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Asia and Pacific > India

You are looking at 1 - 10 of 11 items for :

  • Type: Journal Issue x
  • Forecasting and Other Model Applications x
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Cover IMF Working Papers
Predicting the Law: Artificial Intelligence Findings from the IMF’s Central Bank Legislation Database
Khaled AlAjmi
,
Jose Deodoro
,
Mr. Ashraf Khan
, and
Kei Moriya
Using the 2010, 2015, and 2020/2021 datasets of the IMF’s Central Bank Legislation Database (CBLD), we explore artificial intelligence (AI) and machine learning (ML) approaches to analyzing patterns in central bank legislation. Our findings highlight that: (i) a simple Naïve Bayes algorithm can link CBLD search categories with a significant and increasing level of accuracy to specific articles and phrases in articles in laws (i.e., predict search classification); (ii) specific patterns or themes emerge across central bank legislation (most notably, on central bank governance, central bank policy and operations, and central bank stakeholders and transparency); and (iii) other AI/ML approaches yield interesting results, meriting further research.
Keywords:
central bank legislation; central banking; artificial intelligence; machine learning; Bayesian algorithm; Boolean algorithm; central bank governance; law and economics; ML approach; IMF central bank transparency code; IMF Central Bank Legislation Database; capital markets Department; approaches to analyzing pattern; CBLD data flow; Central bank autonomy; Central bank transparency; Global
Cover IMF Working Papers
Global Economic Impacts of Physical Climate Risks
Caterina Lepore
and
Roshen Fernando
This paper evaluates the global economic consequences of physical climate risks under two Shared Socioeconomic Pathways (SSP 1-2.6 and SSP 2-4.5) using firm-level evidence. Firstly, we estimate the historical sectoral productivity changes from chronic climate risks (gradual changes in temperature and precipitation) and extreme climate conditions (representative of heatwaves, coldwaves, droughts, and floods). Secondly, we produce forward-looking sectoral productivity changes for a global multisectoral sample of firms. For floods, these estimates account for the persistent productivity changes from the damage to firms’ physical capital. Thirdly, we assess the macroeconomic impact of these shocks within the global, multisectoral, intertemporal general equilibrium model: G-Cubed. The results indicate that, in the absence of additional adaptation relative to that already achieved by 2020, all the economies would experience substantial losses under the two climate scenarios and the losses would increase with global warming. The results can be useful for policymakers and practitioners interested in conducting climate risk analysis.
Keywords:
climate risk analysis; capital markets department; climate risk; physical capital; productivity change; Climate change; Productivity; Agricultural sector; Natural disasters; Global; North America; Western Europe; Europe
Cover IMF Working Papers
Identifying Optimal Indicators and Lag Terms for Nowcasting Models
Jing Xie
Many central banks and government agencies use nowcasting techniques to obtain policy relevant information about the business cycle. Existing nowcasting methods, however, have two critical shortcomings for this purpose. First, in contrast to machine-learning models, they do not provide much if any guidance on selecting the best explantory variables (both high- and low-frequency indicators) from the (typically) larger set of variables available to the nowcaster. Second, in addition to the selection of explanatory variables, the order of the autoregression and moving average terms to use in the baseline nowcasting regression is often set arbitrarily. This paper proposes a simple procedure that simultaneously selects the optimal indicators and ARIMA(p,q) terms for the baseline nowcasting regression. The proposed AS-ARIMAX (Adjusted Stepwise Autoregressive Moving Average methods with exogenous variables) approach significantly reduces out-of-sample root mean square error for nowcasts of real GDP of six countries, including India, Argentina, Australia, South Africa, the United Kingdom, and the United States.
Keywords:
Nowcasting; Mixed Frequency; Forecasting; Business Cycles; selection procedure; Annex I. AS-ARIMAX procedure; nowcasting method; evaluation comparison; baseline model; Global
Cover IMF Working Papers
Financial Sector and Economic Growth in India
Ms. Margaux MacDonald
and
Ms. TengTeng Xu
India’s financial sector has faced many challenges in recent decades, with a large, negative, and persistent credit to GDP gap since 2012. We examine how cyclical financial conditions affect GDP growth using a growth-at-risk (GaR) approach and analyze the link between bank balance sheets, credit growth, and long-term growth using bank-level panel regressions for both public and private banks. We find that on a cyclical basis, a negative shock to credit or a rise in macro vulnerability all shift the distribution of growth to the left, with lower expected growth and higher negative tail risks; over the long term, the results indicate that higher credit growth, arising from better capitalized banks with lower NPLs, is associated with higher GDP growth.
