The effective incorporation of research in undergraduate econometrics

Summary

This chapter examines the integration of research into undergraduate econometrics teaching through a focus on replication and reproduction (R&R) of published studies. Rather than limiting students to the passive reception of research findings, the R&R approach fosters active engagement by requiring them to work directly with real data and methods in order to reproduce results and interrogate techniques. The discussion situates R&R within broader debates on the role of research in teaching, highlighting its potential to make scholarship more accessible and relevant to students. Practical case studies and classroom-ready materials from the Economics Network Ideas Bank are presented, illustrating how this approach can enhance learning, develop quantitative skills, and build student confidence. Finally, the chapter shows how R&R aligns with wider educational goals, including employability, self-efficacy, and sustained engagement.

1. Introduction

The inclusion of research within teaching has received much attention in higher education. This is reflected in the frequent use of ‘research-led teaching’ as both a strategic goal and a marketing tool for universities. In this chapter, we explore how research can be incorporated into the undergraduate teaching of econometrics.

While this chapter touches on a range of issues, its central theme is the promotion of replication and reproduction (R&R) as a means of incorporating research into the teaching of econometrics. Although R&R is not presented here as a novel concept, given its presence in textbooks and the availability of data from published studies in software packages[2], the chapter offers a fresh contribution by:

1. synthesising debates on the embedding of research in teaching;
2. collating discussions on the nature of R&R;
3. providing linked practical examples or resources; and
4. assessing the potential pedagogical value of R&R.

One key benefit we highlight is the ability of R&R to counter a potential ‘distancing’ effect of research-led teaching where students passively view the work of others. In contrast to this, R&R encourages active engagement as students work directly with data and retrace the steps of published studies thereby making them participants, rather than observers, in the research process. This supports a transition from being recipients of research findings to active developers of research understanding.

Before going further, it is useful to refer to Cook et al. (2025) who propose a highly simplified approach to the teaching introductory econometrics within an R&R framework. While that study shares the present chapter’s replication-based approach, its use of simplification is not required for the present study. As we shift focus from introductory econometrics to the higher-level econometrics considered in this chapter, students’ existing knowledge can now support deeper engagement with published research and the complexities of real-world data.[3]

The chapter begins in Section 2 with a review of key literature on embedding research in teaching, outlining both its benefits and the different ways in which it can be approached. Section 3 explores the concept of R&R more fully, discussing its role in the academic research arena and its growing use as a teaching method. Section 4 then turns to practical application, presenting three econometrics case studies from the Economics Network’s Ideas Bank (Cook and Watson, 2025a, b, c) based on research published in Oxford Bulletin of Economics and Statistics, Journal of Econometrics and Energy Economics. Section 5 connects R&R to wider themes in educational research, including cognitive load theory, self-efficacy, employability, and the development of quantitative skills. Section 6 concludes with final reflections.

2. Incorporating research in teaching

Interest in ‘incorporating research in teaching’ (IRT) has sparked a wide-ranging literature exploring the topic from multiple angles.[4] Prominent within this literature is the Boyer Commission (1998) which advocated embedding research in undergraduate education. Subsequent work has reinforced this view, highlighting how IRT can help students develop broad, transferable skills (Brew 2013, Ruth et al. 2023, Wood 2003). A growing body of studies also point to more specific benefits, particularly around employability (Boyd et al., 2010; Brew, 2013; Bowyer and Akpinar, 2022). Others link IRT to enhanced student engagement (Boyd et al., 2010), academic performance (Parker, 2008), well-being (Walkington and Ommering, 2022) and greater self-efficacy, alongside more positive attitudes towards research (Wessels et al., 2021). These advantages are echoed in the case studies compiled by Ansell and Marshall (2017) which showcase a range of practical interventions and outcomes.

The discussion so far makes a strong case for IRT. A key issue that follows is the relationship between the research activity of instructors and the effectiveness of IRT. Numerous questions arise, including: Is it sufficient for instructors to have only a general awareness of research? Is IRT more effective when instructors are research active? Does IRT become even more beneficial if instructors draw directly on their own research? This leads naturally to consideration of the concept of pedagogical content knowledge (PCK), introduced by Shulman (1986), which refers to the integration of subject expertise and teaching skill.[5] PCK highlights the value of research for instructors: staying current with developments in the field ensures teaching content is relevant and up to date, while pedagogical expertise supports effective delivery and assessment. However, it could be argued that being ‘research aware’ alone is not enough. Several studies suggest this, pointing to the benefits of direct involvement in research and instructors drawing on their own research. For example, Clark and Hordosy (2019) find that instructors who use their own research generate greater enthusiasm, which in turn boosts student engagement. Healey et al. (2010) report that students see staff research as beneficial to their own development, especially in building research skills. Lindsay et al. (2002) similarly highlight the enhanced credibility, enthusiasm and depth that research-active lecturers bring to the classroom. Taken together, these findings point to the advantages of instructors being research-active, and support the call of Vicens and Bourne (2009) for lecturers to be ‘…shameless in bringing your research interests into your teaching’.

