Testing for the Constancy of Parameters over Time

Nyblom, Jukka

doi:10.1080/01621459.1989.10478759

Cited by 666 publications

(311 citation statements)

References 10 publications

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“…Second, if the long-run parameters prove stable, then we test for the stability of the short-run parameters. To test for parameter stability, we apply the c L tests of Nyblom (1989) and Hansen (1992), which tests the null hypothesis of constant parameters against the alternative hypothesis that the parameters follow a random-walk process, first proposed by Gardner (1969). The c L test can also serve as a test of cointegration, when series are I(1).…”

Section: Resultsmentioning

confidence: 99%

Temporal causality between house prices and output in the US: A bootstrap rolling-window approach

Nyakabawo

Miller

Balcılar

et al. 2015

The North American Journal of Economics and Finance

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This paper examines the causal relationships between the real house price index and real GDP per capita in the U.S., using the bootstrap Granger (temporal) non-causality test and a fixed-size rolling-window estimation approach. We use quarterly time-series data on the real house price index and real GDP per capita, covering the period 1963:Q1 to 2012:Q2. The full-sample bootstrap non-Granger causality test result suggests the existence of a unidirectional causality running from the real house price index to real GDP per capita. A wide variety of tests of parameter constancy used to examine the stability of the estimated vector autoregressive (VAR) models indicate short-and long-run instability. This suggests that we cannot rely on the full-sample causality tests and, hence, this warrants a time-varying (bootstrap) rolling-window approach to examine the causal relationship between these two variables. Using a rolling window size of 28 quarters, we find that while causality from the real house price to real GDP per capita occurs frequently, significant, but less frequent, evidence of real GDP per capita causing the real house price also occurs. These results imply that while the real house price leads real GDP per capita, in general (both during expansions and recessions), significant feedbacks also exist from real GDP per capita to the real house price.

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Section: Resultsmentioning

confidence: 99%

Temporal causality between house prices and output in the US: A bootstrap rolling-window approach

Nyakabawo

Miller

Balcılar

et al. 2015

The North American Journal of Economics and Finance

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“…We therefore conclude that there is no long run relationship between GDP and any of the housing variables studied. We use the Lc of Nyblom (1989) and Hansen (1992) Given that no cointegration is found, the next step is to determine the full sample Granger causality using bivariate VARs (comprising of the growth rates of the real GDP and the specific housing variable) rather than VECM. The results are presented in the first panel of The results indicate that in each of the equations, parameter constancy is rejected at 1 percent and hence, there are no stable long-run parameters.…”

Section: Resultsmentioning

confidence: 99%

“…To examine the stability of the cointegration parameters, we use the L c tests of Nyblom (1989) and Hansen (1992). The Nyblom-Hansen L c test is an LM test for parameter constancy against the alternative hypothesis that the parameters follow a random walk process and, therefore, time-varying, since the first two moments of a random walk are time dependent .…”

Section: Econometric Modelmentioning

confidence: 99%

Housing and the business cycle in South Africa

Aye

Balcılar

Bosch

et al. 2014

Journal of Policy Modeling

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This paper examines the housing-output growth nexus in South Africa by accounting for the time variation in the causal link with a bootstrapped rolling Granger non-causality test. We use quarterly data on real gross domestic product, real house prices, real gross fixed capital formation and number of building plans passed. Our data span 1971Q2-2012Q2. Using full sample bootstrap Granger causality tests, we find a uni-directional causality from output to number of building plans passed; a uni-directional causality from real house price to output and a bi-directional causal link between residential investment and output. However, using parameter stability tests, we show that estimated VARs are unstable, thus full-sample Granger causality inference may be invalid. Hence, we use a bootstrap rolling window estimation to evaluate Granger causality between the housing variables and the growth rate.In general, we find that the causality from housing to output and, vice versa, differ across different sample periods due to structural changes. Specifically speaking, house price is found to have the strongest causal relationship with output compared to residential investment and number of building plans passed, with real house price showing predictive ability in all but one downward phase of the business cycle during this period.

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“…We apply Lc test (Nyblom, 1989;Hansen, 1992) to test for all parameters in the overall VAR system. However, the underlying variables are cointegrated in levels; the VAR model in the first difference is misleading unless it is corrected with error correction.…”

Section: Parameter Stability Testmentioning

confidence: 99%

“…The Sup-F test is used to detect that whether the regime shift occurred or not, whereas the Exp-F, Mean-F, investigates the model stability over time (Balcilar et al, 2010). The Lc test of Nyblom (1989) and Hansen (1992) is also used to test for all parameters in the overall VAR system.…”

Section: Datamentioning

confidence: 99%

Causal Relationship between Monetary Policy and the Stock Market: a Bootstrap Rolling Window Approach

Khan¹,

Qingyang²,

Khurshid³

2017

FMIR

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This study examines the causal relationship between the monetary policy and the stock price in Pakistan. We use bootstrap Granger full-sample causality test and sub-sample rolling window estimation test for the period of 1992:01 to 2015:06. The empirical result shows that there is a unidirectional causality from monetary policy to the stock price. However, considering the structural changes, we assess the parameter stability by using the full sample for short run relationship and find the short run instability for the full sample. This recommends that full-sample data cannot depend upon for the precise result. To address the structural changes issue, we propose rolling window approach is used to revisit the causal relationship between the two series and the result reports the bi-directional causal relationship between the monetary policy and the stock market. The relationship between the stock market and the money supply is not consistent, and the upward trend may cause serious policy implication on the stock market for future investment and profit.

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Testing for the Constancy of Parameters over Time

Cited by 666 publications

References 10 publications

Temporal causality between house prices and output in the US: A bootstrap rolling-window approach

Temporal causality between house prices and output in the US: A bootstrap rolling-window approach

Housing and the business cycle in South Africa

Causal Relationship between Monetary Policy and the Stock Market: a Bootstrap Rolling Window Approach

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