Macroeconomic model is an analytical tool designed to describe the operation of the economy of a country or a region. These models are usually designed to examine the dynamics of aggregate quantities such as the total amount of goods and services produced, total income earned, the level of employment of productive resources, and the level of prices
Econometric theory comprises a body of tools and techniques for analyzing the properties of prospective methods under hypothetical states of nature. In the forecasting context, the methods are forecasting models and procedures, and the states of nature relate to the properties of the variables to be forecast. For an econometric theory of forecasting to be useful in terms of delivering relevant conclusions about empirical forecasting, those states must adequately capture the appropriate aspects of the real world to be forecast.
Many countries' planning procedures are rather fragmented, with responsibilities for economic development, environmental standards etc. distributes among several more or less independent ministries and agencies. These procedures might be improved if they are based on a common understanding of the functioning of the economy. Macroeconomic models can be used to ensure consistency among the various planning activities with regard to behavioural assumptions of economic agents, as well as expectations about future development of key economic variables.
Academic work in macroeconomics tends to focus on specific issues, such as the consumption function or a new theoretical insight. Large macroeconomic policy models, on the other hand, are used to quantify the impact of a range of issues within a unified structure, most notably counter-cyclical macroeconomic policies.
If it is short-term forecasting, then go ahead with a purely statistical approach on macro data. If you want policy advice, you need something that withstands the Lucas Critique, and strong micro-foundations is then the way to go.
...our most successful use of [macroeconomic] models has been in cleaning up after shocks rather than predicting, preventing, or insulating against them through pre-crisis preparation. When despite our best effort to prevent it or to minimize its impact a priori, we get a recession anyway, we can use our models as a guide to monetary, fiscal, and other policies that help to reduce the consequences of the shock
Macroeconomic forecasting is, or ought to be, in a state of confusion. The dominant modeling traditions among academic economists, namely dynamic stochastic general equilibrium (DSGE) and vector autoregression (VAR) models, both spectacularly failed to forecast the financial collapse and recession which began in 2007, or even to make sense of its course after the fact. [...] Whether existing approaches can be rectified, or whether basically new sorts of models are needed, is a very important question for macroeconomics, and, because of the privileged role of economists in policy making, for the public at large.
The modern textbooks still teach these [macroeconomic] models but the exposition has evolved although remains deeply flawed. It seems that this conceptual framework is still used to make public comments along the lines that the US government is facing insolvency and that the euro remains the best monetary organisation for Europe. Those conclusions are as flawed as the model that spawns them. Flawed macroeconomic models lead to erroneous conclusions.
it is recognised that the forecasts produced by macroeconomic modellers are not helpful in making decisions when they are presented as point forecasts (even where they are accompanied by confidence intervals to indicate the degree of uncertainty surrounding the forecasts). Decision-makers are interested in the likely outcome of their decisions which means that they require forecasts of the entire range of possible outcomes (in the form of density functions or, where many variables are involved, joint density functions) or forecasts of the likelihood of specified events occurring. Tools for the production and interpretation of density forecasts and event probability forecasts are therefore important.
Macroeconometric models, on many ways the Flagships of the economist profession in the 1960s, came under increasing attack from both theoretical economist and practitioners in the late 1970s. […] As a result, by the start of the 1990s, the status of macroeconometric models had declined markedly […] Nevertheless, […] macroeconometric models never completely disappeared from the scene. […] Thus, the discipline of macroeconometric modelling has been able to adapt to changing demands, both with regards to what kind of problems that users expect that models can help them answer, and with regard to quality and reliability.
According to Lucas (1980) the objective of macroeconomic model building is "to provide fully articulated, artificial economic systems that can serve as laboratories in which policies that would be prohibitively expensive to experiment with in actual economies can be tested out at much lower cost."