Keywords:
India; Credit and Leverage; Macro-financial Linkages; Growth-at-Risk; Panel Regressions.; credit growth; cyclical financial conditions; economic growth in India; GDP gap; bank capitalization; Credit; Bank credit; State-owned banks; Debt financing; Financial statements; Global; NPL ratio; bank equity; bank assets; growth from bank
Cover IMF Working Papers
Overcoming Data Sparsity: A Machine Learning Approach to Track the Real-Time Impact of COVID-19 in Sub-Saharan Africa
Karim Barhoumi
,
Seung Mo Choi
,
Tara Iyer
,
Jiakun Li
,
Franck Ouattara
,
Mr. Andrew J Tiffin
, and
Jiaxiong Yao
The COVID-19 crisis has had a tremendous economic impact for all countries. Yet, assessing the full impact of the crisis has been frequently hampered by the delayed publication of official GDP statistics in several emerging market and developing economies. This paper outlines a machine-learning framework that helps track economic activity in real time for these economies. As illustrative examples, the framework is applied to selected sub-Saharan African economies. The framework is able to provide timely information on economic activity more swiftly than official statistics.
Keywords:
Sub-Saharan Africa; Economic Activity; GDP; Machine Learning; Nowcasting; COVID-19; machine learning approach; data sparsity; GDP statistics; crisis in Sub-Saharan Africa; learning framework; Oil prices; Real effective exchange rates; Africa; Global
Cover IMF Working Papers
Mitigating Climate Change: Growth-Friendly Policies to Achieve Net Zero Emissions by 2050
Ms. Florence Jaumotte
,
Weifeng Liu
, and
Warwick J. McKibbin
Background paper prepared for the October 2020 IMF World Economic Outlook. This paper provides a detailed presentation of the simulation results from the October 2020 IMF World Economic Outlook chapter 3 and an additional scenario with carbon pricing only for comparison with the comprehensive policy package where green investments were also included. This paper has greatly benefitted from continuous discussions with Oya Celasun and Benjamin Carton on the design of simulations; contributions from Philip Barrett for part of the simulations; and research support from Jaden Kim. We also received helpful comments from other IMF staff. All remaining errors are ours. McKibbin and Liu acknowledge financial support from the Australian Research Council Centre of Excellence in Population Ageing Research (CE170100005).
Keywords:
policy package; climate mitigation strategy; baseline CO2 emission; scenario design; B. Policy tool; Carbon tax; Greenhouse gas emissions; Non-renewable resources; Climate finance; Global; Europe
Cover IMF Working Papers
Quarterly Projection Model for India: Key Elements and Properties
Mr. Jaromir Benes
,
Kevin Clinton
,
Asish George
,
Pranav Gupta
,
Joice John
,
Mr. Ondrej Kamenik
,
Mr. Douglas Laxton
,
Pratik Mitra
,
G.V. Nadhanael
,
Mr. Rafael A Portillo
,
Hou Wang
, and
Fan Zhang
This paper outlines the key features of the production version of the quarterly projection model (QPM), which is a forward-looking open-economy gap model, calibrated to represent the Indian case, for generating forecasts and risk assessment as well as conducting policy analysis. QPM incorporates several India-specific features like the importance of the agricultural sector and food prices in the inflation process; features of monetary policy transmission and implications of an endogenous credibility process for monetary policy formulation. The paper also describes key properties and historical decompositions of some important macroeconomic variables.
