Download
slide1 n.
Skip this Video
Loading SlideShow in 5 Seconds..
Financial Deregulation and Structural Changes in Australia s Monetary Aggregate and Interest Rates PowerPoint Presentation
Download Presentation
Financial Deregulation and Structural Changes in Australia s Monetary Aggregate and Interest Rates

Financial Deregulation and Structural Changes in Australia s Monetary Aggregate and Interest Rates

203 Views Download Presentation
Download Presentation

Financial Deregulation and Structural Changes in Australia s Monetary Aggregate and Interest Rates

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

    1. 1 Financial Deregulation and Structural Changes in Australias Monetary Aggregate and Interest Rates Mosayeb Pahlavani Faculty of Economics, University of Sistan and Baluchestan, Zahedan, Iran Abbas Valadkhani School of Economics and Information Systems University of Wollongong, Australia

    2. 2

    3. 3

    4. 4

    5. 5 Zivot and Andrews(1992) and also Perron & Vogelsang(1992) and Perron (1997) allowed that the date of the change in the series to be unknownn. They have shown that bias in the usual unit root tests can be reduced. by endogenously determining the time of structural breaks.

    6. 6 Innovational and Additive Outlier Models Vogelsang & Perron(1992) and Perron (1997) introduced 2 methods for determining the time of the break endogenously: The Additive Outlier (AO) model that captures sudden change and the Innovational Outlier (IO) model that permits changes to occur gradually over time. The null hypothesis of unit root is rejected if the t-statistic is sufficiently large (in absolute value). In these model the break time is unknown and therefore it is determined through minimizing the t-statistic for testing a =1. A data dependent method is employing to choose the optimal number of lags (k) from general to specific.

    7. 7 Innovational outlier model (IO) Perron(1997)

    8. 8 Zivot-Andrews (1992) Model to Test for a Unit Root In this method, like the innovational outlier model, we run the regression for every possible break date sequentially from the quarter after the starting date until the quarter before the end date. Or trimming=0.05 to 0.15 percent. DUt is a dummy variable capturing a shift in intercept and is equal to 1 if t>Tb and zero otherwise. DTt is another dummy representing a break in the trend occurring at time Tb. DTt is equal to (t-Tb) if (t>tb) and zero otherwise.

    9. 9 Empirical result Prior to estimating these models we have to check the trend property of the variables under investigation. If there is no upward or downward trend in the data, the test power to reject the no-break null hypothesis is reduced as the critical values increase with the inclusion of a trend variable. On the other hand, if the series under investigation truly exhibits a trend, then estimating any of these models with no trend may fail to capture some important characteristics of the data (Ben David and Papell, 1997).

    10. 10

    11. 11

    12. 12 Empirical Result The empirical results based on these models do not provide much evidence against the null hypotheses of unit roots in these series. In other words, despite considering structural breaks in all series, almost all monetary aggregates and financial variables examined are found to be I(1). This is consistent with the results obtained by conventional ADF testing. The structural breaks found coincide with important policy changes during the period of financial deregulation starting in the 1980s. Consider the example of the IO model (Table 2) and the M1 monetary measure. As indicated, the most major structural break in this series (indicating a significant change in both the intercept and the slope) over the period 1975-2003 occurred in 1982q4. This particular break may be attributed to the gradual effects of several policy changes during this time, including: (i) the relaxation of the maturity restrictions on certificate of deposits; (ii) removal of some restrictions on Australian overseas investments; (iii) removal of quantitative controls on bank lending; and (iv) introduction of the new Treasury bonds tender system. In addition, a sudden change in the slope of M1 as derived from the AO model occurred in 1986q2. One argument is that this particular structural break corresponds with several policy changes in 1986 including: (i) the removal of ceiling rates on new home loans: (ii) the abolition of statutory reserve deposits; and (c) regulatory permission for non-bank financial institutions to issue payment orders (Juttner and Hawtrey, 1997).

    13. 13

    14. 14

    15. 15 . Figure 1 shows the plots of the estimated within the trimming region using the ZA procedure. The lowest value for t-statistic in each graph determines TB1. empirical results indicate that most variables under investigation have been subject to significant structural break in 1990-92 when a sever recession hits the Australian economy and Despite some minor differences the results are robust as both the IO and ZA Models indicate that in the 1990-1992 period most of the variables have witnessed significant structural changes. As can be seen from the results, (a) the estimated coefficients for and ? are all statistically significant, supporting the view that at least one structural shift in the intercept has occurred during the sample period for all 6 variables; (b) the trend variable is significant in 5 out of 6 cases; indicating the series exhibit an upward or downward trend; (c) the estimated coefficients for ? are statistically significant for all variables (with the only two exceptions being Ln(CPI) and Ln(MB), implying that at least one significant structural shift in the trend has occurred in the four of the variables under investigation.

    16. 16 Further research The results of this paper can be useful for analysts who will undertake any future research which involves the use of time series data on the Australian macroeconomy. However it should be noted that Zivot and Andrews (1992) and Peron (1997) tests capture only one (the most significant) structural break in each variable. What if, there have been multiple structural breaks in a series? Considering only one endogenous break may not be sufficient and it could lead to a loss of information particularly when in reality there is more than one break (LP, 1997). On this same issue, Ben-David et al (2003) argued that: just as failure to allow one break can cause non-rejection of the unit root null by the Augmented Dickey Fuller test, failure to allow for two breaks, if they exist, can cause non-rejection of the unit root null by the tests which only incorporate one break (2003: 304). LP introduced a new procedure to capture two structural breaks. They argued that unit root test that account for two structural breaks (if significant) is more powerful than those, which only allows for one single break. New Procedure by Bai and Perron(1998;2003) introduced for Multiple structural break

    17. 17

    18. 18

    19. 19 Empirical Result based on the LP(1997) procedure It is interesting to recognise that the estimated TB1s for most variables in the LP model are quite close to their corresponding ZA or IO and AO counterparts. The majority of the endogenously determined break dates coincide with the following events: (a) the 1973 oil shock; (b) the peak of financial reforms during the period 1987-1988; (c) the profound effects of the very deep and prolonged 1990-1991 recessions on the Australian economy and a consequent property market collapse; and (d) the launch of the 1996 Wallis Inquiry into financial system. While the empirical results based on the ZA and/or IO and AO models does not provide enough evidence against the null hypotheses of unit roots in all series. However, by applying the LP test, we found mixed results concerning the stationarity or otherwise of the Australian monetary and interest rates data. Indeed, four out of the six variables under investigation became stationary.

    20. 20 Table 2 indicate that by applying LP(1997) procedure the null hypothesis is rejected for four out of six variables at 5 per cent significance level. These variables are Ln(RS), Ln(M3), Ln(BM), and Ln(MB). The consumer price index and the long-term interest rate remain non-stationary despite the fact that we have captured two structural break in the data. According to the results presented in Table 2, As an example , the consumer price index (CPI) has been subject to two significant structural breaks in 1973q2 and 1987q4. One may attribute these two breaks to the 1973 oil shock and the 1987 stock market crash. A cursory inspection of the first graph presented in Figure 2 clearly shows that the intercept and slope for the CPI visibly changed in TB1=1973q2 and TB2=1987q4 according to the LP test and in 1973q2 according to the ZA test.

    21. 21

    22. 22

    23. 23 Thank you