由弹性价格货币模型论中国汇率和利率的联动性大学论文.doc

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----由弹性价格货币模型论中国汇率和利率的联动性

内容摘要
本文的初衷是想通过计量经济学知识检验弹性价格货币模型，运用本学期所学到的计量经济学知识。但随着探讨的深入，我们不断地发现问题并试图找出修正问题的方法，对模型进行了不断的调整。最后我们得出结论，由于我国目前国情特殊等种种原因该模型并不适用于中国这种汇率并分析了背后的原因。
在整个论文的完成中，我们发现我们学到的东西远比我们开始时预想得多。除了学会了运用计量经济学知识解决实际问题我们还获得了很多启示，也总结了许多经验，于是我们在写最后的报告时附加了我们的体会，希望可以和大家分享。

关键词：
弹性价格货币模型，汇率，实际国民收入水平，利率水平，货币供给水平

导论
汇率决定理论是西方外汇理论的核心，也一直是国际经济学中最为活跃的领域之一。随着世界经济的变化和国际货币体制的变迁，汇率决定理论也在不断地发展

货币模型是西方汇率决定理论中资产市场分析法的一个重要的分支。其中资产市场分析法是从20世纪七十年代中期开始迅速成长起来的汇率决定理论。货币法（Monetary Approach）和资产组合平衡法（Portfolio Balance Approach）是资产市场法的两个主要的分支。货币法中也有两种分析模型，一是弹性价格货币模型（Flexible-Price Monetary Model），另一个是粘性价格货币模型（Sticky-Price Monetary Model）。我们检验的重点就是弹性价格货币模型。

经济解释
一、弹性价格货币模型
1．弹性价格货币模型的基本思想
　　弹性价格货币模型是现代汇率理论中最早建立、也是最基础的汇率决定模型。其主要代表人物有弗兰克尔（J·Frenkel）、穆莎（M·Mussa）、考霍（P·Kouri）、比尔森（J·Bilson）等人。它是在1975年瑞典斯德哥尔摩附近召开的关于“浮动汇率与稳定政策”的国际研讨会上被提出来的。
弹性货币法的一个基本思想：汇率是两国货币的相对价格，而不是两国商品的相对价格，因此汇率水平应主要由货币市场的供求状况决定。

2．弹性货币法的论述
重要假设：
　（1） 稳定的货币需求方程，即货币需求同某些经济变量存在着稳定的关系；
　（2） 购买力平价持续有效。
S=α(y*-y)+β(i-i*)+(Ms-Ms*)

　　从模型中我们可以看出，本国与外国之间实际国民收入水平、利率水平以及货币供给水平通过对各自物价水平的影响而决定了汇率水平。本国利率上升会降低货币需求，在原有的价格水平与货币供给水平上，这会造成支出的增加、物价的上升，从而通过购买力平价关系造成本国货币的贬值
相关数据收集
在中经网中我们找到了1985年到2002年美国，中国各自的官方汇率，实际国民收入，实际利率，货币供给M1，M2。现在的问题是M1,M2都是衡量货币供给的指标，应当选哪个？我们选择了M2.因为在Frederic S. Mishkin(米什金)的《The Economics of Money, Banking, and Financial Market》书我们找到了m1,m2的定义，而且书中明确指出，M2由于其速率远比M1稳定，因而在衡量货币供给方面比M1更好。

在P57给出了M1,M2的定义：
M1=Currency +Traveler’s checks +Demand deposits + Other checkable deposits
M2 M1 + Small denomination time deposits + savings deposits and money market deposit accounts + Money market mutual fund shares

作者在p560写道:”The relative stability of M2 velocity suggests that money demand functions in which the money supply is defined as M2 might performed substantially better than those in which the money supply is defined as M1.”
原始数据如下：
年 度
中国汇率S
美国国民收入Y*
中国国民收入Y
中国实际利率I
美国实际利率I*
美国M2*
中国M2
1985
2.94
55985.65
12133
-2.02
6.52
28010.48
4874.9
1986
3.45
57264.02
13413.65
3.17
5.97
30980.83
6348.6
1987
3.72
59578.44
15097.47
2.72
5.01
31939.3