Although the benefits of IRT are well documented, this may appear surprising given the often uneasy relationship between teaching and research in higher education (Hattie and Marsh, 1996; Schapper and Mayson, 2010; Macfarlane, 2011; Bamber et al., 2023). Still, with growing evidence in its favour, attention turns to how IRT can be implemented in practice. As Brew and Mantai (2017) note, a wide range of approaches is available. One influential framework is the 2×2 model proposed by Healey and Jenkins (2009) which classifies IRT along two dimensions: whether the focus is on research content or process, and whether students are treated as an audience or active participants. This produces four modes of engagement: research-led (content, audience), research-oriented (process, audience), research-based (process, participation), and research-tutored (content, participation). Drawing upon Healey and Jenkins (2009), a summarised version of this framework is presented in Table One below. Importantly, this model highlights a key limitation of research-led teaching: students may remain passive consumers of the findings of others.

Table One: The 2x2 classification of Healey and Jenkins (2009)

Levy and Petrulis (2012) offer a complementary 2×2 model distinguishing between inquiry into existing versus new knowledge, and between tutor- versus student-led approaches. Other scholars extend these typologies. For example, Brew and Mantai (2017) critique such frameworks and propose a ‘wheel’ model focused on context and learning outcomes. Continuing with alternative terminology, Ansell and Marshall (2017) prefer the term ‘research-informed teaching’, while Cook and Watson (2023) employ ‘research-driven teaching’ to reflect a more comprehensive approach. Centred on R&R, the model of Cook and Watson (2023) integrates both the content of research and the methods used in its production. Crucially, it enables students to engage as recipients of research knowledge and as active participants in research activity, while exposing them simultaneously to research content and methods.

This chapter champions the ‘research-driven’ model. By revisiting published empirical studies, students work with original data, to reproduce and replicate results, and to explore alternative approaches. This shifts them from passive observers to active participants in research. In doing so, students engage with all four quadrants of Healey and Jenkins’ framework. The approach is illustrated in case studies by Cook and Watson (2025a, b, c), examined in Section 4. Before that, however, Section 3 takes a closer look at the concept of R&R, discussing its associated terminology, prominence in research, and adaptation as a teaching tool.

3. Replication and reproduction

The reproduction and replication of research findings have a long history in academic research, with renewed attention sparked by the widely reported ‘replication crisis’. Dubbed ‘repligate’ by Machery and Doris (2017), numerous papers are prominent in the literature associated with this crisis (see, inter alia, Ioannidis, 2005; Open Science Collaboration, 2015; Nosek et al. 2015, 2022). As noted by Machery (2020), an increased interest in the replicability of research findings sparked by the replication crisis has spread across disciplines since these debates (see, inter alia, the following studies and their references: Persaud et al., 2024; Rode et al., 2024).

Economics is clearly one discipline where interest in R&R is prominent. While this is illustrated, for example, by the relatively recent research of Camerer et al. (2016) and Chang and Li (2017), which highlight the challenges of reproducing published findings, there is also a collection of work pre-dating this. As Dewald et al. (1986) note, calls for greater transparency go back, at least, to Frisch’s (1933) editorial in the first edition of Econometrica where the publication of data used in research was emphasised. Notable further examples of interest in R&R include the special edition of Oxford Economic Papers where R&R and more general re-evaluation of previous research were presented (Hendry and Morgan, 1989; Spanos, 1989; Thomas, 1989; Wulwick, 1989) and the ‘Data Storage and Evaluation Project’ launched in 1982 by the Journal of Money, Credit and Banking (Dewald et al., 1986). This interest has continued with, inter alia, the special issue of Energy Economics dedicated to replication (Tol, 2019) and the contributions to R&R in the American Economic Review Papers and Proceedings of 2017 (e.g., Berry et al., 2017; Chang and Li, 2017; Duvendack et al., 2017). An ongoing commitment to R&R is also apparent with, for example, the Journal of Applied Econometrics introducing a dedicated replication section (Pesaran, 2003) which built upon the momentum resulting from its prior creation of a data archive in 1994. In addition, an alternative perspective on R&R within Economics is provided by the Journal of Applied Econometrics Experiment (see Magnus and Morgan, 1997), which examines the robustness of, and variability in, empirical findings through a field experiment.

