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Progressive Modeling of Macroeconomic Time Series The LSE Methodology

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• Grayham E. Mizon  

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Econometric models, large and small, have played an increasingly important role in macroeconomic forecasting and policy analysis. However, there is a wide range of model types used for this purpose, including simultaneous-equation models in either reduced or structural form, vector autoregressive models (VAR), autoregressive distributed-lag models, autoregressive integrated moving-average models, leading-indicator models, and error-correction models (ECM). Hendry, Pagan, and Sargan (1984) discuss a typology for dynamic single-equation models for time-series variables, and Hendry (1994) presents a typology for the various types of dynamic model used in the analysis of systems of equations. There is also a wide range of views about the appropriate way to develop and evaluate models. Sims (1980, 1992) advocates the use of VAR models, which can accurately represent the time-series properties of data, while eschewing the reliance on “incredible dentifying restrictions” that characterizes the use of simultaneous equation models of the structural or Cowles Commission type. The potential value of structure (loosely defined) within the context of VAR models has led to the development of structural VAR models, and Canova (1995) provides a recent review of this literature. Leamer (1978, 1983), on the other hand, has been critical of the use of non-Bayesian models that do not analyze formally the role and value of a priori information, especially when there is no checking of model sensitivity. Summers (1991), though aware of the important developments made in theoretical statistics and econometrics in this century, argues that too much emphasis is placed on the technical aspects of modeling and not enough on the real issues that are concerned with the analysis of well-established and fundamental relationships between economic variables. One approach to modeling that does not overemphasize the role of model evaluation and statistical technique is that associated with real business cycle analysis and the calibration of economic theory, rather than its evaluation. Kydland and Prescott (1982, 1995) have been pioneers in this field, and Canova, Finn, and Pagan (1994) provide a critique.

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Mizon, G.E. (1995). Progressive Modeling of Macroeconomic Time Series The LSE Methodology . In: Hoover, K.D. (eds) Macroeconometrics. Recent Economic Thought Series, vol 46. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-0669-6_4

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SPSS provides a comprehensive set of flexible tools that can be used to accomplish various data analysis tasks and has the option of using drop-down menus or scripting. It is well-suited to accommodate different exploration strategies such as surveys and experiments in diverse fields of enquiry. SPSS is among the used for statistical analysis in business, government, health, education, research and academic organisations.

SPSS vs Stata: Which is right for you? 

SPSS is similar to Stata in that you can either use drop-down menus or use coding to do analysis. SPSS deals better with very large datasets but is more expensive than Stata. Stata generally has better support available from various sources such as the statalist forum, documentation online and built-in help. You can request a license and instructions on how to download and install SPSS by emailing  [email protected] .

SPSS  vs R or Python 

SPSS is easier to learn than R and Python, providing easier access to running some types of analysis. It is still a popular choice in the social sciences, but R and Python are catching up. R and Python are open-source and freely available, whereas SPSS needs a licence, which can be expensive. R and Python are better options for data visualisation and machine learning but you will have to learn to code in order to fully utilise them.

We also have workshops and self-study courses in  Stata ,  Python  and  R . See below if you're not sure which is right for you.

SPSS fundamentals workshop series

The Digital Skills Lab is currently running a 4-part SPSS fundamentals workshop series. Workshops will run throughout the year. Click on the link below to book your place or express an interest so that you are notified as soon as workshops are available to book.

Workshops will take place on campus in LRB.R.08 on the lower ground floor of the Library.

The SPSS fundamentals workshop series teaches you the basics of using SPSS for statistical analysis. At the end of this SPSS series, you will be able to import, manage, and explore data in SPSS, as well as perform various statistical tests. Our SPSS Fundamentals workshop series is ideal for those with no or very little prior experience of using SPSS, or those looking for a refresher.

Click on the link below to check availability and book your place:

SPSS Fundamentals Workshop Series

SPSS 1: Foundations of Data Management

Get started with the SPSS interface and how to use it as a data management tool for your analysis. We will be loading in datasets, making and recoding variables and other pre-analysis steps.

You will learn how to:

• Work with the SPSS interface
• Import data from csv and Excel files
• Process imported datasets
• Recode variables
• Compute new variables

SPSS 2: Data Exploration

Learn how to explore your data pre-analysis in SPSS. We will be running descriptive statistics and making data visualisations to review and explore our data.

• Explore categorical variables with frequencies and bar charts
• Explore continuous variables with descriptive statistics and box plots
• Explore two continuous variables with correlations and scatterplots

SPSS 3: Introduction to inferential statistics 

Learn how to get started with inferential statistics in SPSS. We will be performing some fundamental statistical tests including chi-square, t-tests, and ANOVAs. You will also try and form some of your own ideas and hypothesis from data, and use that to understand the statistical outputs.

• How to perform cross-tabulation and chi-squared tests
• How to perform different types of t-tests
• How to perform different types of ANOVAs

SPSS 4: Regressions in SPSS

Learn how to run linear regressions in SPSS and how to interpret the results. You will also try and form some of your own ideas and hypothesis from data, and use that to understand the statistical outputs.

• How to perform and interpret simple linear regression
• How to perform and interpret multiple linear regression

Other support

The  Department of Methodology  also has some  online tutorials in SPSS  that were produced in 2011 that students have found useful along with a  YouTube training channel . The Department of Methodology also provides training for PhD and MSc students as well as staff in the design of social research and in qualitative and quantitative analysis. Information on this can be found on their  Methods training page .

