Macroeconometric and financial researchers often use secondary or constructed binary random variables that differ in terms of their statistical properties from the primary random variables used in micro-econometric studies. One important difference between primary and secondary binary variables is that, while the former are, in many instances, independently distributed (i.d.), the latter are rarely i.d. We show how popular rules for constructing the binary states interact with the stochastic processes for of the variables they are constructed from, so that the binary states need to be treated as Markov processes. Consequently, one needs to recognize this when performing analyses with the binary variables, and it is not valid to adopt a model like static Probit which fails to recognize such dependence. Moreover, these binary variables are often censored, in that they are constructed in such a way as to result in sequences of them possessing the same sign. Such censoring imposes restrictions upon the DGP of the binary states and it creates difficulties if one tries to utilize a dynamic Probit model with them. Given this we describe methods for modeling with these variables that both respects their Markov process nature and which explicitly deals with any censoring constraints. An application is provided that investigates the relation between the business cycle and the yield spread.
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Paper provided by National Centre for Econometric Research in its series NCER Working Paper Series with number
39.
Find related papers by JEL classification: C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation
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Daniel M. Chin & John F. Geweke & Preston J. Miller, 2000.
"Predicting turning points,"
Staff Report
267, Federal Reserve Bank of Minneapolis.
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