Reflecting on the R&R literature in economics, two key issues stand out as warranting further discussion.[6] The first concerns the extent to which replication exercises are actually undertaken and the factors that encourage, or discourage, this activity. While much attention has been paid to whether findings can be reproduced, less is known about the extent to which R&R is actually undertaken, with Berry et al. (2017) noting the difficulty in estimating the volume of replication studies being published. With regard to factors encouraging or discouraging R&R studies, Chang and Li (2017) explore ways to make replication in economics more feasible.

The second issue relates to the terminology surrounding R&R, which is marked by variation and inconsistency not only in economics but across disciplines. As the National Academies of Sciences, Engineering and Medicine report (NASEM, 2019) observes, the lack of standardisation regarding the use of ‘replication’ and ‘reproduction’ complicates efforts to assess research credibility. This problem is echoed in the work of Nosek and Errington (2020) and Machery (2020)– the latter suggesting that the traditional distinction between direct and conceptual replication should be dropped in favour of a ‘resampling’ approach.

Economics contributes significantly to this terminological variation. Barba (2018), in a review of economics papers, highlights that many authors use ‘reproduce’ and ‘replicate’ interchangeably. Clemens (2017) also notes the variation in terminology employed in relation to R&R within economics, while providing further classifications in an attempt to introduce clarity and consistency. More specifically, Clemens (2017) defines replication as including both ‘verification’ and ‘reproduction’ tests, while robustness is taken to include reanalysis and extension. This work follows Pesaran (2003) where a distinction is made between ‘narrow sense’ replication (reproducing original results using the same data) and ‘wide sense’ replication (testing findings using new data). These distinctions mirror NASEM’s (2019) proposal: reproducibility involves consistent results using the same data and methods, whereas replicability involves consistent results across independent studies using new data. In short, while extensive terminology exists with a variety of terms employed, a long-standing and continuing interest in R&R within economics is apparent.

So far, our discussion of R&R has focused on its place within the research community where revisiting findings is an activity for researchers. However, R&R has also gained attention in teaching contexts, as shown in recent studies by Ball et al. (2022), Janz (2016), Stojmenovska et al. (2019) and Smith et al. (2021). Predating these more recent studies, an earlier call to employ R&R within the teaching of Economics comes from Pesaran’s (2003, p.11) statement when introducing the Replication Section of the Journal of Applied Econometrics: ‘We also hope that this [the replication section] will encourage students and teachers of applied econometrics to replicate the published work in the classroom’. This classroom use of R&R has been taken a step further by Wagge et al. (2019) and the Collaborative Replications and Education Project (CREP) where replication is encouraged for assessment design.

Building on this momentum, Cook and Watson (2023) extend the conversation by proposing a three-part framework for using replication in teaching comprising ‘direct replication’, ‘step replication’, and ‘flexible replication’. These forms of replication can be summarised as below:

• Direct replication: This refers to the familiar concept of the reproduction of published findings using the original data and methods employed in a study.
• Step replication: This presents an alternative challenge, requiring students to retrace the analytical steps that may be hidden within automated software outputs. For example, software might generate results for a two-step procedure at the click of a button; in step replication, students are expected to manually execute and understand each step of that process.
• Flexible replication: This adds a further layer to the notion of replication via its use resources that allow results to be repeatedly generated and subsequently reproduced. Examples of this are provided in Cook (2016, 2019).

Together, these approaches illustrate the growing pedagogical interest in R&R and its potential to deepen student engagement with research. The next section turns to concrete examples that put these ideas into practice through case studies developed to support this chapter.

4. Case studies and resources

To demonstrate how R&R can be integrated into the teaching of undergraduate econometrics, we draw on three associated case studies available from the Economics Network Ideas Bank (Cook and Watson, 2025a, b, c). Each focuses on unit root analysis but introduces distinct extensions and teaching opportunities beyond this core topic. While these case studies have been developed alongside this chapter, a further similar case study employing R&R (Cook, 2020) is available to readers as an additional resource.

