Economic news

Volatile food prices led to India cenbank missing inflation forecasts – economists

2022.10.13 05:31



© Reuters. FILE PHOTO: Nikhil Kumar Mondal, 65, a retired school headmaster, buys vegetables from a vendor at a market on the outskirts of Kolkata, India, May 20, 2022. Picture taken May 20, 2022. REUTERS/Rupak De Chowdhuri

By Anushka Trivedi

MUMBAI (Reuters) – India’s central bank has been consistently missing its inflation projections over the last two and half years mainly due to extreme weather conditions that drove up food prices, economists said on Thursday.

Inflation has been above the RBI’s quarterly projections in the bi-annual monetary policy report (MPR) in eight out of the last 10 quarters until September, while retail price rise exceeded the 4%-6% mandated target band since January.

RBI’s inflation projections in the MPR are based on its economic model but the extreme weather conditions have made food price volatility hard to predict, said economists.

Graphic: India cenbank’s CPI projections vs actual outcomes

“There are extreme weather conditions globally and domestically, which is really causing a lot of volatility to perishable food items,” said Upasna Bhardwaj, senior economist at Kotak Mahindra Bank.

“So, to that extent, it is becoming increasingly difficult to capture food inflation beyond immediate near-term.”

“Particularly in India, monsoon has a role to play. If rice producing states don’t receive up to the mark rainfall, it can cause inflation to grow…and those are hard things to predict,” said Disha Sahgal, an economist and country risk manager with ANZ.

India’s retail inflation accelerated in September to a five-month high of 7.41%, data showed on Wednesday, as annual food prices surged to a two-year high of 8.60%, on the back of continually growing cereal prices.

However, the RBI’s monetary policy committee does take into account near-term price pressures when it announces its quarterly projections, and the revised projections by the MPC have been closer to actual numbers due to constant revisions.

“War-induced price pressures as well as domestic supply shocks turned out to be stronger and more persistent than anticipated, resulting in actual inflation exceeding projections by around 100 bps each in Q1 and Q2 (of FY23),” the RBI said in its latest MPR published on Sept. 30.

It added that a large part of the surprise increase came from food inflation due to global factors, while heat wave and depreciation in the rupee were also responsible for the projection errors.

Graphic: Components of Indian inflation

In the past two years, black swan events like the COVID-19 pandemic and the Ukraine-Russia war had overall made mathematical forecasting difficult, resulting in actual inflation overshooting the prediction, economists said.

The extreme volatility has raised the need for a rejig of inflation modelling.

There are multiple models that can factor in volatility like the ones seen in food prices this year, Kotak’s Bhardwaj said.

“But at some point, a reworking may be required to make them more dynamic.”



Source link

Related Articles

Leave a Reply

Back to top button
bitcoin
Bitcoin (BTC) $ 101,061.08 0.84%
ethereum
Ethereum (ETH) $ 3,079.88 3.06%
xrp
XRP (XRP) $ 3.06 0.73%
tether
Tether (USDT) $ 0.999616 0.05%
solana
Solana (SOL) $ 226.72 3.74%
bnb
BNB (BNB) $ 663.23 2.47%
usd-coin
USDC (USDC) $ 1.00 0.01%
dogecoin
Dogecoin (DOGE) $ 0.318347 4.10%
cardano
Cardano (ADA) $ 0.91558 1.80%
staked-ether
Lido Staked Ether (STETH) $ 3,077.60 2.99%
tron
TRON (TRX) $ 0.239483 2.82%
chainlink
Chainlink (LINK) $ 22.71 5.97%
avalanche-2
Avalanche (AVAX) $ 32.17 5.91%
wrapped-bitcoin
Wrapped Bitcoin (WBTC) $ 100,762.00 1.09%
wrapped-steth
Wrapped stETH (WSTETH) $ 3,647.18 3.28%
stellar
Stellar (XLM) $ 0.394666 2.61%
the-open-network
Toncoin (TON) $ 4.79 4.26%
hedera-hashgraph
Hedera (HBAR) $ 0.300064 4.75%
sui
Sui (SUI) $ 3.65 8.34%
shiba-inu
Shiba Inu (SHIB) $ 0.000018 5.06%
weth
WETH (WETH) $ 3,077.49 3.09%
leo-token
LEO Token (LEO) $ 9.70 0.74%
polkadot
Polkadot (DOT) $ 5.66 4.88%
litecoin
Litecoin (LTC) $ 110.47 4.05%
bitget-token
Bitget Token (BGB) $ 6.93 0.32%
bitcoin-cash
Bitcoin Cash (BCH) $ 410.30 3.93%
hyperliquid
Hyperliquid (HYPE) $ 22.00 4.90%
usds
USDS (USDS) $ 1.00 0.00%
uniswap
Uniswap (UNI) $ 11.12 1.65%
wrapped-eeth
Wrapped eETH (WEETH) $ 3,239.49 3.62%
ethena-usde
Ethena USDe (USDE) $ 1.00 0.02%
official-trump
Official Trump (TRUMP) $ 27.00 1.86%
near
NEAR Protocol (NEAR) $ 4.30 7.05%
pepe
Pepe (PEPE) $ 0.000012 11.72%
aave
Aave (AAVE) $ 284.36 6.31%
mantra-dao
MANTRA (OM) $ 4.43 5.88%
ondo-finance
Ondo (ONDO) $ 1.34 0.74%
aptos
Aptos (APT) $ 7.21 5.72%
internet-computer
Internet Computer (ICP) $ 8.60 3.49%
whitebit
WhiteBIT Coin (WBT) $ 28.25 0.05%
monero
Monero (XMR) $ 219.12 2.83%
ethereum-classic
Ethereum Classic (ETC) $ 24.96 3.88%
mantle
Mantle (MNT) $ 1.08 3.88%
bittensor
Bittensor (TAO) $ 440.86 1.65%
dai
Dai (DAI) $ 1.00 0.07%
crypto-com-chain
Cronos (CRO) $ 0.126955 3.83%
vechain
VeChain (VET) $ 0.04249 4.98%
polygon-ecosystem-token
POL (ex-MATIC) (POL) $ 0.390088 6.04%
okb
OKB (OKB) $ 53.33 0.60%
kaspa
Kaspa (KAS) $ 0.121938 3.63%