---
ticker: "KRTRINF"
title: "South Korea Trend Inflation"
unit: "%"
frequency: "Monthly"
source: "Stock and Watson (2007)"
release: "Updated Monthly"
category: "Inflation Expectations"
country: "KR"
language: "en"
canonical: "https://kred.dev/en/series/KRTRINF"
license: "https://creativecommons.org/licenses/by-nc-nd/4.0/"
latest_value: 2.39
latest_date: "2026-08-01"
first_date: "1991-01-01"
observations_total: 428
observations_shown: 120
---

# South Korea Trend Inflation

## Overview

The long-run persistent component of inflation, tracing the underlying price trend after stripping transitory shocks and noise.

## Key Figures

|  | Value | Date |
|---|---|---|
| Latest | 2.39 | 2026-08-01 |
| Change from previous | +0.05 | 2026-07-01 |
| Change over one year | +0.39 | 2025-08-01 |
| Highest on record | 4.41 | 1991-03-01 |
| Lowest on record | 1.28 | 2019-09-01 |
| Period covered | 1991-01-01 – 2026-08-01 |  |
| Observations | 428 |  |

## Recent observations

| Date | Value | Change |
|---|---|---|
| 2016-09-01 | 1.82 | +0.20 |
| 2016-10-01 | 1.88 | +0.06 |
| 2016-11-01 | 1.90 | +0.02 |
| 2016-12-01 | 1.90 | -0.01 |
| 2017-01-01 | 2.11 | +0.21 |
| 2017-02-01 | 2.10 | -0.01 |
| 2017-03-01 | 2.12 | +0.02 |
| 2017-04-01 | 2.06 | -0.06 |
| 2017-05-01 | 2.05 | -0.01 |
| 2017-06-01 | 2.03 | -0.02 |
| 2017-07-01 | 2.10 | +0.07 |
| 2017-08-01 | 2.17 | +0.07 |
| 2017-09-01 | 2.06 | -0.10 |
| 2017-10-01 | 1.98 | -0.09 |
| 2017-11-01 | 1.83 | -0.15 |
| 2017-12-01 | 1.84 | +0.01 |
| 2018-01-01 | 1.73 | -0.11 |
| 2018-02-01 | 1.82 | +0.09 |
| 2018-03-01 | 1.83 | +0.01 |
| 2018-04-01 | 1.90 | +0.07 |
| 2018-05-01 | 1.90 | 0.00 |
| 2018-06-01 | 1.89 | -0.01 |
| 2018-07-01 | 1.82 | -0.07 |
| 2018-08-01 | 1.91 | +0.09 |
| 2018-09-01 | 2.05 | +0.13 |
| 2018-10-01 | 2.05 | 0.00 |
| 2018-11-01 | 2.02 | -0.02 |
| 2018-12-01 | 1.84 | -0.18 |
| 2019-01-01 | 1.68 | -0.16 |
| 2019-02-01 | 1.58 | -0.10 |
| 2019-03-01 | 1.55 | -0.03 |
| 2019-04-01 | 1.58 | +0.02 |
| 2019-05-01 | 1.62 | +0.04 |
| 2019-06-01 | 1.62 | 0.00 |
| 2019-07-01 | 1.57 | -0.04 |
| 2019-08-01 | 1.40 | -0.17 |
| 2019-09-01 | 1.28 | -0.12 |
| 2019-10-01 | 1.37 | +0.08 |
| 2019-11-01 | 1.43 | +0.06 |
| 2019-12-01 | 1.59 | +0.16 |
| 2020-01-01 | 1.72 | +0.13 |
| 2020-02-01 | 1.63 | -0.09 |
| 2020-03-01 | 1.60 | -0.03 |
| 2020-04-01 | 1.39 | -0.20 |
| 2020-05-01 | 1.32 | -0.07 |
| 2020-06-01 | 1.42 | +0.10 |
| 2020-07-01 | 1.51 | +0.08 |
| 2020-08-01 | 1.60 | +0.10 |
| 2020-09-01 | 1.65 | +0.04 |
| 2020-10-01 | 1.46 | -0.19 |
| 2020-11-01 | 1.55 | +0.10 |
| 2020-12-01 | 1.57 | +0.02 |
| 2021-01-01 | 1.67 | +0.10 |
| 2021-02-01 | 1.83 | +0.16 |
| 2021-03-01 | 1.98 | +0.16 |
| 2021-04-01 | 2.14 | +0.16 |
| 2021-05-01 | 2.19 | +0.05 |
| 2021-06-01 | 2.15 | -0.05 |
| 2021-07-01 | 2.21 | +0.06 |
| 2021-08-01 | 2.21 | -0.01 |
| 2021-09-01 | 2.19 | -0.02 |
| 2021-10-01 | 2.39 | +0.20 |
| 2021-11-01 | 2.58 | +0.20 |
| 2021-12-01 | 2.57 | -0.01 |
| 2022-01-01 | 2.59 | +0.02 |
| 2022-02-01 | 2.62 | +0.02 |
