Interest rate liberalization

The overall reform strategy was set early on in the reform process—interest rates in the money and bond markets were liberalized first, followed by a gradual liberalization of interest rates on loans and deposits. For lending and deposit rates, reform measures were introduced on foreign currencies ahead of the domestic currency, on loans ahead of deposits, and on long-term and large-value loans and deposits ahead of short-term and small-value instruments (see Hu (2007)).

Since 1996, the interbank market rate, the bond market rate, and the issuing rate on government bonds and policy financial bonds have been liberalized;³ interest rates on foreign currency loans and large-value foreign currency deposits have been deregulated; and the floating band around the renminbi (RMB) lending rate has been widened gradually. In 2004, China introduced a floating central bank lending rate system and removed the ceiling on lending rates and floor on deposit rates of commercial banks. However, floor lending and ceiling deposit rates still exist. The Shanghai Interbank Offered Rate (SHIBOR), which was formally launched in January 2007, has since become the benchmark rate in the money market.

Reform of the exchange rate regime

Exchange rate reform has also moved ahead in recent years, with the foreign exchange market playing a larger role in establishing the renminbi (RMB) exchange rate. On July 21,2005, the PBC announced that it would adopt a market-based managed floating exchange rate regime with reference to a basket of currencies, which has led to greater RMB flexibility. During the period July 2005–March 2008, the RMB appreciated by nearly 18 percent against the U.S. dollar and in real effective terms by around 14 percent (Figure 2).⁴

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China: Renminbi Nominal Exchange Rate and REER, 1994–2008

Citation: IMF Working Papers 2009, 131; 10.5089/9781451872781.001.A001

Sources: People’s Bank of China; and IMF, International Financial Statistics.

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A number of other reforms have been initiated to give the market a bigger role in determining the RMB exchange rate. These measures include introducing a market-maker system and over-the-counter transactions in the interbank foreign exchange market; introducing forward and swap transactions; and widening the daily floating band of the RMB against the U.S. dollar in the interbank spot foreign exchange market.

Inevitably, these changes have raised expectations of a further RMB appreciation, which may be affecting the currency composition of deposits and resulting in a greater willingness to hold RMB balances (Figure 3). Since M2 statistics in China still do not include foreign currencies, the ongoing currency substitution would probably affect the size of M2.

Commonly, the inclusion of the exchange rate in the money demand function can be justified by the fact that changes in the exchange rate alter the domestic value of foreign assets and therefore affect wealth. In addition, expected exchange rate changes can be seen as indicative of expected return on foreign monetary assets and therefore could influence the opportunity cost of holding domestic monetary assets (see Hanburger (1977)).⁵

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China: Currency Substitution and Expectation of Exchange Rate, 2003–07

Citation: IMF Working Papers 2009, 131; 10.5089/9781451872781.001.A001

Sources: People’s Bank of China; and IMF, International Financial Statistics.

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• Development of capital markets. Since the Shanghai Stock Exchange was established in 1990 and households were allowed to open mutual funds accounts in 1991, capital markets have developed rapidly in China (Figures 4 and 5). From 1993 to 1998, China consolidated its capital markets and associated regulations, with the establishment of China Securities Regulatory Commission (CSRC) during this period as a key milestone. From 1998 onward, with the promulgation of the Securities Law, the legal status of China’s capital markets in the economy was formalized and strengthened, with a series of major reforms implemented to facilitate further development. These measures included the initiation of nontradable share reform, restructuring of securities companies, improvement in share issuance procedures, and promotion of institutional investors. By end-2007, stock issues of listed companies totaled US$204 billion and bond issues totaled US$124 billion (of which US$7.5 billion were from convertible bond issues).⁶

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China: Number of Investor Accounts and Listed Securities on the Shanghai Stock Exchange, 1996–2008

Citation: IMF Working Papers 2009, 131; 10.5089/9781451872781.001.A001

Source: CEIC Data Company Ltd.

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China: Household Holdings of Financial Assets, 2001–07

(In trillions of RMB, unless otherwise indicated)

Citation: IMF Working Papers 2009, 131; 10.5089/9781451872781.001.A001

Sources: CEIC Data Company, Ltd.; Chinese authorities; and IMF staff estimates.1/ Takes into account only tradable shares.

