Do building permits act as a leading indicator of Italy short-term production in construction?


Abstract


Index of production in construction and building permits are two indica-
tors used to describe the short-term evolution of the construction sector. In
particular, the former measures the level of activity in terms of the sector
output, whereas the latter are meant to anticipate production in construction
in the very near future, as they represent the administrative applications to
start building activity. Nevertheless, for a number of reasons to be detected,
building permits do not always act as a leading indicator of the construction
sector short-term performance. To investigate whether there are any leading-
lagging relations between these two variables, a descriptive analysis based on
cross-correlations has been preliminarily carried out and then supplemented
by the application of a VAR (Vector Autoregressive) model, used to analyse
Granger causality within a cointegrated system of the two variables.

DOI Code: 10.1285/i20705948v12n2p416

Keywords: Building permits; Construction production; Granger causality; Cointegration.

References


Baxter, M. and King, R. G. (1999). Measuring business cycles: Approximate band-pass filters for economic time series. The Review of Economics and Statistics, 81(4):575– 593.

Canova, F. (1998). Detrending and business cycle facts. Journal of Monetary Economics, 41(3):475–512.

Canova, F. (1999). Does detrending matter for the determination of the reference cycle and the selection of turning points? Economic Journal, 109(452):126–50.

Engle, R. and Granger, C. (1987). Cointegration and error correction: representation, estimation and testing. Econometrica, 55:251–76.

Granger, C. (1969). Investigating causal relations by econometric models and cross- spectral methods. Econometrica, 37:424–38.

Hamilton, J. (2017). Why you should never use the Hodrick-Prescott filter. NBER Working Papers 23429, National Bureau of Economic Research, Inc.

Harvey, A. C. and Jaeger, A. (1993). Detrending, stylised facts and the business cycle. Journal of Applied Econometrics, 8(3):231–47.

Hendry, D. (1997). The econometrics of macro-economic forecasting. Economic Journal, 107:1330–57.

Hodrick, R. J. and Prescott, E. (1997). Postwar U.S. business cycles: an empirical investigation. Journal of Money, Credit and Banking, 29:1–16.

Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12:231–54.

Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in gaus- sian vector autoregressive models. Econometrica, 59(6):1551–1580.

Kwiatkowski, D., Phillips, P., Schmidt, P., and Shin, Y. (1992). Testing the null hy- pothesis of stationarity against the alternative of a unit root. how sure are we that economic time series have unit root? Journal of Econometrics, 54:159–178.

Lütkepohl, H. and Reimers, H.-E. (1992). Granger-causality in cointegrated VAR pro- cesses: The case of the term structure. Economics Letters, 40:263–268.

MacKinnon, J. G. (1996). Numerical distribution functions for unit root and cointegra- tion tests. Journal of Applied Econometrics, 11(6):601–618.

Phillips, P. and Hansen, B. (1990). Statistical inference in instrumental variables regres- sion with I(1) processes. Review of Economic Studies, 57:99–125.

Qiao, Z., McAleer, M., and Wong, W.-K. (2009). Linear and nonlinear causality between changes in consumption and consumer attitudes. Economics Letters, 102(3):161–164.

Stock, J. and Watson, M. (1993). A simple estimator of cointegrating vectors in higher order integrated systems. Econometrica, 61:783–820.

Toda, H. Y. and Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2):225 – 250.

Vogelsang, T. and Wagner, M. (2014). Integrated modified OLS estimation and fixed-b inference for cointegrating regressions. Journal of Econometrics, 148:741–760.


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