Business and Economics > Production and Operations Management

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Yang Liu
,
Ran Pan
, and
Rui Xu
Forecasting inflation has become a major challenge for central banks since 2020, due to supply chain disruptions and economic uncertainty post-pandemic. Machine learning models can improve forecasting performance by incorporating a wider range of variables, allowing for non-linear relationships, and focusing on out-of-sample performance. In this paper, we apply machine learning (ML) models to forecast near-term core inflation in Japan post-pandemic. Japan is a challenging case, because inflation had been muted until 2022 and has now risen to a level not seen in four decades. Four machine learning models are applied to a large set of predictors alongside two benchmark models. For 2023, the two penalized regression models systematically outperform the benchmark models, with LASSO providing the most accurate forecast. Useful predictors of inflation post-2022 include household inflation expectations, inbound tourism, exchange rates, and the output gap.
Philipp Engler
,
Ms. Margaux MacDonald
,
Mr. Roberto Piazza
, and
Galen Sher
We propose a novel approach to measure the dynamic macroeconomic effects of immigration on the destination country, combining the analysis of episodes of large immigration waves with instrumental variables techniques. We distinguish the impact of immigration shocks in OECD countries from that of refugee immigration in emerging and developing economies. In OECD, large immigration waves raise domestic output and productivity in both the short and the medium term, pointing to significant dynamic gains for the host economy. We find no evidence of negative effects on aggregate employment of the native-born population. In contrast, our analysis of large refugee flows into emerging and developing countries does not find clear evidence of macroeconomic effects on the host country, a conclusion in line with a growing body of evidence that refugee immigrants are at disadvantage compared to other type of immigrants.
Mr. Jiaqian Chen
and
Lucyna Gornicka
We apply a range of models to the U.K. data to obtain estimates of the output gap. A structural VAR with an appropriate identification strategy provides improved estimates of output gap with better real time properties and lower sensitivity to temporary shocks than the usual filtering techniques. It also produces smaller out-of-sample forecast errors for inflation. At the same time, however, our results suggest caution in basing policy decisions on output gap estimates.
Michal Andrle
This paper discusses several popular methods to estimate the ‘output gap’. It provides a unified, natural concept for the analysis, and demonstrates how to decompose the output gap into contributions of observed data on output, inflation, unemployment, and other variables. A simple bar-chart of contributing factors, in the case of multi-variable methods, sharpens the intuition behind the estimates and ultimately shows ‘what is in your output gap.’ The paper demonstrates how to interpret effects of data revisions and new data releases for output gap estimates (news effects) and how to obtain more insight into real-time properties of estimators.
Maral Shamloo
In this paper we study the dynamics of inflation in Macedonia, provide three forecasting tools and draw some policy conclusions from the quantitative results. We explore three forecasting methods for inflation. We use a Dynamic Factor Model (DFM) for short-term, monthly forecasting. We also develop two quarterly models: A Vector Error Correction Model (VECM), and a New Keynesian Phillips Curve (NKPC) for a more structural model of inflation. The NKPC shows a significant effect of output gap and inflation expectations on current inflation, confirming that the expectations channel of monetary transmission mechanism is strong. In terms of forecast-error variance, we show that all three models do very well in one-period ahead forecasting.
Mr. Sanjeev Gupta
,
Mr. Alvar Kangur
,
Mr. Abdoul A Wane
, and
Mr. Chris Papageorgiou
This paper constructs an efficiency-adjusted public capital stock series and re-examines the public capital and growth relationship for 52 developing countries. The results show that public capital is a significant contributor to economic growth. Although the estimated coefficient for the income share of public capital is larger in middle- than in low-income countries, the opposite is true for the marginal product of public capital. The quality of public investment, as measured by variables capturing the adequacy of project selection and implementation, are statistically significant in explaining variations in economic growth, a result mainly driven by low-income countries.
International Monetary Fund
In this paper, we first introduce investment-specific technology (IST) shocks to an otherwise standard international real business cycle model and show that a thoughtful calibration of them along the lines of Raffo (2009) successfully addresses the "quantity", "international comovement", "Backus-Smith", and "price" puzzles. Second, we use OECD data for the relative price of investment to build and estimate these IST processes across the U.S and a "rest of the world" aggregate, showing that they are cointegrated and well represented by a vector error correction model (VECM). Finally, we demonstrate that when we fit such estimated IST processes in the model instead of the calibrated ones, the shocks are actually not as powerful to explain any of the four montioned puzzles.
International Monetary Fund
Tunisia’s reliance on European countries for export earnings, tourism, remittances, and foreign direct investment inflows has remained high over the last decades. Remittances and tourism receipts have been broadly stable in percent of GDP, with somewhat more fluctuations in the latter caused in part by identifiable political events that harmed tourism in the region. Tunisia’s annual growth rate appears to have become increasingly synchronized over time with the annual growth rate of its main European trading partners.
Sanjay Kalra
The paper characterizes trade exposure and regional integration in six ASEAN economies during 1997-2008. For this, the paper uses the 2000 Asian Input Output Tables which are extrapolated using National Income Accounts and COMTRADE data. On the demand side, the paper shows that the level and geographical nature of external exposure varies across the ASEANs, and has changed over time. In particular, there was a shift in the external demand exposure of ASEANs from mature markets, including the United States, to China and ROW. In addition, the share of China in East Asia’s final demand, especially investment, rose sharply while that of Japan fell. On the supply side, the paper documents the rise of China into a “global factory” and the steady shift in regional production and integration from Japan and the United States to China.