World Currency

It is a pleasure to welcome you all to the Biennial Conference of the Irving Fisher Committee (IFC). This is already the 11th in the series, all hosted by the BIS. I am especially glad to see that former IFC Chairs, the current Chair and the Chair-to-be are participating. This is a clear sign of the importance of the event. I am sure that, as in past, the conference will provide plenty of food for thought to better inform our discussions and help implement the IFC's agenda. Last but not least, it is great that, finally, we can all meet in person. The programme and the IFC's agenda are naturally concerned with the challenges of the day and with the statistics that will help policymakers address them. Today, however, I would like to stand back and reflect on how the past can inform the present and on the role that statistics can play in that context. This will also allow me to introduce the "new kid on the block" – the latest addition to the IFC's "enlarged" family. This is the central bank network on historical monetary and financial statistics (HMFS), which brings together central bank statisticians and academics. I am particularly excited about this development. I will address three questions. Why do we need historical economic statistics? What is the network about? And what has been its most concrete output to date? Consider this an open invitation to get more involved in the network's activities. The value of historical economic statistics They say that history is to society what memory is to an individual. Memory is what provides continuity to us as individuals. One could even say that it is the basis of consciousness, as we can only exist in time. It helps define who we are. Of course, history has a connotation of "distant memory". But that, too, helps define who we are and determine what we do. Moreover, the dividing line between "history" and "recent past" – or "yesterday" – is fuzzy and context-dependent. Often, the line is arbitrarily drawn at the point when our own experience begins. Introspection comes in handy here. When I was a teenager, I thought of the 1920s as distant history; today, I think of the 1970s as yesterday. And yet, they are both separated by half a century. What is true of history in general is also true of economic history, be it the history of economic thought or the history of events. And all study of economic history must be based on statistics, ie the "facts" or data points that inform our interpretation of what happened. Hence their critical importance in understanding the past and in drawing lessons for the present. Although I did not have much exposure to economic history in my years at university, I came to embrace it during my professional life. Personally, I found it essential to shed light on the preoccupations of current policymakers. The list of issues, as reflected in my work, is not a short one. It includes issues such as the hidden perils of the so-called Great Moderation; the costs or, in fact, non-costs of deflation; the usefulness of the concept of the natural interest rate; the great power but also great limitations of monetary policy; the waxing and waning of central bank independence; and, more generally, how policy regimes shape, and are shaped by, the economic environment in an interaction that can spring challenges from unsuspected quarters – the Great Financial Crisis of 2008–09 being the most notable example. It goes without saying, reading history correctly is tricky. Drawing lessons for today requires identifying what can and cannot be inferred given the difference in context. In turn, this calls for knowledge that goes beyond narrow economic understanding and a degree of imagination, to avoid projecting onto the past today's intellectual baggage and vice versa. This is also true for the statisticians that develop the raw material that economists work with. I will get back to this in a minute. The HMFS network What, then, is the HMFS network? It is an informal group that brings together central bank statisticians, economists with a strong interest in statistics and academics to exchange views and share their experience in the development and use of historical monetary and financial statistics. The objective is to help develop those statistics and to stimulate their production more broadly. The group is very much a "coalition of the willing". At present it involves 10 central banks and 2 academics. Importantly, the focus of the group is not on collection, but on design and production. Participants have been brought together by the recognition that production of historical statistics is both hard and a public good. As a result, there is a strong disincentive to create them. Participants also recognise that a robust transnational methodology is necessary to guide the production of high-quality statistics. Hence the core concern with methodology and the aspiration to delineate standards of good practice. In the absence of such guidance, it is all too easy for compilation of national statistics into panels to involve series that are not only imperfect, but like Tolstoy's unhappy families, which are all unhappy in their own way, to all be imperfect in their own way. The group has two guiding principles. One is the importance of comparability of statistics across countries and time. The other is the importance of transparency in how those statistics are produced, in "how the sausage is made", as it were. Transparency is essential to address the major obstacles and pitfalls involved as well as to give rise to an "open-ended" process. This is how knowledge is transferred and statistics can be improved over time. Transparency is of the essence to improve not just production, but also consumption. All too often economists, as main consumers, take statistics at face value. Sometimes I have been guilty of this sin myself! It is important that statisticians do not tire of raising awareness. This is true not only of historical statistics, but of current ones as well! As the group notes, producing high-quality historical statistics requires "statistorians" –professionals who combine technical statistical know-how with an understanding of history. This allows them to place the original statistics in their proper institutional data-generating context and hence to understand sources and the sources' limitations. We need more of