Empirical study of causality between Real GDP and Monetary variables. Presented by : Hanane Ayad

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Empirical study of causality between Real GDP and
Monetary variables.Presented by
Hanane Ayad
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overview

• Since the early 60s , the literature concerning
  the interaction between monetary variables and
  real income, especially in U.S, have taken an new
  direction.
• Many of those models have been constructed to
  take into consideration some sophisticated
  channels through which money can influence Real
  Income.
• However, the empirical results gathered so far
  still do not lead to a clear path as to whether
  monetary variables do have a direct or whatever
  kind of effect on the real GDP.

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Chronology of literature

• The earliest study dealing with the
  
  causal relationship between money and GDP was
  performed by Freidman and Schwartz (1963).
• Nine Years later, Sims(1972) came to argue that
  it is quite inappropriate to distinguish between
  cause and effect based only on correlation
  pattern.
• Eight years later (1980),Sims suggested a vector
  auto regression process that takes into
  consideration the eventual effect of additional
  controlling macroeconomic variables.

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Chronology of literature

• The causality between money and GDP that Sims
  found in (1972) disappered as soon as he included
  a short term interest rate as a controlling
  variable.
• Others later found a strong evidence of causality
  when they use log level data and no significant
  effect of money on income when they adopt first
  difference data
• Stock and Watson (1989) adopted a first
  differenced log data, they found that M1 growth
  affects GDP growth when they included a Trend
  function, this monetary effect vanished once they
  increased the sample size, and added interest
  rate as an explanatory variable
• Dufour and Tessier (1997)came up with a more
  ingenious and logically coherent specification
  VARMA-Echelon.

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Objectives of the empirical study

• Establish a causal link between monetary
  variables(m1 interest rate) and real GDP .
• Investigate the nature of the dynamic of this
  relationship.
• Detect a long run relationship and the speed of
  convergence into equilibrium (if there any)

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methodology

• The most commonly used test for causality is the
  standard granger causality test.
• The best method that can be used to test the
  causality of cointegrated variables is the ECM
  procedure.
• The main advantage of this methodology it does
  not only detect the causality effect, but also
  gives an idea about the long run relationship
  between the variables.
• If the variables are not cointegrated, the ECM is
  inappropriate, the best alternative in this case
  is the use of a VAR in difference process.

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• In this paper, I intended to test the causality
  between between some Monetary variables m1,
  interest rate, CPI, and the real GDP using the
  ECM procedure.
• Before using the ECM, we need to make sure that
  all the four time series have the same unit
  roots.
• If it is the case, we then test for cointegration
  among the four variables, if they are
  cointegrated, we then can proceed to the second
  step of ECM, and run the long run regression
  D(RGDP)ab1D(RGDPt-1)b2D(M1t-1b3D(INTRTt-1)b4D
  (CPIt-1)b5Ut-1et
• In the case of no cointegration, we, use the VAR
  differenced, and test for the significance of the
  lagged independent variables.
• If the variables do no have the same unit roots,
  we just use the standard Granger causality test

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Econometrics analysis

• Simulations reveals that the four variables are
  all I(1)for example of the real M1
• ADF (M1 LEVEL)
• ADF Test Statistic-0.837416 1 Critical
  Value-3.5889
• 
  5 Critical Value-2.9303
• 
  10 Critical Value-2.6030
• ADF M1 (FIRST DIFFERENCE)
• ADF Test Statistic-3.950100 1 Critical
  Value-3.5930
• 
  5 Critical Value-2.9320
• 
  10 Critical Value-2.6039

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Econometrics analysis

• ADF RES2 (LEVEL)
• ADF Test Statistic-2.287506 1 Critical
  Value-3.6019
• 
  5 Critical Value-2.9358
• 
  10 Critical Value-2.6059
• The residuals have a unit root of more than o,
  hence, the variables are not cointegrated, in
  this case, the use of ECM procedure is
  inappropriate.
• The best method than can be adopted in the
  absence of cointegration is the VAR in difference

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cointegration

• We say that Xt and Yt are cointegrated if there
  is a long run relationship between Xt and Yt.
• If we have two non-stationary time series that
  have the same unit roots, let say Xt
  T(t) e(x)t, and
• Yt z(t) e(y)t ,
  where T(t) , Z(t) are trend terms,
• 
  e(x)t and e(y)t are white noise.
• If these two series can be written as a linear
  combination so that the trend terms cancels out,
  then we can say that Xt and Yt are cointegrated.
• This can be done through testing the unit root
  for the residuals resulting from regressing Yt on
  Xt (Xt and Yt ) has the same unit root. The
  residual should have unit root less than that of
  the variable.
• Less say Xt ?I(b) and Yt ? I(b)
• If Ut ? I(d) where d is less than b. Them we can
  say that Xt and Yt are cointegrated.

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Unit Root

• Yt a bt Ut where Ut is the white noise and
  t is the trend factor.
• Yt-1 a b(t-1) Ut-1
• ?Y a Ut - Ut-1.
• If we manage to cancel out the trend factor by
  differentiating only one time this means that Yt
  has one unit root.
• The DF test is done as follows
• H0 THE VARIABLES HAS 1 UNIT ROOT Ha b lt 0.
• ADF test done the same way, but we just add other
  lag difference terms of the dependent variable.
  So that we can control for higher order
  correlation.
• If ADF is less than ADF critical value we dont
  reject the null.

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Var in Difference

• ?Yt C1 a11?Yt-1 a12?Xt-1 U1t
• ?Xt C2 a21?Yt-1 a22?Xt-1 U2t
• We notice that the dependent variables are
  different but the set of the independent
  variables are the same.

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Results of the VAR

• After analyzing the VAR in difference results, it
  seems plausible( Based on t-statistics) that
• Interest rate has a direct effect on M1
• M1 has a direct effect on CPI
• CPI has a direct effect on GDP
• Overall
• Even though M1 does not have a direct effect on
  RGDP, it affects RGDP indirectly through CPI

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conclusion

• This paper dealt first with detecting if there is
  a long relationship between variables unit rot
  testing was used to achieve this objective.
• Econometrics results reveals that monetary
  variables have an impact and a causal
  relationship on real GDP, but there were no
  evidence that they play any statistical
  significant role in the determination of real GDP
  in the long run.