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...

We propose a new generalized forecast error variance decomposition with the property that the proportions of the impact accounted for by innovations in each variable sum to unity. Our decomposition is based on the well-established concept of the generalized impulse response function. The use of...

We propose a new simple model incorporating the implication of the quantity theory of money that money growth and inflation should move one for one in the long run, and, hence, inflation should be predictable by money growth. The model fits postwar U.S. data well, and beats common univariate...

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. For a...

We develop a similarity-based structural vector autoregressive (SVAR) model using the similar clusters of data relevant for the prevailing initial macroeconomic conditions of interest. Our computationally attractive simple approach enables us to uncover time-varying effects of structural...