkler, 1988, 1989 and 1993; Nigam et al., 1999; Larsen and Rubin,1999). Metamorphosis of Europe, December 2015 Vienna, Austria

  42. Conclusion One conclusion we can draw is that we need to get more statisticians out into the field both helping to analyze the data. Designing good studies so that useful data are collected in the first place (as opposed to merely “big” data). There aren’t enough of us on the planet to fill the demand. Come up with more creative ways to get the skills out there without requiring our physical presence. Metamorphosis of Europe, December 2015 Vienna, Austria

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