An important goal of macroeconomic policy is the stabilization of business cycles. For the conduct of policy, good predictions and identification of the business cycle are necessary. The industrial confidence indicator (ICI), obtained from the Business Tendency Survey (BTS) conducted by Statistics Norway among business leaders in the manufacturing sector, may be useful in this respect. This thesis aims to investigate the leading properties of the ICI with regard to economic activity in the manufacturing sector and the economy as a whole (the mainland economy). Specifically, I will seek to formulate a dynamic empirical model of the business cycle, with lags of the indicator as explanatory variables.
The BTS contains information which may be analyzed in a variety of ways. When attempting to use data from this survey for the purpose of modeling and forecasting quantitative economic phenomena such as GDP growth, several issues should be taken into consideration. First, when answering the survey, respondents choose between a few alternative responses such as “better”, “worse” or “no change” without indicating the magnitude of the change. That is, results obtained from the survey are mainly qualitative in nature, while the phenomena we wish to explain are mainly quantitative. Second, indicators extracted from this survey, like the ICI, represent an aggregation of answers across firms which may not be optimal: information relevant to modeling may be lost.
After a presentation of these concerns, the matter of model specification is discussed. As economic theory fails to give an unambiguous answer as to the preferred model, a general-to-specific modeling approach is used to arrive at the final model specifications. The general-to-specific procedure is carried out using an automated model selection feature of the module PCGive in OxMetrics, Autometrics.
The general-to-specific approach leads to four final model specifications for the output gap and quarter-on-quarter growth in the manufacturing sector and the mainland economy. The ICI appears to be leading movements in output by two quarters. As the results are fairly similar regardless of how we measure economic activity, the analysis will focus on the models of the output gap. The long-run properties of the models are considered, and no obvious inconsistencies are found. Using these models and the latest available figures of GDP, one can make short-term forecasts of the output gap. Such predictions are of particular interest in the context of the financial crisis which has also impacted the Norwegian economy. The model predicts that the output gap is largest in absolute value (that is, farthest below trend) at 2009:2. For the manufacturing sector, output is predicted to return at trend level by 2009:4, while recovery is expected to be somewhat slower in the mainland economy