them. Hopefully, the efforts of the network will stimulate their emergence. The first report The group first met at the BIS in October 2016 and, subsequently, at roughly yearly intervals, interrupted by the pandemic. I guess you will be asking yourselves: "What about any concrete output?". The first visible output is a report or monograph to be released soon entitled "Historical monetary and financial statistics for policymakers: towards a unified framework". As with any first child, its birth has proved challenging but also very rewarding! The report does three things: it provides context on the history and purpose of the group; it lays out the key methodological principles; and it applies them to the construction of interest rate, credit and real estate price statistics. The principles are then illustrated concretely by the statistical series put together by participating central banks. These include those of the United States, Japan, France, the United Kingdom, Italy, Canada, Austria, Sweden, Norway and Denmark. Why the choice of those economic variables to start with? Three reasons. First, they have come to prominence in policymaking owing to the historical re-emergence of major financial cycles. After being dormant for several decades, these cycles have been at the heart of business cycles since the mid-1980s. But they had also been common in the late 19th century all the way to the Great Depression. There are clearly lessons to be learnt. Second, statistics on credit and real estate prices are comparatively scarce and, surprisingly, rather poorly understood (we were struck by this). And those that do exist have significant shortcomings. Third, from a methodological perspective, the three series shed light on different issues. One is the deceptive simplicity of the construction of (short- and long-term) interest rate series (a financial price). For example, the construction of benchmark interest rates involves sometimes subtle questions concerning the structure and operation of markets, the specific nature of contracts as well as those of pricing practices and conventions. Another issue is the huge complications that hinder the production of consistent credit aggregates (a financial quantity) – a financial variable that had been neglected until recently in favour of its close cousin, monetary aggregates. Yet another issue is the complexity of aggregating into an index highly heterogenous assets (real estate), which can have first-order effects on the corresponding series. What are the key takeaways of the report? Many! I have already mentioned some and would strongly encourage you to read the whole study, which is one of a kind. But in the time available, let me mention two. One is conceptual and often overlooked. The other is empirical and largely novel. The conceptual one is that, fundamentally, building historical statistics requires dealing with synthetic countries and synthetic objects. Synthetic countries, because the borders of nation states have been in flux. This has important and often neglected implications for the interpretation of the statistics. Synthetic objects, because the same term can be applied to highly different variables. Just think of how much what constitutes the right "policy interest rate" varies across countries and has changed over time! Both issues require careful treatment.    The empirical takeaway concerns what one might call "missing credit". This largely reflects a focus on regulated institutions and, more specifically, banks – sometimes only a subset of them – in the construction of the statistics, an instance of "look under the lamppost" syndrome. The most widely used historical credit statistics miss large chunks of credit. What today would be termed "shadow banking" was typically big, and so was "peer-to-peer" lending in several countries, in the form of mortgage credit often intermediated by notaries. Not such new phenomena after all! As a result, our understanding of the degree of "financial deepening" or of the information content of credit aggregates for financial crises has probably been distorted, across both countries and time, despite efforts to overcome the drawbacks in the data. Conclusion Let me stop here. I hope I have encouraged you to reflect more on the value of historical statistics and of their careful construction. If something is a public good, as the production of these statistics is, it is worth investing in it. Critically, the importance of a transparent approach cannot be emphasised enough, as a means to bring to light the statistics' strengths and limitations, to allow for their improvement over time and to facilitate their proper interpretation and use. The message of the network is both sobering and optimistic. It is sobering, because it offers a vigorous discussion of the limitations of existing measures of our macro-financial history. It is optimistic, because while the gaps and imperfections are extensive, they can be overcome. The network offers a way forward. You can see how the IFC having the HMFS network operate under its aegis – "adopting" this young kid, as it were – is mutually beneficial. It offers the network a welcome and cosy new home, and it offers the IFC a new vehicle to develop policy-oriented statistics and to nurture links with academia. We are grateful for your decision. I very much hope, too, that my remarks have whetted your appetite for the work of the network. Consider this an open invitation to become more involved in its activities
In recent years, many central banks have engaged in data projects aimed at the collection and documentation of historical monetary and financial statistics (HMFS) for their respective countries. Long runs of data for key macroeconomic time series are increasingly being used in policy-oriented research. Information from these historical databases is used to draw parallels between current developments and historical events to shed light on today's policy issues in the areas of monetary and financial stability. The aim of the Bank for International Settlements (BIS) HMFS project is to establish a network between interested central banks that have already invested in local national HMFS databases, using the BIS as a hub. This BIS paper is an attempt to take stock of what counts as "good practice" in collating HMFS and how it should be implemented in different contexts.
