Sectoral inflation and the Phillips curve: What has changed since the Great Recession?

Luengo‐Prado, María José; Rao, Nikhil; Sheremirov, Viacheslav

doi:10.1016/j.econlet.2018.08.016

Cited by 15 publications

(14 citation statements)

References 7 publications

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“…We find that it does. As noted above, using data only up through 2006 for coefficient estimation, we find that recursive forecasts from our specification (conditioned only on historical 7 See Luengo-Prado, Rao, and Sheremirov (2018). Laxton, Meredith, and Rose (1995) and Debelle and Laxton (1997) also draw attention to the weak relationship between positive slack and inflation.…”

Section: But Does This New Reduced-form Phillips Curve Specification mentioning

confidence: 55%

“…A mismeasured gap will likely lead to the conclusion that forecasting performance is episodic (for example, Stock and Watson 2009) or that there is time variation in the inflation process, such as time variation in the coefficient on the activity variable (see evidence in Clark and McCracken 2006, Stock and Watson 2009, Vavra 2013and Luengo-Prado, Rao, and Sheremirov, 2018. This may explain the forecasting performance of the time-varying unobserved components model of Stock and Watson (2007).…”

Section: A4 Relation To Some Other Findings In the Literaturementioning

confidence: 99%

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Finding a Stable Phillips Curve Relationship: A Persistence-Dependent Regression Model

Ashley

Verbrugge

2020

Working Paper (Federal Reserve Bank of Cleveland)

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We establish that the Phillips curve is persistence-dependent: inflation responds differently to persistent versus moderately persistent (or versus transient) fluctuations in the unemployment gap. Previous work fails to model this dependence, so it finds numerous "inflation puzzles"-such as missing inflation/ disinflation-noted in the literature. Our model specification eliminates these puzzles; for example, the Phillips curve has not weakened, and inflation is not "stubbornly low" at present. The model's coefficients are stable, and it provides accurate conditional recursive forecasts through the Great Recession. The persistence-dependent relationship we uncover is interpretable as being businesscycle-phase-dependent and is thus consistent with existing theory. JEL Classification Codes: E00, E31, C22, C32, E5.

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Section: But Does This New Reduced-form Phillips Curve Specification mentioning

confidence: 55%

Section: A4 Relation To Some Other Findings In the Literaturementioning

confidence: 99%

Finding a Stable Phillips Curve Relationship: A Persistence-Dependent Regression Model

Ashley

Verbrugge

2020

Working Paper (Federal Reserve Bank of Cleveland)

View full text Add to dashboard Cite

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“…A mismeasured gap will likely lead to the conclusion that forecasting performance is episodic (e.g., Stock and Watson 2009) or that there is time variation in the inflation process, such as time variation in the coefficient on the activity variable (see evidence in Clark and McCracken 2006, Stock and Watson 2009, Vavra 2014and Luengo-Prado, Rao and Sheremirov, 2017. This may explain the forecasting performance of the time-varying unobserved components model of Stock and Watson (2007).…”

Section: A4 Relation To Some Other Findings In the Literaturementioning

confidence: 99%

“…Using a richer model specification, this paper explains the true nature of the Phillips relationship, including its apparent weakening. In addition to rationalizing the missing disinflation puzzle, we provide a unified explanation of several other prominent recent findings, such as the odd behavior of the reverse-engineered NAIRU in Coibion and Gorodnichenko (2015), the fact that the Phillips relationship appears to be only episodically helpful in forecasting Watson, 2009, 2010), that it appears to have a convex-concave relationship (e.g., Barnes and Olivei 2003), that it is time-varying (e.g., McCracken 2006 or Stock andWatson 2007), and that it appears to have vanished after 2009 (Luengo-Prado, Rao, and Sheremirov, 2017). We do this without reference to the short-term unemployment rate (cf.…”

Section: Introductionmentioning

confidence: 99%

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Variation in the Phillips Curve Relation Across Three Phases of the Business Cycle

Ashley

Verbrugge

2019

Working Paper (Federal Reserve Bank of Cleveland)

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We use recently developed econometric tools to demonstrate that the Phillips curve unemployment rate-inflation rate relationship varies in an economically meaningful way across three phases of the business cycle. The first ("bust phase") relationship is the one highlighted by Stock and Watson (2010): A sharp reduction in inflation occurs as the unemployment rate is rising rapidly. The second ("recovery phase") relationship occurs as the unemployment rate subsequently begins to fall; during this phase, inflation is unrelated to any conventional unemployment gap. The final ("overheating phase") relationship begins once the unemployment rate drops below its natural rate. We validate our findings in a forecasting exercise and find statistically significant episodic forecast improvement. Our analysis allows us to provide a unified explanation of many prominent findings in the literature.

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Forecasting food price inflation during global crises

Toledo,

Duncan

2024

Journal of Forecasting

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In this paper, we consider the forecasting of domestic food price inflation (DFPI) using global indicators, with emphasis on episodes of macroeconomic turbulence, namely, the Global Financial Crisis (GFC) and the COVID‐19 pandemic and its subsequent repercussions. Our monthly dataset covers about two decades for more than a hundred economies. We employ dynamic model averaging (DMA) to tackle both model uncertainty and parameter instability and produce pseudo out‐of‐sample forecasts. Thus, we are able to focus on the forecasting ability of the global predictors of DFPI before and during the global crises. We find evidence that the DMA specification tends to outperform statistical models frequently used in the literature such as random walks, autoregressive models, and time‐varying parameter models, especially during global crises. We also identify the most successful predictors during the crises using their posterior probabilities of inclusion. By comparing the distributions of such probabilities, we find that the international food price inflation is the most useful predictor of DFPI for numerous countries during both crises. Other indicators such as domestic CPI inflation as well as the international inflation of agricultural commodities, fertilizers, and other food categories improved their forecasting ability, particularly during the COVID‐19 period.

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Sectoral inflation and the Phillips curve: What has changed since the Great Recession?

Cited by 15 publications

References 7 publications

Finding a Stable Phillips Curve Relationship: A Persistence-Dependent Regression Model

Finding a Stable Phillips Curve Relationship: A Persistence-Dependent Regression Model

Variation in the Phillips Curve Relation Across Three Phases of the Business Cycle

Forecasting food price inflation during global crises

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