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27 August 2024

For media and investors only

Issued: 27 August 2024, London UK

Statement: Zantac (ranitidine) litigation – Delaware Supreme Court to review Superior Court’s Daubert decision

GSK plc (LSE/NYSE: GSK) welcomes today’s decision by the Delaware Supreme Court that it will review the Delaware Superior Court’s decision allowing the introduction of plaintiffs’ expert evidence at trial. Interlocutory reviews are granted in exceptional circumstances, and GSK is pleased that the Supreme Court is of the view that such circumstances are present here.

The scientific consensus remains that there is no consistent or reliable evidence that ranitidine increases the risk of any cancer. Since 2019, there are 16 epidemiological studies looking at human data regarding the use of ranitidine, including outcomes for more than 1 million patients using ranitidine, supporting this consensus.

GSK is committed to vigorously defending itself and managing this litigation in the best interests of the Company and its shareholders. The Delaware litigation will progress in parallel with the Delaware Supreme Court review. Alongside review by the Delaware Supreme Court, the Company will press additional defenses in the litigation, including failure to provide proof of use and proof of diagnosis requirements recently ordered by the Court.

Notes to Editors

* The Daubert standard, established in the U.S. Supreme Court case Daubert v. Merrell Dow Pharmaceuticals, Inc. 509 US 579 (1993) provides criteria for evaluating whether expert testimony is admissible under Federal Rule of Evidence 702. Under Rule 702 and Daubert, an expert may offer testimony if he or she is qualified by knowledge, education, training or experience in a given area and the testimony offered is reliable, relevant and helpful to the jury. In applying the Daubert standard, the Court acts as a gatekeeper, ensuring that expert opinions meet certain standards for reliability and that speculative or unreliable opinions are not presented to the jury.  In Daubert, the Supreme Court identified four factors to guide assessment of an expert’s methodology: (1) whether the expert’s methodology has been tested or is capable of being tested; (2) whether the theory or technique used by the expert has been subjected to peer review and publication; (3) whether there is a known or potential error rate of the methodology; and (4) whether the technique has been generally accepted in the relevant scientific community. The Daubert standard is applicable to expert testimony in all federal cases. Many states also have adopted standards identical to the federal Daubert standard.

The term “Daubert Standard” comes from the United States Supreme Court case: Daubert v Merrell Dow Pharmaceuticals Inc 509 US 579 (1993).

GSK is a global biopharma company with a purpose to unite science, technology, and talent to get ahead of disease together. Find out more at gsk.com.

Cautionary statement regarding forward-looking statements

GSK cautions investors that any forward-looking statements or projections made by GSK, including those made in this announcement, are subject to risks and uncertainties that may cause actual results to differ materially from those projected. Such factors include, but are not limited to, those described under Item 3.D “Risk factors” in GSK’s Annual Report on Form 20-F for 2023, and GSK’s Q2 Results for 2024.

Media contacts

Access the contact details of our media relations teams at GSK

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We are a global biopharma company with a purpose to unite science, technology and talent to get ahead of disease together

Event Categories: BSPS Choice Group Conjectures and Refutations Popper Seminar Sigma Club

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AI in public policy: opportunities and challenges

5 november, 6:30 pm – 8:00 pm, event navigation.

• Lucy McDonald (King’s College London): ‘Power and Romantic Relationships’ »

THIS EVENT IS PART OF THE LSE PUBLIC LECTURE SERIES.

In a world increasingly shaped by digital transformation, AI and data science present new opportunities to change policymaking in nearly all areas of policy. Yet the capabilities of these emerging technologies are still unfolding and need to be better understood, both in terms of their benefits and their limitations.

This events launch the publication of the most recent issue of the  LSE Public Policy Review , which brings together contributions from a range of disciplines – from philosophy to statistics, government and law – to reflect together on future directions, applications, and consequences of the use of AI in public policies. Join our panellists as they discuss how emerging technologies can transform evidence-based policy development through their analytical capabilities, predictive powers, and real-time monitoring, while also bringing questions around regulation, transparency, accountability and ethics to the fore.

Meet our speakers and chair

Helen Margetts ( @HelenMargetts ) is Professor of Society and the Internet at the University of Oxford and the Programme Director for Public Policy at The Alan Turing Institute for Data Science and Artificial Intelligence.

Andrew Murray is Professor of Law at LSE and a Fellow of the Royal Society of Arts (FRSA). He is the Academic Director of LSE’s Law, Technology and Society research group and is Academic Director of LSE Online. He has been since 2014 a visiting Professor at the Amsterdam Law and Technology Institute and was in Spring 2015 and Spring 2017 a visiting Professor at the Paris Institute of Political Science (Sciences Po).

Kate Vredenburgh is Assistant Professor in the Department of Philosophy, Logic and Scientific Method at LSE. She works in the philosophy of social science, political philosophy, and the philosophy of technology. Much of her research and teaching interact with other disciplines, such as economics, sociology, and computer science.

Ken Benoit ( @kenbenoit ) is Director of the Data Science Institute at LSE and Professor of Computational Social Science in the Department of Methodology.

More about this event

This event will be available to watch on LSE Live. LSE Live is the new home for our live streams, allowing you to tune in and join the global debate at LSE, wherever you are in the world. If you can’t attend live, a video will be made available shortly afterwards on  LSE’s YouTube channel .

The  LSE School of Public Policy  ( @LSEPublicPolicy ) equips you with the skills and ideas to transform people and societies. It is an international community where ideas and practice meet. Their approach creates professionals with the ability to analyse, understand and resolve the challenges of contemporary governance.

The  LSE Public Policy Review  is a journal published by LSE Press ( @LSEPress ) and hosted by the School of Public Policy.

The  Data Science Institute  ( @LSEDataScience ) is an interdisciplinary institute established to foster the study of data science and new forms of data with a focus on their social, economic and political aspects.

LINK TO THE EVENT PAGE.