for banks and financial institutions. mitigation and green transition policies such as carbon pricing can also affect price stability, potentially precipitating large and long - lasting movements in relative 10 tree - based methods are flexible machine learning algorithms that can tackle a wide range of tasks. decision trees group individual data points by sequentially partitioning data into finer categories according to specific characteristics of interest. neural networks ’ main building blocks are artificial neurons, which take multiple input values and transform them in a non - linear way to output a single number – like logistic regressions. source : bis. ( 2024 ). artificial intelligence in central banking. prices and shifts in trend inflation. depreciation pressures on currencies of countries frequently affected by climate disasters can also cause financial instability, higher import costs and negative terms of trade. the range of policy options available to mitigate climate risks require dedicated research, especially in the context of the complex, non - linear ways in which climate, the real economy, financial systems and markets interact and affect each other. improved inter - disciplinary macroeconomic modelling is becoming crucial for understanding directions of causality and feedbacks. iii. globalisation and the natural rate of interest monetary policy making has evolved in line with structural changes in the economy and the financial system. inflation targeting ( it ) – the longest surviving modern monetary policy framework - is no exception. this could be attributed to the ‘ rule - based ’ principle built into the framework alongside elements of “ flexibility ” that have evolved in practice. it has been argued while the application of a core set of " scientific principles " has expanded significantly in practice, there remains, and will likely always remain, elements of art in the conduct of monetary policy 11. 11 mishkin, frederic s. ( 2007 ). will monetary policy become more of a science? nber working paper 13566, october. one principle followed by central banks in setting policy rates is the natural rate of interest – popularly known as r - star. it is a theoretical benchmark for monetary policy, reflecting the real interest rate that supports the economy at full employment while keeping inflation low and stable. this concept of r - star or the natural rate dates back to 189812 and currently forms an integral element of modern macroeconomic frameworks. it is argued in a seminal work that “ a central bank should seek to close the gaps between actual economic conditions and the economy ’ s potential for output and employment ( y - star and u - star, respectively )

of time, i will focus on four emerging areas of research that, i believe, will redefine human behaviour and hence economic research. i. redefining technology shocks in economic models the rapid spread of digitalisation has been transformative, reshaping the way we live and work, the interactions between economic agents, production processes and market structures. digitalisation can be regarded as a long - term technology shock impacting economic growth, productivity, labour markets, older technologies and inflation. it is estimated that the global digital economy already accounts for more than 15 per cent of global gdp. 2 generative artificial intelligence ( gen - ai ) alone is projected to boost global gdp by $ 7 - 10 trillion over the next three years. 3 to capture these evolving dynamics, empirical research methodologies must evolve to be able to understand and assess the underlying relationships and implications. despite the potential of digital technologies to drive productivity through efficiency, growth has consistently fallen short of expectations. this has given rise to antithetical scenarios of slow innovation diffusion. 4 recent studies have deepened the debate, highlighting the uncertainty around ai ’ s impact on productivity and economic growth. 5 this productivity puzzle could be pointing to research gaps in growth decomposition models as well as in the received wisdom in explaining sectoral productivity shifts and the distributional effects of technology shocks across regions and income groups. digitalisation ’ s long - term impact on employment appears benign so far, but its disruptive effects on labour markets have drawn considerable 2 united nations. ( 2023 ). opening session of global development initiative digital cooperation forum. 3 jp morgan. ( 2024 ). is generative ai a game changer? 4 brynjolfsson, e., and mcafee, a. ( 2014 ). the second machine age : work, progress, and prosperity in a time of brilliant technologies. ww norton & company ; haldane, a. ( 2017 ). productivity puzzles. speech at the london school of economics ; summers, l. h. ( 2013 ). speech at the imf 14th annual research conference in honor of stanley fisher. international monetary fund, 8 5 goldman sachs. ( 2024 ). gen ai : too much spend, too little benefit report. attention. 6 population ageing may further accelerate adoption of digital technologies. with ai set to affect 40 per cent of global jobs, education, retraining and social safety nets will be crucial. 7 within central banks too, recruitment and retention of fintech