A natural starting point for R&R in the context of unit root analysis is Nelson and Plosser (1982), a seminal study that examined the orders of integration of fourteen major U.S. macroeconomic time series. The influence of this study has been substantial, prompting a wide range of follow-up studies using both the original and extended versions of the dataset. Among the most notable contributions are Perron’s (1989, 1997) analyses of structural breaks and their impact on inferences. Other contributions include work by Abadir et al. (2013), Leybourne (1995), Lu and Podivinsky (2003), Lucas (1995), Lumsdaine and Papell (1997), Phillips (1991), and Rudebusch (1992).

Our first case study (Cook and Watson, 2025a) draws upon Leybourne (1995), in which the original Nelson-Plosser data are used to introduce the maximum augmented Dickey-Fuller (ADF) test. Designed to improve test power, the maximum ADF test provides a platform for students to explore key concepts such as statistical power, higher-powered testing and the use of simulation to assess the properties of tests. In doing so, the case study goes beyond basic reproduction to open up broader discussions around test design and interpretation.

The second case study (Cook and Watson, 2025b), based on Leybourne et al. (1998), offers a different perspective on unit root testing by focusing on structural change and the issue of empirical size. However, unlike Perron’s (1989, 1997) work, which examines breaks under the alternative hypothesis and the resulting loss of test power, Leybourne et al. (1998) investigate how structural breaks can affect unit root tests when they occur under the null. This case study also moves beyond reproduction to consider replication (using the terminology of NASEM, 2019) via the use of a revised version, or later vintage, of the data employed in the original study.[7] Alternatively expressed in the terminology of Pesaran (2003), a ‘wide-sense’ replication rather than ‘narrow-sense’ replication is undertaken. This use of updated data allows students to explore how findings can shift over time, highlighting the evolving nature of empirical research and providing a foundation for broader discussions around data revision, real-time data, measurement systems, progressive modelling, and the reliability of empirical inference (see, for example: Cook, 2008; Croushore and Stark, 2003; Egginton et al., 2002; Garratt and Vahey, 2006; Mankiw and Shapiro, 1986; Mankiw et al., 1984; Patterson and Heravi, 1991).

The third case study (Cook and Watson, 2025c) is based on the reproduction of Holmes and Otero (2019). Like the previous case studies, it involves unit root testing but adds depth by examining relationships between time series. Specifically, this case study requires students to use R&R to undertake unit root analysis of not only individual price series, but also differentials – in this case, the difference between spot and futures prices – and extend their analysis through cointegration testing using the Johansen (1988) procedure. This case study therefore supports the development of further skills by moving from the consideration of individual series to the exploration of relationships between series via unit root analysis of differentials and the use of cointegration techniques.

As a final point, a common feature of these case studies is that they do not provide the data they consider, but instead present information on its source. As such, the development of data retrieval skills, which are highlighted as important in quantitative skills training in Economics (see, for example, QAA 2023), is supported by requiring students to access the data and undertake the steps required to transfer them into relevant econometric software.

5. Pedagogical concerns

This section explores the pedagogical benefits of using R&R, building on the themes introduced in Section 2. Rather than offering an exhaustive review, the aim here is to provide an overview of key ideas drawn from the broader literature.

As highlighted by Cook et al. (2019), Cook and Watson (2023) and Cook et al. (2025), the use of R&R in teaching can assist engagement with several key themes in pedagogical research. These include: effective active learning (Mayer, 2004, 2021); self-efficacy (Bandura, 1978; Zahaciva et al., 2005); anxiety towards quantitative methods (Dreger and Aiken, 1957; Dowker et al., 2016); cognitive load theory (Sweller et al., 1998, 2019; van Merriënboer and Sweller, 2005); and learning models such as productive failure (Kapur, 2008, 2012, 2015; Loibl et al., 2017), impasse-driven learning (VanLehn, 2003) and the expertise reversal effect (Cooper and Sweller, 1987; Kalyuga et al., 2001, 2003; Kirschner et al., 2006; Sweller and Cooper, 1985).

R&R supports these areas by offering students a clear, structured objective – namely, the replication and reproduction of published findings. This reduces cognitive load by providing focus and scaffolding, while also supplying a concrete context through which productive failure, impasse-driven learning, and other active learning strategies can be employed. Because R&R requires critical engagement rather than passive behaviour, it encourages cognitive, rather than simply behavioural, activity and thereby supports effective active learning (Mayer 2004, 2021). Also, when students succeed, they not only improve their skills but also build confidence, helping to alleviate anxiety and strengthen self-efficacy.