7957.4
1988
3.72
62681.01
16958.06
-2.79
5.7
33946.54
9602.1
1989
3.77
64121.72
17739.94
2.33
6.79
35906.38
11393.1
1990
4.78
64915.73
18598.37
3.49
5.87
37674.36
14681.9
1991
5.32
65061.65
20405.99
1.79
4.65
38900.36
18598.9
1992
5.51
66602.99
23502.67
0.68
3.72
39547.64
24327.3
1993
5.76
68257.01
26798.67
-3.12
3.52
40137.58
35680.8
1994
8.62
71056.98
30525.25
-7.44
4.96
40153.51
46920.3
1995
8.35
73322
33496.5
-0.99
6.51
42418.03
60743.5
1996
8.31
75932.52
36830.48
3.93
6.2
45010.02
76095.3
1997
8.29
79473.88
4040025
7.76
6.36
47968.64
91867.81
1998
8.28
83736.65
43205.3
9
7.02
52809.77
105560.11
1999
8.28
87407.58
46178.05
8.22
6.45
57122.22
121042.06
2000
8.28
91182.21
49249.8
4.86
6.98
61088.86
135960.23
2001
8.28
91502.37
52826.99
4.61
4.44
69677.66
156411.93
2002
8.28
93332.11
57512.11
5.62
3.47
72688.63
186790.54

由于模型中s , y, I Ms是指汇率，实际国民收入，货币供给量的自然对数值，于是作如下数据处理：( s=lnS, y=lnY, i=I, m=lnM)

年 度
s
y*-y

i-i*
m-m*
1985
1.078409581
2.607576
-0.0854
-2.82689
1986
1.238374231
2.689774
-0.028
-2.82351
1987
1.313723668
2.68649
-0.0229
-2.70346
1988
1.313723668
2.621039
-0.0849
-2.57653
1989
1.327075001
2.61204
-0.0446
-2.47498
1990
1.564440547
2.814457
-0.0238
-2.5068
1991
1.671473303
2.83098
-0.0286
-2.40937
1992
1.706564623
2.7482
-0.0304
-2.19247
1993
1.750937475
2.685865
-0.0664
-1.86864
1994
2.154085085
2.999013
-0.124
-1.99834
1995
2.122261539
2.905681
-0.075
-1.76317
1996
2.117459609
2.840979
-0.0227
-1.59236
1997
2.115049969
2.791642
0.014
-1.46525
1998
2.113842968
2.775557
0.0198
-1.42126
1999
2.113842968
2.75192
0.0177
-1.3629
2000
2.113842968
2.729797
-0.0212
-1.31381
2001
2.113842968
2.663186
0.0017
-1.30523
2002
2.113842968
2.598012
0.0215
-1.17004

平稳性检验：
单位根检验：
y一阶差分，滞后0 期

ADF Test Statistic
-4.485774
1% Critical Value*
-4.6712

5% Critical Value
-3.7347

10% Critical Value
-3.3086
*MacKinnon critical values for rejection of hypothesis of a unit root.

Augmented Dickey-Fuller Test Equation
Dependent Variable: D(Y,2)
Method: Least Squares
Date: 06/14/05 Time: 09:35
Sample(adjusted): 1987 2002
Included observations: 16 after adjusting endpoints
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(Y(-1))
-1.211682
0.270117
-4.485774
0.0006
C
0.056801
0.065830
0.862841
0.4039
@TREND(1985)
-0.006505
0.006251
-1.040761
0.3170
R-squared
0.607580
Mean dependent var
-0.009211
Adjusted R-squared
0.547208
S.D. dependent var

0.165921
S.E. of regression
0.111648
Akaike info criterion
-1.379568
Sum squared resid
0.162049
Schwarz criterion
-1.234707
Log likelihood
14.03654
F-statistic
10.06389

M的单位根检验：
滞后期为0，2阶差分
ADF Test Statistic
-6.098875
1% Critical Value*
-4.7315

5% Critical Value
-3.7611

10% Critical Value
-3.3228
*MacKinnon critical values for rejection of hypothesis of a unit root.