| 2022-03-01 | 2.75 | +0.13 |
| 2022-04-01 | 2.94 | +0.19 |
| 2022-05-01 | 3.13 | +0.20 |
| 2022-06-01 | 3.39 | +0.25 |
| 2022-07-01 | 3.54 | +0.16 |
| 2022-08-01 | 3.35 | -0.19 |
| 2022-09-01 | 3.27 | -0.08 |
| 2022-10-01 | 3.30 | +0.03 |
| 2022-11-01 | 3.14 | -0.16 |
| 2022-12-01 | 3.08 | -0.05 |
| 2023-01-01 | 3.08 | -0.01 |
| 2023-02-01 | 2.99 | -0.08 |
| 2023-03-01 | 2.84 | -0.15 |
| 2023-04-01 | 2.68 | -0.16 |
| 2023-05-01 | 2.56 | -0.13 |
| 2023-06-01 | 2.38 | -0.18 |
| 2023-07-01 | 2.27 | -0.10 |
| 2023-08-01 | 2.52 | +0.25 |
| 2023-09-01 | 2.63 | +0.11 |
| 2023-10-01 | 2.65 | +0.02 |
| 2023-11-01 | 2.54 | -0.11 |
| 2023-12-01 | 2.47 | -0.07 |
| 2024-01-01 | 2.37 | -0.10 |
| 2024-02-01 | 2.43 | +0.06 |
| 2024-03-01 | 2.44 | +0.02 |
| 2024-04-01 | 2.38 | -0.06 |
| 2024-05-01 | 2.32 | -0.06 |
| 2024-06-01 | 2.23 | -0.09 |
| 2024-07-01 | 2.23 | 0.00 |
| 2024-08-01 | 2.10 | -0.14 |
| 2024-09-01 | 1.97 | -0.13 |
| 2024-10-01 | 1.88 | -0.08 |
| 2024-11-01 | 1.95 | +0.07 |
| 2024-12-01 | 2.06 | +0.12 |
| 2025-01-01 | 2.13 | +0.07 |
| 2025-02-01 | 2.08 | -0.05 |
| 2025-03-01 | 2.09 | 0.00 |
| 2025-04-01 | 2.10 | +0.01 |
| 2025-05-01 | 2.05 | -0.05 |
| 2025-06-01 | 2.08 | +0.03 |
| 2025-07-01 | 2.07 | -0.01 |
| 2025-08-01 | 2.00 | -0.07 |
| 2025-09-01 | 2.08 | +0.08 |
| 2025-10-01 | 2.16 | +0.08 |
| 2025-11-01 | 2.18 | +0.02 |
| 2025-12-01 | 2.15 | -0.04 |
| 2026-01-01 | 2.08 | -0.07 |
| 2026-02-01 | 2.07 | 0.00 |
| 2026-03-01 | 2.13 | +0.06 |
| 2026-04-01 | 2.27 | +0.13 |
| 2026-05-01 | 2.40 | +0.13 |
| 2026-06-01 | 2.41 | +0.01 |
| 2026-07-01 | 2.33 | -0.08 |
| 2026-08-01 | 2.39 | +0.05 |

## Definition

Trend inflation is the long-run persistent component of inflation, namely the underlying rate of price increase that remains after removing transitory supply shocks, seasonal fluctuations, base effects, and measurement noise. It represents the inflation regime toward which observed inflation gravitates over the medium to long term, and can be interpreted as the economy's 'steady-state' inflation rate.

Observed inflation contains two distinct components. One is a slowly evolving trend driven by monetary policy credibility, the anchoring of expectations, and structural economic forces, and the other is a volatile transitory component driven by supply shocks (oil prices, food prices, exchange rate pass-through), tax changes, and measurement idiosyncrasies. Since the trend is a latent component that cannot be directly observed in market prices, it is extracted using an unobserved-components model.

A rise in trend inflation indicates strengthening underlying price pressure, while a fall indicates its easing. Under a credible inflation-targeting regime, trend inflation gravitates toward the announced target over time, and persistent deviations from the target may indicate erosion of central bank credibility, structural shifts in price-setting behavior, or supply-side forces that monetary policy cannot easily offset (Carvalho et al. 2023).