Keywords:
WP; core inflation; interest rate; monetary policy; inflation targeting; Reserve Bank of India; inflation episodes in India; forecasting models; monetary policy models; model calibration; monetary policy rules; monetary policy simulations; food inflation; inflation expectation; reaction function; inflation target; transmission mechanism; Inflation; Output gap; Central bank policy rate; Food prices; Global
Cover IMF Working Papers
Inflation-Forecast Targeting for India: An Outline of the Analytical Framework
Mr. Jaromir Benes
,
Kevin Clinton
,
Asish George
,
Joice John
,
Mr. Ondrej Kamenik
,
Mr. Douglas Laxton
,
Pratik Mitra
,
G.V. Nadhanael
,
Hou Wang
, and
Fan Zhang
India formally adopted flexible inflation targeting (FIT) in June 2016 to place price stability, defined in terms of a target CPI inflation, as the primary objective of monetary policy. In this context, the paper draws on Indian macroeconomic developments since 2000 and the experience of other countries that adopted FIT to bring out insights on how credible policy with an emphasis on a strong nominal anchor can reduce the impact of supply shocks and improve macroeconomic stability. For illustrating the key issues given the unique structural characteristics of India and the policy options under an FIT framework, the paper describes an analytical framework using the core quarterly projection model (QPM). Simulations of the QPM are carried out to illustrate the monetary policy responses under different types of uncertainty and to bring out the importance of gaining credibility for improving monetary policy efficacy.
Keywords:
WP; monetary policy; interest rate; central bank; exchange rate; inflation targeting; Reserve Bank of India; inflation episodes in India; forecasting models; monetary policy models; model calibration; monetary policy rules; monetary policy simulations; inflation expectation; output-inflation trade-off; transmission mechanism; inflation process; asset price inflation; inflation outcome; food inflation; inflation spiral; inflation condition; monetary policy credibility; inflation glide path; a number of monetary policy instrument; Inflation; Central bank policy rate; Output gap; Supply shocks; Global
Cover IMF Working Papers
Growth: Now and Forever?
Giang Ho
and
Mr. Paolo Mauro
Forecasters often predict continued rapid economic growth into the medium and long term for countries that have recently experienced strong growth. Using long-term forecasts of economic growth from the IMF/World Bank staff Debt Sustainability Analyses for a panel of countries, we show that the baseline forecasts are more optimistic than warranted by past international growth experience. Further, by comparing the IMF’s World Economic Outlook forecasts with actual growth outcomes, we show that optimism bias is greater the longer the forecast horizon.
Keywords:
WP; economic growth; country; growth rate; economic growth forecast; growth forecast; optimism bias; forecast horizon; debt sustainability analysis; World Bank DSA exercise; IMF engagement status; teams' baseline forecast; per capita income; IMF definition; standard deviation; Personal income; Output gap; Western Hemisphere; Sub-Saharan Africa; Global
Cover IMF Working Papers
The Future of Oil: Geology Versus Technology
Ms. Marcelle Chauvet
,
Mr. Jack G. Selody
,
Mr. Douglas Laxton
,
Mr. Michael Kumhof
,
Mr. Jaromir Benes
,
Mr. Ondrej Kamenik
, and
Susanna Mursula
We discuss and reconcile two diametrically opposed views concerning the future of world oil production and prices. The geological view expects that physical constraints will dominate the future evolution of oil output and prices. It is supported by the fact that world oil production has plateaued since 2005 despite historically high prices, and that spare capacity has been near historic lows. The technological view of oil expects that higher oil prices must eventually have a decisive effect on oil output, by encouraging technological solutions. It is supported by the fact that high prices have, since 2003, led to upward revisions in production forecasts based on a purely geological view. We present a nonlinear econometric model of the world oil market that encompasses both views. The model performs far better than existing empirical models in forecasting oil prices and oil output out of sample. Its point forecast is for a near doubling of the real price of oil over the coming decade. The error bands are wide, and reflect sharply differing judgments on ultimately recoverable reserves, and on future price elasticities of oil demand and supply.
Keywords:
WP; oil price; supply shock; Oil prices; exhaustible resources; fossil fuels; oil depletion; Hubbert’s Peak; Bayesian econometrics; price elasticity; price mechanism; elasticities of demand and supply; cost share; price effect; price channel; long-run price elasticities of oil demand and supply; adjustment cost; share of energy; oil price growth rate; price elasticities of oil demand and supply; price elasticities of demand and supply; Oil; Oil production; Output gap; Inflation; Global