Augmented Dickey-Fuller Test Equation
Dependent Variable: D(M,3)
Method: Least Squares
Date: 06/14/05 Time: 09:36
Sample(adjusted): 1988 2002
Included observations: 15 after adjusting endpoints
Variable
Coefficient
Std. Error
t-Statistic
Prob
.

I的单位根检验
滞后期为1，2阶差分
ADF Test Statistic
-4.409217
1% Critical Value*
-4.8025

5% Critical Value
-3.7921

10% Critical Value
-3.3393
*MacKinnon critical values for rejection of hypothesis of a unit root.

Augmented Dickey-Fuller Test Equation
Dependent Variable: D(I,3)
Method: Least Squares
Date: 06/14/05 Time: 09:29
Sample(adjusted): 1989 2002
Included observations: 14 after adjusting endpoints
Variable
Coefficient
Std. Error
t-Statistic
Prob.

E一阶差分滞后1期
ADF Test Statistic
-3.415388
1% Critical Value*
-4.7315

5% Critical Value
-3.7611

10% Critical Value
-3.3228
*MacKinnon critical values for rejection of hypothesis of a unit root.

Augmented Dickey-Fuller Test Equation
Dependent Variable: D(E,2)
Method: Least Squares
Date: 06/14/05 Time: 09:13
Sample(adjusted): 1988 2002
Included observations: 15 after adjusting endpoints
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(E(-1))
-1.480432
0.433459
-3.415388
0.0058
D(E(-1),2)
0.297192
0.285576
1.040677
0.3204
C
0.204362
0.098391
2.077042
0.0620
@TREND(1985)
-0.011981
0.007830
-1.530147
0.1542
R-squared
0.610438
Mean dependent var
-0.005023
Adjusted R-squared
0.504194
S.D. dependent var
0.168304
S.E. of regression
0.118509
Akaike info criterion
-1.204485
Sum squared resid
0.154487
Schwarz criterion
-1.015671
Log likelihood
13.03364
F-statistic
5.745612
Durbin-Watson stat
2.071058
Prob(F-statistic)
0.012930

因果关系检验：
E 与m2 互为因果
Pairwise Granger Causality Tests
Date: 06/14/05 Time: 09:23
Sample: 1985 2002
Lags: 2
Null Hypothesis:
Obs
F-Statistic
Probability
M does not Granger Cause E
16
14.0385
0.00094
E does not Granger Cause M
6.99392
0.01097

E与 y：互不为因果
Pairwise Granger Causality Tests
Date: 06/14/05 Time: 09:28
Sample: 1985 2002
Lags: 1

Null Hypothesis:
Obs
F-Statistic
Probability
Y does not Granger Cause E
17
0.93813
0.34920
E does not Granger Cause Y
0.09072
0.76769

E与 I 互不为因果
Pairwise Granger Causality Tests
Date: 06/15/05 Time: 11:50
Sample: 1985 2002
Lags: 3
Null Hypothesis:
Obs
F-Statistic
Probability
I does not Granger Cause E
15
0.44195
0.72943
E does not Granger Cause I
0.83104
0.51325

