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The theoretical issues involved in establishing links between the different notions of equilibrium in economics and the long-run are addressed. Formulating long-run relations as the steady state solutions of intertemporal optimization problems from economic theory is advocated. For empirical purposes the steady state solutions can then be embedded within a suitable multivariate dynamic model, such as the vector autoregressive models. The issues of estimation and inference are addressed, and it is argued that the abandonment of traditional methods in favor of the new unit-root-cointegration procedures might have been premature. The importance of a priority restriction is emphasized, obtainable from the steady state solution of suitable intertemporal optimization problems, in structural identification of long-run or cointegrating relations. The issue of aggregation and its implications for estimation theory-consistent long-run relations are raised, and alternative methods for dealing with dynamic heterogeneities across groups in estimation of long-run relations, using panel data, are discussed.

A casual inspection of many economic time series shows that they have trends. It is equally apparent that, unless the time period is fairly short, these trends cannot be adequately captured by straight lines. In other words, a deterministic linear time trends is too restrictive. Despite this, a good deal of applied economic work starts off by detrending the data by regressing on time, thereby rendering all that follows invalid. Separating out the trend from the cycle is motivated by the idea that the economic theory which is relevant to the long run is different to the theory one wishes to apply in the short run. Irrespective of whether or not one accepts this view, an arbitrary separation into trend and cycle is clearly not to be recommended. The ideal way to proceed is by construction a multivariate model using original data. If this proves too difficult, one should at least began to separating out the long run in a way which pays attention to the properties of series.

The process that generate data which attempt to summarize the major features of a macroeconomy is called the data generating process (DGP). It is assumed that the objective of theoretical macroeconomics and of macroeconometrics is to determine the DGP, or at least segments of it. It can be argued that even though the quantity of data produced by a macroeconomy is quite large, it is still quite insufficient to capture all of the complexities of the DGP. The modeling object has to be limited to providing an adequate or satisfactory approximation of the true DGP. Partial attention is paid to time series techniques rather that to cross-section or panel data. A time series analysis contains 3 major components: pre-analysis of the data to help with model specification, model estimation and possible a respecification, and finally the model evaluation.