Predicting U.S. Recessions: Financial Variables as Leading Indicators

Estrella, Arturo; Mishkin, Frederic S.

doi:10.1162/003465398557320

Cited by 1,025 publications

(638 citation statements)

References 16 publications

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“…For our method, it is crucial that one estimates the model for horizon l, and elicits information from y t on J t+l . For leading indicators, the same observation is made in Estrella and Mishkin (1998), and in Cox (1961), in the context of a forecast generated by EWMA, when in fact the data generator is not the corresponding integrated moving average process.…”

Section: Relations To Previous Hmm Studiesmentioning

confidence: 72%

A classifying procedure for signalling turning points

Koskinen

Öller

2004

Journal of Forecasting

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Abstract. A Hidden Markov Model (HMM) is used to classify an out of sample observation vector into either of two regimes. This leads to a procedure for making probability forecasts for changes of regimes in a time series, i.e. for turning points.Instead of maximizing a likelihood, the model is estimated with respect to known past regimes. This makes it possible to perform feature extraction and estimation for different forecasting horizons. The inference aspect is emphasized by including a penalty for a wrong decision in the cost function. The method is tested by forecasting turning points in the Swedish and US economies, using leading data. Clear and early turning point signals are obtained, contrasting favourable with earlier HMM studies.Some theoretical arguments for this are given.

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Section: Relations To Previous Hmm Studiesmentioning

confidence: 72%

A classifying procedure for signalling turning points

Koskinen

Öller

2004

Journal of Forecasting

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“…Indeed, one of the most popular leading indicators of the US growth is the spread between long-term and short-term interest rates (Estrella and Hardouvelis, 1991;Hamilton and Kim, 2002). In contrast, Stock and Watson (2003) conclude that stock returns have only marginal content for predicting output growth, although the results of Estrella and Mishkin (1998) suggest some power in predicting recessions at short horizons. Short-term interest rates are not as popular indicators as the spread, but recently Ang, Piazzesi and Wei (2006) argue that it is a better leading indicator than the spread from 1990 onwards.…”

Section: Introductionmentioning

confidence: 99%

Changes in predictive ability with mixed frequency data

Galvão

2013

International Journal of Forecasting

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This paper proposes a new regression model-a smooth transition mixed data sampling (STMIDAS) approach-that captures recurrent changes in the ability of a high frequency variable in predicting a low frequency variable. The STMIDAS regression is employed for testing changes in the ability of financial variables in forecasting US output growth. The estimation of the optimal weights for aggregating weekly data inside the quarter improves the measurement of the predictive ability of the yield curve slope for output growth. Allowing for changes in the impact of the short-rate and the stock returns in future growth is decisive for finding in-sample and out-of-sample evidence of their predictive ability at horizons longer than one year.

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“…Such models estimated probability of economy being in recession (binary outcome) as a function of explanatory variables and their lags and leads only. Result of Estrella and Mishkin (1998) about recession forecasting power of interest rate spreads and stock prices was received using a static binary model. Variables such as interest rates and spreads, stock prices, and monetary aggregates, together with other financial and nonfinancial indicators, were tested for potential early warning properties.…”

Section: Recession and Growth Cycle Modeling Literaturementioning

confidence: 99%