参数的估计
最小二乘回归得

Dependent Variable: E
Method: Least Squares
Date: 05/18/04 Time: 21:02
Sample: 1985 2002
Included observations: 18
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
-0.617765
0.246003
-2.511204
0.0249
Y
1.290026
0.085334
15.11731
0.0000
I
-0.142262
0.263086
-0.540744
0.5972
M
0.575430
0.018366
31.33085
0.0000
R-squared
0.993250
Mean dependent var
1.780155
Adjusted R-squared
0.991804
S.D. dependent var
0.385816
S.E. of regression
0.034928
Akaike info criterion
-3.677909
Sum squared resid
0.017080
Schwarz criterion
-3.480048
Log likelihood
37.10118
F-statistic
686.7379
Durbin-Watson stat
1.371224
Prob(F-statistic)
0.000000
线性关系显著（由F统计量得知）, R2=0.993250说明拟合优度很好，
但是由I的T检验中t=0.540744，其绝对值小于2，可以看出，I 作为解释变量不是很合理

经济意义检验：
回归所得的I的系数符号与经济意义不符 ，其他变量经济意义符合

计量经济学检验
1． 重共线性检验
相关系数矩阵：
的确存在多重线性，并且I的t统计量不显著

I
Y
M
I
1.000000
-0.238763
0.526896
Y
-0.238763
1.000000
0.176456
M
0.526896
0.176456
1.000000

逐步回归得：
Dependent Variable: E
Method: Least Squares
Date: 05/18/04 Time: 13:08
Sample: 1985 2002
Included observations: 18

Variable
Coefficient
Std. Error
t-Statistic
Prob.

C
-3.269684
2.051116
-1.594100
0.1305
Y
1.841804
0.747527
2.463862
0.0255

R-squared
0.275054
Mean dependent var
1.780155
Adjusted R-squared
0.229745
S.D. dependent var
0.385816
S.E. of regression
0.338608
Akaike info criterion
0.776491
Sum squared resid
1.834484
Schwarz criterion
0.875421
Log likelihood
-4.988419
F-statistic
6.070615
Durbin-Watson stat
0.115207
Prob(F-statistic)
0.025456
Dependent Variable: E
Method: Least Squares
Date: 05/18/04 Time: 13:08
Sample: 1985 2002
Included observations: 18

Variable
Coefficient
Std. Error
t-Statistic
Prob.

C
1.887803
0.112636
16.76017
0.0000
I
3.322453
2.184643
1.520822
0.1478

R-squared
0126299
Mean dependent var
1.780155
Adjusted R-squared
0.071693
S.D. dependent var
0.385816
S.E. of regression
0.371728
Akaike info criterion
0.963132
Sum squared resid
2.210912
Schwarz criterion
1.062063
Log likelihood
-6.668192
F-statistic
2.312898
Durbin-Watson stat
0.271065
Prob(F-statistic)
0.147820

Dependent Variable: E
Method: Least Squares
Date: 05/18/04 Time: 13:08
Sample: 1985 2002
Included observations: 18

Variable
Coefficient
Std. Error
t-Statistic
Prob.

C
2.998507
0.128513
23.33228
00000
M
0.613007
0.062182
9.858300
0.0000

R-squared
0.858640
Mean dependent var
1.780155
Adjusted R-squared
0.849805
S.D. dependent var
0.385816
S.E. of regression
0.149523
Akaike info criterion
-0.858295
Sum squared resid
0.357714
Schwarz criterion
-0.759365
Log likelihood
9.724656
F-statistic
97.18608
Durbin-Watson stat
1.194219
Prob(F-statistic)
0.000000

选M为第一个解释变量
Dependent Variable: E
Method: Least Squares
Date: 05/18/04 Time: 13:20
Sample: 1985 2002
Included observations: 18
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
-0.675075
0.216703
-3.115204
0.0071
M
0.569518
0.014405
39.53654
0.0000
Y
1.308324
0.076469
17.10928
0.0000
R-squared
0.993109
Mean dependent var
1.780155
Adjusted R-squared
0.992191
S.D. dependent var
0.385816
S.E. of regression
0.034095

Akaike info criterion
-3.768349
Sum squared resid
0.017437
Schwarz criterion
-3.619953
Log likelihood
36.91514
F-statistic
1080.952
Durbin-Watson stat
1.286969
Prob(F-statistic)
0.000000