## Methodology

Extracted from CPI year-over-year (headline, from January 1999) using the Unobserved Components Stochastic Volatility (UCSV) model of Stock and Watson (2007).

**(1) State-Space Setup.** The observation and trend equations are expressed in the following state space:

$$\begin{aligned} \pi_t &= \tau_t + \exp(h_t / 2) \, \varepsilon_t \\ \tau_t &= \tau_{t-1} + \exp(g_t / 2) \, \eta_t \end{aligned}$$

The log-volatility processes follow:

$$\begin{aligned} h_t &= h_{t-1} + \sigma_h \, w_t \\ g_t &= g_{t-1} + \sigma_g \, v_t \end{aligned}$$

where $\tau_t$ is trend inflation, $h_t$ is observation log-volatility, and $g_t$ is trend innovation log-volatility.

**(2) Gibbs Sampling.** Estimation uses the precision-based Gibbs sampler of Chan and Jeliazkov (2009), which exploits the tridiagonal structure of the precision matrix for $O(T)$ sampling. The nonlinear SV observation equation is linearized via the 10-component Gaussian mixture approximation to $\log(\chi^2(1))$ from Omori et al. (2005). The sampler cycles through five Gibbs blocks, drawing $\tau_{1:T}$ via precision-based sampling, $h_{1:T}$ and $g_{1:T}$ via the KSC mixture, the mixture indicators via a multinomial draw, and $\sigma^2_h, \sigma^2_g$ from an inverse-gamma conjugate prior.

**(3) MCMC Settings.** The sampler uses 2,000 burn-in draws, 5,000 posterior samples, an IG prior with $\nu_0 = 10$ and $E[\sigma^2] = 0.02$, and a diffuse initial state variance $V_0 = 10.0$. The trend is initialized with a 12-month moving average to smooth seasonality.

## Applications in Economics

Trend inflation provides a benchmark for measuring the inflation gap in policy rate rules that is not distorted by transitory shocks. In the Taylor (1993) rule and its modern extensions, the optimal policy rate responds to the inflation gap, namely the deviation of current inflation from the target. Using trend inflation rather than headline inflation to compute this gap yields a structurally cleaner measure, enabling policymakers to assess whether the current policy rate is appropriate for the medium-term inflation trajectory.

Changes in trend inflation are associated with shifts in the monetary policy regime. The concept originates from the permanent-transitory decomposition of macroeconomic time series, applied to inflation by Beveridge and Nelson (1981) and refined by Stock and Watson (2007). Cogley and Sargent (2005) and Cogley, Primiceri, and Sargent (2010) demonstrate that movements in trend inflation track changes in the policy regime. During periods of high and rising trend inflation (e.g., the 1970s in the U.S.), monetary policy responded insufficiently to inflation, violating the Taylor principle ($\partial i / \partial \pi > 1$), and inflation rose in a self-reinforcing manner. The subsequent decline in trend inflation during the Volcker disinflation and the Great Moderation reflected a credible commitment to price stability.

For Korea, trend inflation dynamics have been shaped by several structural factors. Rapid productivity growth during the industrialization period had a disinflationary effect, the Asian Financial Crisis of 1997–98 produced a sharp but temporary inflation spike, and after the adoption of formal inflation targeting in 1998 Korean inflation gradually converged toward advanced-economy norms. Since 2020, the global inflation surge driven by supply chain disruptions and energy price shocks has tested the anchoring of Korean trend inflation.

Trend inflation also provides the appropriate benchmark for computing 'core' inflation gaps and for calibrating the inflation component in financial conditions indices (FCIs). Chan, Koop, and Potter (2013) show that the UCSV trend outperforms traditional core measures (trimmed mean, weighted median, ex-food-and-energy) in forecasting future headline inflation, because it adaptively filters out different types of transitory shocks rather than mechanically excluding fixed categories.

The UCSV model's allowance of time-varying volatility in both the trend and the transitory component is empirically important for trend extraction. Inflation volatility varies substantially across regimes, from the Great Inflation of the 1970s to the Great Moderation and through the post-COVID inflation surge, and a model that imposes constant volatility produces biased trend estimates during regime transitions. The model's stochastic volatility structure further provides an additional informational dimension, where the estimated variance of trend innovations ($\exp(g_t)$) measures the degree of uncertainty about the inflation regime itself. High trend innovation volatility signals that the inflation regime may be shifting, which has implications for the conduct and communication of monetary policy.

## Applications in Financial Markets

Changes in trend inflation signal shifts in long-run inflation expectations, with direct implications for nominal bond yields, real return outlooks, and inflation-linked asset valuations.