R&R also has clear links to employability, as the replication of empirical research requires students to work with data, software, and published studies. The supports the development of skills directly aligned with the growing emphasis on data literacy and quantitative competence in social science education (MacInnes et al., 2016; Mansell, 2015). With further reports such as Quantifying the UK Data Skills Gap (DSIT & DDCMS, 2021) and Data Science in the New Economy (World Economic Forum, 2019) underscoring the increasing importance of these capabilities from an employment perspective, the benefits of R&R are further emphasised.

In the context of economics specifically, a survey of professional economists by Anand et al. (2019) identified ‘Evaluating Econometric Work Done by Others’ as a leading area where further support could be offered during undergraduate education. Additional training needs flagged in the same survey included ‘Doing Econometrics’ and ‘Using Econometrics Software’. Clearly, these findings provide further support for the use of R&R, with these activities figuring prominently in the active revisiting of published empirical research.

6. Conclusion

This chapter has advanced the case for replication and reproduction (R&R) as far more than a methodological add-on: it is a catalyst for transforming undergraduate econometrics teaching. By revisiting the historical roots of R&R, engaging with the pedagogical literature, and presenting practical strategies for implementation, we have shown how R&R can turn students from passive recipients of knowledge into active participants in the research process. Its benefits extend beyond the classroom, equipping students with the confidence, quantitative expertise, and data literacy demanded in today’s academic and professional landscapes. Ultimately, R&R provides a distinctive bridge between research and teaching – one that not only enriches learning but also redefines what it means to study econometrics in a research-driven environment.

Related resources

1. Cook, S. 2006. Understanding the construction and evaluation of forecast evaluation statistics using computer-based tutorial exercises. Economics Network Ideas Bank. https://doi.org/10.53593/n143a
2. Cook, S. 2019. Forecast evaluation using Theil’s Inequality Coefficients. Economics Network Ideas Bank. https://doi.org/10.53593/n3168a
3. Cook, S. 2020. Unit root analysis. Economics Network Ideas Bank. https://doi.org/10.53593/n3341a
4. Cook, S. and Watson, D. 2025a. Replication and Reproduction I: Leybourne (1995, Oxford Bulletin of Economics and Statistics) and the maximum Dickey-Fuller test. Economics Network Ideas Bank. https://doi.org/10.53593/n4409a
5. Cook, S. and Watson, D. 2025b. Replication and Reproduction II: Leybourne et al. (1998, Journal of Econometrics) and the Dickey-Fuller test in the presence of breaks under the null. Economics Network Ideas Bank. https://doi.org/10.53593/n4410a
6. Cook, S. and Watson, D. 2025c. Replication and Reproduction III: Holmes & Otero (2019, Energy Economics), unit root testing of differentials and cointegration analysis. Economics Network Ideas Bank. https://doi.org/10.53593/n4411a

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[1] We are very grateful to the editor, Peter Dawson, for comments that have improved both the content and presentation of this chapter and the three associated case studies (Cook and Watson, 2025a, b, c).

[2] An example of the use of R&R in textbooks is provided by the consideration of Hendry and Ericsson (1991) in Hendry (1995). An example of the appearance of R&R in software is the provision of the data of Davidson et al. (1978) in PC-GIVE (see Ericsson, 2004; Hendry, 1986, 2024).

[3] Here ‘higher-level’ simply refers a level above introductory. As will be seen later, the specific case studies reviewed focus upon published research considering unit root and cointegration analysis which can both be viewed as ‘higher-level’.

[4] As will be seen, a number of alternative terms are used in relation to the inclusion of research in teaching. IRT is employed here for convenience as a means of referring to ‘incorporating research in teaching’ or ‘the incorporation of research in teaching’.

[5] Following its introduction and subsequent exploration for secondary-level education, PCK has gained traction in the Higher Education literature with recent research considering its alternative forms (Carlson et al., 2019), application to different disciplinary areas (Nind, 2020) and the factors impacting upon lecturer engagement with PCK (Fraser, 2016).

[6] Given our interest in econometrics, our reference to Economics arises via consideration of this as the broader discipline within which econometrics lies. We are not arguing that what follows is specific to Economics.

[7] The data employed in Leybourne et al. (1998) are available from Maddison (1995). These revised data are available from the 2023 Maddison Project Database (Bolt and van Zanden, 2024).