Optimal military spending in the US: A time series analysis

This paper extends previous work on the optimal size of government spending by including nested functional decompositions of military spending into consumption and investment. Post World War II US data are then used to estimate nested non-linear growth models using semi-parametric methods. As expected, investments in military and non-military expenditure are both found to be productive expenditures with respect to the private production. Moreover there is little evidence to suggest that current military spending is having a negative impact on economic growth in the US, while civilian consumption only tends to have only a weak impact. This does not imply that society will necessarily benefit from a reallocation of more spending to the military sector, nor that it is the best way to achieve economic growth. © 2010 Elsevier B.V

Evaluation of bottom-up and top-down strategies for aggregated forecasts: state space models and arima applications

Abstract. In this research, we consider monthly series from the M4 competition to study the relative performance of top-down and bottom-up strategies by means of implementing forecast automation of state space and ARIMA models. For the bottomup strategy, the forecast for each series is developed individually and then these are combined to produce a cumulative forecast of the aggregated series. For the top-down strategy, the series or components values are first combined and then a single forecast is determined for the aggregated series. Based on our implementation, state space models showed a higher forecast performance when a top-down strategy is applied. ARIMA models had a higher forecast performance for the bottom-up strategy. For state space models the top-down strategy reduced the overall error significantly. ARIMA models showed to be more accurate when forecasts are first determined individually. As part of the development we also proposed an approach to improve the forecasting procedure of aggregation strategies

Lutkepohl

Data from Introduction to Multiple Time Series Analysis, Springer-Verlag, 1993. Quarterly data on West German economy, 1960q1-1982q4.

Asymptotic Distributions of Impulse Response Functions and Forecast Error Variance Decompositions of Vector Autoregressive Models.

In recent years, vector autoregressive models have become standard tools for economic analyses. Impulse response functions and forecast error variance decompositions are usually computed from these models in order to investigate the interrelationships within the system. However, sometimes no measures of estimation uncertainty are provided by authors. One reason may be that the relevant asymptotic distribution theory is distributed over various publications. In this article, the available results are summarized and the missing links are provided in order to facilitate the computation of standard errors and test statistics. Copyright 1990 by MIT Press.