Forecasting Macedonian Business Cycle Turning Points Using Qual Var Model

Open access

Abstract

This paper aims at assessing the usefulness of leading indicators in business cycle research and forecast. Initially we test the predictive power of the economic sentiment indicator (ESI) within a static probit model as a leading indicator, commonly perceived to be able to provide a reliable summary of the current economic conditions. We further proceed analyzing how well an extended set of indicators performs in forecasting turning points of the Macedonian business cycle by employing the Qual VAR approach of Dueker (2005). In continuation, we evaluate the quality of the selected indicators in pseudo-out-of-sample context. The results show that the use of survey-based indicators as a complement to macroeconomic data work satisfactory well in capturing the business cycle developments in Macedonia.

1. Chauvet, M. and Hamilton, J. D. (2005). Dating Business Cycle Turning Points, NBER Working Paper No. 11422.

2. Chen, L. (2014). Forecasting Canadian Recession using Qual VAR Model, Master thesis, University of Ottawa, Department of Economics, ECO6999, August 2014.

3. Chow, G.C. and Lin, A. (1971). Best linear unbiased interpolation, distribution and extrapolation of time series by related series. The Review of Economics and Statistics, 53, 372-375.

4. Davidson, R. and MacKinnon, J.G. (1993). Estimation and Inference in Econometrics, New York: Oxford University Press.

5. Dueker, J. M. (2005). Dynamic Forecasts of Qualitative Variables: A Qual VAR Model of U.S. Recession, Journal of Business & Economic Statistics, 23, 96-104.

6. Estrella, A. and Mishkin F.S. (1998). Predicting U.S. Recessions: Financial Variables as Leading Indicators, The Review of Economics and Statistics, 80, 45-61.

7. European Commission (2014). The joint harmonized EU programme of the business and consumer surveys, User guide. Brussels: European Commission.

8. Kauppi, H. and Saikkonen, P. (2008). Predicting US recessions with dynamic binary response models, The Review of Economics and Statistics, 90, 777-791.

9. Meinusch, A. and Tillmann, P.(2014). The Macroeconomic Impact of Unconventional Monetary Policy Shocks, MAGKS Papers on Economics 201426. Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics.

10. Moon, H. and Lee, J. (2012). Forecast Evaluation of Economic Sentiment Indicator for the Korean Economy. Basel, IFC Conference, BIS.

11. Ng, E. (2012). Forecasting US recessions with various risk factors and dynamic probit models, Journal of Macroeconomics, 34, 112-125.

12. Nyberg, H. (2010). Dynamic probit models and financial variables in recession forecasting, Journal of Forecasting, 29, 215-230.

Journal Information


CiteScore 2017: 0.43

SCImago Journal Rank (SJR) 2017: 0.284
Source Normalized Impact per Paper (SNIP) 2017: 0.910

Metrics

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 225 199 11
PDF Downloads 92 86 9