Thank you, Your Excellency President Gitanas Nausėda, for opening this International Conference on the Future of Central Banking. Since its official establishment on 27 September 1922, the objective of Lietuvos bankas has been developing a sound and sustainable monetary system that contributes to the resilience of the Lithuanian economy. Reestablished 1 March 1990, the Lietuvos bankas continues to contribute to sustainable economic growth and the improvement of resident welfare. The fundamental principle that underlies all our activities is creating value for society. While we are all gathered here today in celebration of the centenary of Lietuvos bankas, the focus of our discussions will not be reflection on what has already been accomplished. Rather, we will talk about the challenges, opportunities, and changes that await Lietuvos bankas – and central banking in general – over the next hundred years. Dear Governors, Distinguished Guests, Ladies and Gentlemen, We root our decisions in data, and as the world becomes increasingly digital, we are shifting from data-centric to data-driven policy making. In the course of this shift, we have been hearing legitimate questions as to whether central bankers will be replaced by algorithms – computers, so to speak – in the foreseeable future, given that computers can make better decisions than humans when those decisions are purely data-driven. So how will Lietuvos bankas – or generally speaking, a central bank – look a hundred years from today? Central banks were always data-dependent in their decision-making. But nowadays – and going forward – it no longer suffices just to have a hypothesis and validate it with data. Banks must become data-driven and adept at data wrangling in order to generate useful insights. A lesson we central banks learned from the crises of the recent decades is that we don't know enough, but those who are performing the best are always the ones who know the most. With the power of increasingly advanced computers and near-instant access to data, it is almost certain that in a hundred years, artificial intelligence and machine learning will do most of the heavy lifting in predicting economic indicators upon which to base central bank policies. We will undoubtedly rely on technology to obtain real-time forecasts. We will also undoubtedly use big data analysis to detect bubbles in the financial markets and to identify and analyze complex macro-financial linkages. Central banks are already seeking to obtain more micro-level data by becoming data warehouses and to develop-data based supervisory tools which will improve risk mitigation. Will this technology replace us – central bankers – in making policy decisions? My answer is: certainly not!                                   Let me elaborate. First, a great deal of uncertainty exists as to how basic economic relations evolve over time. Economics, though increasingly fixated on mathematics, econometric tools, and applications is a social science focusing on how humans make decisions. It does not, and never has been, a homogenous body with a firm canon of knowledge. Fundamental structural features of the economy, known by more familiar names such as the "natural rate of unemployment", "potential output growth", "neutral real interest rate" – what FED Chairman Mr. Jerome Powell called the "stars"[1] – change location significantly over time, making "navigating by the stars" rather challenging. Furthermore, even seemingly simple issues, such as establishing causal links rather than merely finding correlations among various economic variables, require lengthy discussion and often lead to clashes of opinions. Second, the effectiveness of central bank policies is based on adequate communication. And communication boils down to presenting stories that shape the expectations of market participants regarding future monetary policy. To date, the ability to present such nuances associated with the conduct of monetary policy has been exclusively human. Finally, it may also happen that policy objectives are not achieved, due to unforeseeable crises – so-called black swans. Central banks during "black swan" episodes must behave differently than in normal times – and rightly so. Deciding the best way forward when circumstances are rapidly changing and the future is clouded by enormous uncertainty is a task which no computer is able to cope with, today or quite possibly ever. It is thus my firm conviction that, in a hundred years' time, there will still be some central bankers around to gather and celebrate two hundred years of Lietuvos bankas. Dear Colleagues, Central banks must embrace the revolution underway in digital money to ensure that they remain at the heart of the global payments system. While the private sector excels at customer-facing activity, central banks provide the basis for trust, ensure liquidity, and set standards. Central banks, while independent institutions, are accountable to the general public. This accountability necessitates building public confidence through intensive communication, namely, explaining the specific policy decisions taken by decision-makers under specific economic conditions. Shifting to data-driven decision-making and evolving together with changing technologies will ensure that central banks remain effective and credible, in turn enabling us to fulfill our mission of creating value for society. Distinguished Guests, Lietuvos bankas is pleased to organize this Centenary Conference in cooperation with our colleagues from the Bank for International Settlements (BIS), an institution that serves as a central bank for central banks. Lietuvos bankas' relationship with the BIS is almost as old as both these institutions! The BIS commenced operation in 1930, and in March of 1931, Lietuvos bankas became its shareholder. Please allow me to give the floor to Mr. Luiz Pereira da Silva, Deputy General Manager of the Bank for International Settlements. Luiz, the floor is yours. [1] From the speech "Monetary Policy in a Changing Economy", delivered 24 August 2018 at "Changing Market Structure and Implications for Monetary Policy" symposium sponsored by the Federal Reserve Bank of Kansas City, in Jackson Hole, Wyoming.