Dependent Variable: E
Method: Least Squares
Date: 05/18/04 Time: 13:20
Sample: 1985 2002
Included observations: 18
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
3.070207
0.127327
24.11284
0.0000
M
0.677111
0.068721
9.853034
0.0000
I
-1.719333
0.971156
-1.770398
0.0970
R-squared
0.883072
Mean dependent var
1.780155
Adjusted R-squared

0.867482
S.D. dependent var
0.385816
S.E. of regression
0.140449
Akaike info criterion
-0.936939
Sum squared resid
0.295887
Schwarz criterion
-0.788544
Log likelihood
11.43246
F-statistic
56.64223
Durbin-Watson stat
1.499001
Prob(F-statistic)
0.000000

由于调整后可决系数最大的为Y,逐步回归可以终止，最后的回归模型为：

Dependent Variable: E
Method: Least Squares
Date: 05/18/04 Time: 13:20
Sample: 1985 2002
Included observations: 18
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
-0.675075
0.216703
-3.115204
0.0071
M
0.569518
0.014405
39.53654
0.0000
Y
1.308324
0.076469
17.10928
0.0000
R-squared
0.993109
Mean dependent var
1.780155
Adjusted R-squared
0.992191
S.D. dependent var
0.385816
S.E. of regression
0.034095
Akaike info criterion
-3.768349
Sum squared resid
0.017437
Schwarz criterion
-3.619953
Log likelihood
36.91514
F-statistic
1080.952
Durbin-Watson stat
1.286969
Prob(F-statistic)
0.000000

可以看出，只有Y和M对模型的影响显著，而I对模型没有什么影响。

2．异方差检验

做ARCH（P=3）检验：
ARCH Test:
F-statistic
5.461801
Probability
0.015183
Obs*R-squared
8.974892
Probability
0.029627

Test Equation:
Dependent Variable RESID^2
Method: Least Squares
Date: 06/13/05 Time: 20:26
Sample(adjusted): 1988 2002
Included observations: 15 after adjusting endpoints
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
0.000154
0.000318
0.482194
0.6391
RESID^2(-1)
1.038941
0.329839
3.149842
0.0092
RESID^2(-2)
0.100379
0.423071
0.237263
0.8168
RESID^2(-3)
-0.074375
0.227784
-0.326517
0.7502
R-squared
0.598326
Mean dependent var
0.000925
Adjusted R-squared
0.488779
S.D. dependent var
0.000874
S.E. of regression
0.000625
Akaike info criterion
-11.69368
Sum squared resid
4.30E-06
Schwarz criterion
-11.50487
Log likelihood
91.70261
F-statistic
5.461801
Durbin-Watson stat
1.912115
Prob(F-statistic)
0.015183
Obs*R-squared 对应的P值是0.029627 〈 0.03初步判定没有异方差存在

用WHITE检验：
White Heteroskedasticity Test:
F-statistic
2.788935
Probability
0.066926
Obs*R-squared
10.86065
Probability
0.092780

Test Equation:
Dependent Variable: RESID^2
Method: Least Squares
Date: 06/13/05 Time: 20:46
Sample: 1985 2002
Included observations: 18
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
0.093689
0.199497
0.469627
0.6478
M
0.006867
0.005187
1.323964
0.2124
M^2
0.001506
0.001249
1.205570
0.2533
I
0.002166
0.014963
0.144748
0.8875
I^2
0.106044
0.167649
0.632536
0.5400
Y
-0.058697
0.145379
-0.403752
0.6941
Y^2
0.009988
0.026364
0.378865
0.7120
R-squared
0.603369
Mean dependent var
0.000949
Adjusted R-squared
0.387025
S.D. dependent var
0.000952
S.E. of regression
0.000746
Akaike info criterion
-11.27944
Sum squared resid
6.12E-06
Schwarz criterion
-10.93318
Log likelihood
108.5149
F-statistic
2.788935
Durbin-Watson stat
1.248839