Lanne, Markku and Nyberg, Henri and Saarinen, Erkka (2011): Forecasting U.S. Macroeconomic and Financial Time Series with Noncausal and Causal AR Models: A Comparison.
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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.
|Item Type:||MPRA Paper|
|Original Title:||Forecasting U.S. Macroeconomic and Financial Time Series with Noncausal and Causal AR Models: A Comparison|
|Keywords:||Noncausal autoregression; forecast comparison; macroeconomic variables; financial variables|
|Subjects:||C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods; Simulation Methods
C - Mathematical and Quantitative Methods > C2 - Single Equation Models; Single Variables > C22 - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications
E - Macroeconomics and Monetary Economics > E4 - Money and Interest Rates > E47 - Forecasting and Simulation: Models and Applications
|Depositing User:||Henri Nyberg|
|Date Deposited:||20. Apr 2011 20:41|
|Last Modified:||12. Feb 2013 20:05|
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