Recent developments in the construction, simulation and estimation of dynamic stochastic general equilibrium (DSGE) models have made it possible to combine a rigorous microeconomic derivation of the behavioural equations of macro models with an empirically plausible calibration or estimation which fits the main features of the macroeconomic time series.
The main difference between empirical DSGE models and the more traditional macroeconometric models (such as the AWM) is that both the parameters and the shocks to the structural equations are related to deeper structural parameters describing household preferences and technological and institutional constraints.
These micro foundations have three advantages:
They provide a theoretical discipline on the structure of the model that is being estimated, which may be particularly helpful in those cases where the data themselves are not very informative, for example regarding the long-run behaviour of the economy or because there has been a regime change.
Being able to relate the reduced-form parameters to deeper structural parameters makes the use of the model for policy analysis more appropriate, i.e. less subject to the Lucas critique, as those structural parameters are less likely to change in response to changes in policy regime.
Micro-founded models may provide a more suitable framework for analysing the optimality of various policy strategies as the utility of the agents in the economy can be taken as a measure of welfare.
For these reasons, staff at the ECB and the Eurosystem have started to develop empirical DSGE models for monetary policy analysis. The Smets-Wouters (2003) Model is an example of such a medium-sized DSGE model, which has been estimated on the basis of quarterly euro area macro data. The model features three types of economic agents: households, firms and the central bank. Households decide how much to consume, how much to invest and how much to work and at what wage. Firms employ workers and capital and decide how much to produce and at what price to sell their products.
In addition to a number of real frictions such as habit formation in consumption and adjustment costs in investment, the model features nominal price and wage rigidities. The model is estimated using seven euro area macroeconomic series (real GDP, consumption, investment, employment, real wages, inflation and the nominal short-term interest rate). Using Bayesian estimation and validation techniques, it is shown that the estimated model is able to compete with more standard, unrestricted time series models, such as vector auto regressions (VARs), in out-of-sample forecasting.
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