59 research outputs found
Testing an autoregressive structure in binary time series models
This paper introduces a Lagrange Multiplier (LM) test for testing an autoregressive structure in a binary time series model proposed by Kauppi and Saikkonen (2008). Simulation results indicate that the two versions of the proposed LM test have reasonable size and power properties when the sample size is large. A parametric bootstrap method is suggested to obtain approximately correct sizes also in small samples. The use of the test is illustrated by an application to recession forecasting models using monthly U.S. data.LM test, Binary response, Dynamic probit model, Parametric bootstrap, Recession forecasting
QR-GARCH-M Model for Risk-Return Tradeoff in U.S. Stock Returns and Business Cycles
In the empirical finance literature findings on the risk return tradeoff in excess stock market returns are ambiguous. In this study, we develop a new QR-GARCH-M model combining a probit model for a binary business cycle indicator and a regime switching GARCH-in-mean model for excess stock market return with the business cycle indicator defining the regime. Estimation results show that there is statistically significant variation in the U.S. excess stock returns over the business cycle. However, consistent with the conditional ICAPM, there is a positive risk-return relationship between volatility and expected return independent of the state of the economy.Regime switching GARCH model; GARCH-in-mean model; probit model; stock return; risk-return tradeoff; business cycle
QR-GARCH-M Model for Risk-Return Tradeoff in U.S. Stock Returns and Business Cycles
In the empirical finance literature findings on the risk-return tradeoff in excess stock market returns are ambiguous. In this study, we develop a new QR-GARCH-M model combining a probit model for a binary business cycle indicator and a regime switching GARCH-in-mean model for excess stock market return with the business cycle indicator defining the regime. Estimation results show that there is statistically significant variation in the U.S. excess stock returns over the business cycle. However, consistent with the conditional ICAPM, there is a positive risk-return relationship between volatility and expected return independent of the state of the economy
A Qualitative Response VAR Model : An Application to Joint Dynamics of U.S. Interest Rates and Business Cycle
Forecasting US interest rates and business cycle with a nonlinear regime switching VAR model
This paper introduces a regime switching vector autoregressive model with time-varying regime probabilities, where the regime switching dynamics is described by an observable binary response variable predicted simultaneously with the variables subject to regime changes. Dependence on the observed binary variable distinguishes the model from various previously proposed multivariate regime switching models, facilitating a handy simulation-based multistep forecasting method. An empirical application shows a strong bidirectional predictive linkage between US interest rates and NBER business cycle recession and expansion periods. Due to the predictability of the business cycle regimes, the proposed model yields superior out-of-sample forecasts of the US short-term interest rate and the term spread compared with the linear and nonlinear vector autoregressive (VAR) models, including the Markov switching VAR model.Peer reviewe
Testing an Autoregressive Structure in Binary Time Series Models
This paper introduces a Lagrange Multiplier (LM) test for testing an autoregressive structure in a binary time series model proposed by Kauppi and Saikkonen (2008). Simulation results indicate that the two versions of the proposed LM test have reasonable size and power properties when the sample size is large. A parametric bootstrap method is suggested to obtain approximately correct sizes also in small samples. The use of the test is illustrated by an application to recession forecasting models using monthly U.S. data. JEL Classification: C12, C22, C2
Forecasting U.S. Macroeconomic and Financial Time Series with Noncausal and Causal AR Models: A Comparison
In this paper, we compare the forecasting performance of univariate noncausal and conventional causal autoregressive models for a comprehensive data set consisting of 170 monthly U.S. macroeconomic and financial time series. The noncausal models consistently outperform the causal models in terms of the mean square and mean absolute forecast errors. For a set of 18 quarterly time series, the improvement in forecast accuracy due to allowing for noncausality is found even greater.Noncausal autoregression; forecast comparison; macroeconomic variables; financial variables
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