Are Phillips Curves Useful for Forecasting Inflation?

Atkeson, Andrew; Ohanian, Lee E.

doi:10.21034/qr.2511

Cited by 421 publications

(561 citation statements)

References 17 publications

(19 reference statements)

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“…The first no-change forecast is the past month's inflation rate; the second no-change forecast uses the past year's inflation rate as its forecast. The former matches the no-change forecast in Stock and Watson (1999) and the latter matches the no-change forecast in Atkeson and Ohanian (2001). Stock and Watson also presented results for forecast combinations and forecasts based on principal component indicator variables.…”

Section: Simulation Experiments Ii: Regression Modelsmentioning

confidence: 59%

“…This turns out to matter for the second subsample, because the no-change (year) forecast has the smallest mean square prediction error (MSPE) of all forecasts. This enables us to reconcile Stock and Watson (1999) with Atkeson and Ohanian (2001) by showing that their different definitions of the benchmark forecast-no-change (month) and no-change (year), respectively-explain the different conclusions they reach about these forecasts.…”

Section: Empirical Analysis Of Inflation Forecasts and Taylor Rulesmentioning

confidence: 99%

“…Stock and Watson (1999) made the case that "a reasonably specified Phillips curve is the best tool for forecasting inflation"; also see Gordon (1997), Staiger, Stock, and Watson (1997b), and Stock and Watson (2003). Atkeson and Ohanian (2001) concluded that this is not the case because they found it is difficult for any of the Phillips curves they studied to beat a simple no-change forecast in out-ofsample point prediction.…”

Section: Empirical Analysis Of Inflation Forecasts and Taylor Rulesmentioning

confidence: 99%

“…We come to this conclusion because the Stock and Watson no-change forecast never ends up in the MCS. Next, we add the no-change forecast employed by Atkeson and Ohanian (2001) to the comparison. Their forecast uses the past year's inflation rate as the point prediction rather than month-over-month inflation.…”

Section: Empirical Analysis Of Inflation Forecasts and Taylor Rulesmentioning

confidence: 99%

“…The set of inflation forecasting models includes several that have a Phillips curve interpretation, along with autoregressive and a no-change (month-over-month) forecast. We extend their set of forecasts by adding a second no-change (12-monthsover-12-months) forecast that was used in Atkeson and Ohanian (2001). Stock and Watson (1999) measured inflation, π t , as either the CPI-U, all items (PUNEW), or the headline personal consumption expenditure implicit price deflator (GMDC).…”

Section: Simulation Experiments Ii: Regression Modelsmentioning

confidence: 99%

See 4 more Smart Citations

The Model Confidence Set

2011

Econometrica

1,549

110

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This paper introduces the model confidence set (MCS) and applies it to the selection of models. A MCS is a set of models that is constructed such that it will contain the best model with a given level of confidence. The MCS is in this sense analogous to a confidence interval for a parameter. The MCS acknowledges the limitations of the data, such that uninformative data yield a MCS with many models, whereas informative data yield a MCS with only a few models. The MCS procedure does not assume that a particular model is the true model; in fact, the MCS procedure can be used to compare more general objects, beyond the comparison of models. We apply the MCS procedure to two empirical problems. First, we revisit the inflation forecasting problem posed by Stock and Watson (1999), and compute the MCS for their set of inflation forecasts. Second, we compare a number of Taylor rule regressions and determine the MCS of the best regression in terms of in‐sample likelihood criteria.

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Section: Simulation Experiments Ii: Regression Modelsmentioning

confidence: 59%

Section: Empirical Analysis Of Inflation Forecasts and Taylor Rulesmentioning

confidence: 99%

Section: Empirical Analysis Of Inflation Forecasts and Taylor Rulesmentioning

confidence: 99%

Section: Empirical Analysis Of Inflation Forecasts and Taylor Rulesmentioning

confidence: 99%

Section: Simulation Experiments Ii: Regression Modelsmentioning

confidence: 99%

See 3 more Smart Citations

The Model Confidence Set

2011

Econometrica

1,549

110

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Deep learning models for inflation forecasting

Theoharidis

Guillén

Lopes

2023

Appl Stoch Models Bus & Ind

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We propose a hybrid deep learning model that merges Variational Autoencoders and Convolutional LSTM Networks (VAE-ConvLSTM) to forecast inflation. Using a public macroeconomic database that comprises 134 monthly US time series from January 1978 to December 2019, the proposed model is compared against several popular econometric and machine learning benchmarks, including Ridge regression, LASSO regression, Random Forests, Bayesian methods, VECM, and multilayer perceptron. We find that VAE-ConvLSTM outperforms the competing models in terms of consistency and out-of-sample performance. The robustness of such conclusion is ensured via cross-validation and Monte-Carlo simulations using different training, validation, and test samples. Our results suggest that macroeconomic forecasting could take advantage of deep learning models when tackling nonlinearities and nonstationarity, potentially delivering superior performance in comparison to traditional econometric approaches based on linear, stationary models.

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Measuring Disagreement in Qualitative Expectations

Mokinski

Sheng

Yang

2015

Journal of Forecasting

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We assess how well measures of disagreement in qualitative survey expectations reflect disagreement in corresponding quantitative expectations. We consider a variety of measures, belonging to two categories: measures of dispersion in nominal and ordinal variables and measures based on the probability approach of Carlson and Parkin (Economica, 1975; 42, 123–138). Using data from two household surveys that collect both qualitative and quantitative inflation expectations, we find that the probability approaches with time‐varying categorization thresholds and either a piecewise uniform or t distribution perform best and the resulting disagreement estimates are highly correlated with the benchmark. Copyright © 2015 John Wiley & Sons, Ltd.

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Are Phillips Curves Useful for Forecasting Inflation?

Abstract: This publication primarily presents economic research ai med at improving policymaking by the Federal Reserve System and other governmental authorities.

Cited by 421 publications

References 17 publications

The Model Confidence Set

The Model Confidence Set

Deep learning models for inflation forecasting

Measuring Disagreement in Qualitative Expectations

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