Predicting U.S. Recessions with Dynamic Binary Response Models

By Heikki Kauppi, Pentti Saikkonen. Review of Economics and Statistics, 2008

357 citations recorded by the source corpus.

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Household Loan Loss Risk in Finland - Estimations and Simulations With Micro Data (2007, unknown): “See Kauppi and Saikkonen (2006) for a discussion of many-step-ahead forecast tests in binary response models.”
Forecasting the direction of the US stock market with dynamic binary probit models (2011, methodology): “Parameters of the probit models (4)–(6), as well as those of the case of a new model presented in the next section, can be estimated by the method of maximum likelihood as described in de Jong and Woutersen (2007) and Kauppi and Saikkonen (2007).”
Forecasting US Recessions: The Role of Sentiment (2014, unknown): “However, Estrella and Mishkin (1998) and Kauppi and Saikkonen (2008) show that the latest value of the explanatory variables is not necessarily the best in terms of predictive power.”
Dynamic Probit Models and Financial Variables in Recession Forecasting (2010, methodology): “An interesting extension of model (7) is obtained by including an interaction term: π ω α π β γt t t k t a t ky z= + + ′ + ′− − − −1 1 x (8) 1 We use the terminology of Kauppi and Saikkonen (2008).”
DYNAMIC TIME SERIES BINARY CHOICE (2011, methodology): “In order to achieve an empirically relavant setup for conducting our simulations experiment we use the dynamic binary choice models that are proposed by Kauppi and Saikkonen (2008).”