Robustness of the Risk-Return Relationship in the U.S. Stock Market

In this paper, we study the risk-return relationship in monthly U.S. stock returns (1928:1— 2004:12) using GARCH-in-Mean models. In particular, we consider the robustness of the relationship with respect to the omission of the intercept term in the equation for the expected excess return recently recommended by Lanne and Saikkonen (2006). The existence of the relationship is quite robust, but its estimated strength is dependent on the prior belief concerning the intercept. This is the case in particular in the first half of the sample period, where also the coefficient of the relative risk aversion is found to be smaller and the equity premium greater than in the latter half.ICAPM model; relative risk aversion; GARCH-in-Mean model; Bayesian analysis

Has U.S. Inflation Really Become Harder to Forecast?

Recently Stock and Watson (2007) showed that since the mid-1980s it has been hard for backward-looking Phillips curve models to improve on simple univariate models in forecasting U.S. inflation. While this indeed is the case when the benchmark is a causal autoregression, little change in forecast accuracy is detected when a noncausal autoregression is taken as the benchmark. In this note, we argue that a noncausal autoregression indeed provides a better characterization of U.S. inflation dynamics than the conventional causal autoregression and it is, therefore, the appropriate univariate benchmark model.Inflation forecast; Noncausal time series; Phillips curve

Autoregression-Based Estimation of the New Keynesian Phillips Curve

We propose an estimation method of the new Keynesian Phillips curve (NKPC) based on a univariate noncausal autoregressive model for the inflation rate. By construction, our approach avoids a number of problems related to the GMM estimation of the NKPC. We estimate the hybrid NKPC with quarterly U.S. data (1955:1-2010:3), and both expected future inflation and lagged inflation are found important in determining the inflation rate, with the former clearly dominating. Moreover, inflation persistence turns out to be intrinsic rather than inherited from a persistent driving process.Noncausal time series; Non-Gaussian time series; inflation; Phillips curve

Optimal Forecasting of Noncausal Autoregressive Time Series

In this paper, we propose a simulation-based method for computing point and density forecasts for univariate noncausal and non-Gaussian autoregressive processes. Numerical methods are needed to forecast such time series because the prediction problem is generally nonlinear and no analytic solution is therefore available. According to a limited simulation experiment, the use of a correct noncausal model can lead to substantial gains in forecast accuracy over the corresponding causal model. An empirical application to U.S. inflation demonstrates the importance of allowing for noncausality in improving point and density forecasts.Noncausal autoregression; density forecast; inflation

Autoregression-Based Estimation of the New Keynesian Phillips Curve

We propose an estimation method of the new Keynesian Phillips curve (NKPC) based on a univariate noncausal autoregressive model for the inflation rate. By construction, our approach avoids a number of problems related to the GMM estimation of the NKPC. We estimate the hybrid NKPC with quarterly U.S. data (1955:1-2010:3), and both expected future inflation and lagged inflation are found important in determining the inflation rate, with the former clearly dominating. Moreover, inflation persistence turns out to be intrinsic rather than inherited from a persistent driving process

Robustness of the Risk-Return Relationship in the U.S. Stock Market

In this paper, we study the risk-return relationship in monthly U.S. stock returns (1928:1— 2004:12) using GARCH-in-Mean models. In particular, we consider the robustness of the relationship with respect to the omission of the intercept term in the equation for the expected excess return recently recommended by Lanne and Saikkonen (2006). The existence of the relationship is quite robust, but its estimated strength is dependent on the prior belief concerning the intercept. This is the case in particular in the first half of the sample period, where also the coefficient of the relative risk aversion is found to be smaller and the equity premium greater than in the latter half

Has U.S. Inflation Really Become Harder to Forecast?

Peer reviewe

Statistical Identification of Economic Shocks by Signs in Structural Vector Autoregression

The authors propose a new frequentist approach to sign restrictions in structural vector autoregressive models. By making efficient use of non-Gaussianity in the data, point identification is achieved which facilitates standard asymptotic inference and, hence, the assessment of theoretically implied signs and labelling of the statistically identified structural shocks. The authors illustrate the benefits of their approach in an empirical application to the US labour market.Peer reviewe

Has U.S. Inflation Really Become Harder to Forecast

Recently Stock and Watson (2007) showed that since the mid-1980s it has been hard for backward-looking Phillips curve models to improve on simple univariate models in forecasting U.S. inflation. While this indeed is the case when the benchmark is a causal autoregression, little change in forecast accuracy is detected when a noncausal autoregression is taken as the benchmark. In this note, we argue that a noncausal autoregression indeed provides a better characterization of U.S. inflation dynamics than the conventional causal autoregression and it is, therefore, the appropriate univariate benchmark model