For bond investors, trend inflation is a key determinant of the long-run level of nominal yields. A secular decline in trend inflation, as observed in Korea and other advanced economies from the late 1990s through the 2010s, is associated with a structural downshift in equilibrium bond yields. Cieslak and Povala (2015) show that decomposing inflation into a trend and a cyclical component substantially improves bond return predictability, as the trend component captures the persistent shifts in yield levels that drive long-horizon returns.

The stochastic volatility structure of the UCSV model provides a natural measure of inflation uncertainty, which is itself a priced risk factor in bond markets. Wright (2011) demonstrated that inflation uncertainty is positively correlated with term premiums, and when the inflation regime is uncertain, investors demand higher compensation for holding nominal bonds. The UCSV's time-varying volatility estimates can therefore serve as inputs for term premium models and inflation risk premium estimation.

For asset allocators, trend inflation informs the real return outlook for different asset classes. When trend inflation is rising, nominal bonds face headwinds (capital losses from rising yields), while commodities, real estate, and inflation-protected securities tend to outperform. Conversely, declining trend inflation is favorable for long-duration nominal bonds and growth equities whose valuations benefit from lower discount rates.

Unlike the U.S. TIPS market, Korea has no inflation-linked bond market, so model-based trend inflation estimates like the UCSV provide the only systematic measure of long-run inflation expectations. This makes the KRED trend inflation series particularly valuable for Korean institutional investors who need to assess inflation risk exposure without a market-implied breakeven inflation benchmark.

## Statistical Tests

KRTRINF is the permanent component of the Stock-Watson unobserved-components stochastic-volatility inflation model, a trend extracted by a two-sided Gibbs smoother over the full sample. Over 425 monthly observations from 1991-01-01 to 2026-05-01, its measured serial correlation is dominated by the smoother's gain rather than by the data-generating process, so the object framed here is a persistence summary of the smoothed path and not an integration order of the data.

The integration-order battery is therefore deliberately not run. The augmented unit-root regression of Dickey and Fuller (1979) with the lag augmentation of Said and Dickey (1984), the semiparametric Phillips and Perron (1988) test, the KPSS stationarity test (Kwiatkowski et al. 1992), the efficient GLS-detrended test of Elliott, Rothenberg, and Stock (1996), and the modified M-tests of Ng and Perron (2001) are all excluded, because a symmetric two-sided filter manufactures the persistence an integration test reads and the random-walk-versus-constant character of the latent state is not point-identified by the likelihood (Stock and Watson 1998; Orphanides and van Norden 2002). The level mean-break search of Bai and Perron (1998) is likewise not run, since its asymptotics require a stationary object and the filter-persistent path spuriously segments (Perron 1989).

The matrix-mandated replacement is a descriptive persistence summary labeled as a property of the smoothed series. The lag-one autocorrelation is 0.970 and the implied half-life is 22.52 months, a description of how slowly the smoothed path decays that carries no integration-order claim. Because the UCSV trend is drawn from the full sample and the loader rewrites the state history on every re-run, the smoothed component at any fixed past month revises across vintages, and no stored vintage panel exists to quantify the revision magnitude (Orphanides and van Norden 2002).

No order of integration is assigned, by ruling rather than by an inconclusive test. The lag-one coefficient carries the familiar downward small-sample bias near unity, so the half-life is a lower-leaning descriptive figure whose median-unbiased interval would follow the grid of Andrews (1993), and the lag-one autocorrelation a portmanteau such as that of Ljung and Box (1978) would register is a filter-gain reading rather than evidence about the process. The model-implied content is honest as an assumption rather than a discovery, namely a stochastic-trend permanent component whose innovation variance is drawn by a precision-based Gibbs sampler over the full sample, so its near-unity persistence is the two-sided smoother's, and the median-unbiased identification logic of Stock and Watson (1998) is what keeps that persistence from being read as an integration order.

## Frequently Asked Questions

### What is trend inflation?

The permanent component that remains once transitory supply shocks, seasonal variation, base effects and measurement noise are removed. It is read as the steady-state rate towards which observed inflation converges over the medium to long run.

### Why does the trend inflation model let the variance change over time?

Inflation volatility differs markedly across periods. Holding the variance fixed forces the model to treat calm and turbulent stretches as equally informative about the trend, which leads it to read a transitory shock in a turbulent period as a shift in trend.

### Is the trend inflation estimate revised later?

It is. Being a smoothed estimate, a new observation updates the trend at earlier dates as well, with the largest revisions in the most recent stretch.