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The capital markets offer a diversified investment channel for Chinese households and firms, which have likely altered factors influencing their money holdings. With the emergence and growth of capital markets, the range of investment products has expanded as expected. In addition, the means by which household wealth is managed is evolving rapidly. Wealth management services are proliferating, with the number of individual investor accounts increasing from fewer than 9 million in 1992 to nearly 138 million at end-2007.

A. Earlier Studies

A number of previous studies have analyzed monetary conditions in China. They can be categorized into two distinct modeling approaches (see Austin et al., (2007)), although most of these studies focus on the period before major economic reforms noted in the previous section were undertaken. Beginning with Chow (1987), the first approach applies conventional models developed to estimate money demand in advanced economies (in addition, see Chen (1997); Deng and Liu (1999); Hafer and Kutan (1993 and 1994); Huang (1994); and Yu and Tsui (2000)). In contrast, the second approach has adapted these frameworks to the specific conditions of the Chinese economy (see Feltenstein and Farhadian (1987), Feltenstein and Ha (1992), Girardin (1996), Ma (1993), Qin (1994), and Yi (1993)). Models have typically included extra variables to factor in different aspects of the transition process. For example, much of the work involving Feltenstein uses a constructed “true” price level owing to the inability of measured prices to reflect market conditions with partial price controls in effect. Yi (1993) adds monetization and inflation expectation variables into the money demand function, which enhanced significantly its explanatory power. Qin (1994) introduces two more factors in his money demand equation: (i) the ratio of the growth rates of total savings and loans to capture the special feature of a centrally planned economy; and (ii) a monetization index to approximate the transitional feature of China’s economic reforms, whereas Girardin (1996) uses differences in size of state and nonstate industrial sectors to proxy institutional change.

These studies underscore several major points relevant to modeling and estimating the demand for money—mainly the choice of relevant variables and specification of the estimated model. Failure to provide due consideration to these issues has tended to yield poor results (see Sriram (2001)). For the former, proper specification of opportunity cost variables has been shown in other studies to be the most important factor in getting meaningful results.Regarding the latter, ECMs have been shown to perform well in providing a dynamic framework for estimating short- and long-run properties of money demand. In this paper, recent developments in computer-automated model selection help obtain a parsimonious, empirically constant, data-coherent ECM for broad money demand in China.

B. Model Specification

In a modern economy, money is conventionally held for at least two reasons: (i) as a means of smoothing differences between income and expenditure streams, and (ii) as one among several assets in a portfolio (see Ericsson and Sharma (1996)). The standard theory of money demand posits:

where M is nominal money demand, P is the price level, Y is a scale variable for income or output, and R is a vector (in bold) of returns on various assets. The function f is increasing in Y, decreasing in those elements of R associated with assets excluded from M, and increasing in those elements of R for assets included in M. Commonly, (1) is also specified in log-linear form, albeit with the interest rates as levels.

In light of China’s financial structure and data described later in this section, four assets are considered here: broad money, domestic goods, equity shares, and foreign currency.⁷ The nominal returns for the first three are denoted R^d, Δp, Δs, respectively. For foreign currency, the nominal return and change in the exchange rate are denoted R^f and Δe, respectively (see Appendix I for a complete description of these data). Thus, (1) can be written as follows (logs are in lower case):

The coefficient μ₀ is an intercept, μ₁ the income elasticity, and μ₂… μ₆ the semi-elasticities on the RMB deposit interest rate, foreign currency deposit interest rate, inflation rate, change of stock prices, and change in the exchange rate, with expected signs on the coefficients. μ₁> 0, μ₂> 0, μ₃< 0, μ₄< 0, μ₅< 0, μ₆< 0.

C. Integration and Cointegration Tests

Initially, unit root tests are conducted on each right-hand side variable in equation (2). Subsequently, Johansen’s maximum likelihood procedure is applied to test for cointegration among real money, real output, the RMB deposit rate, the U.S. dollar deposit rate, stock prices, the inflation rate, and the exchange rate. Using this framework, coefficient restrictions and the adjustment mechanisms are examined (see Ericsson et al. (1996, 1998, and 2007)).

Integration

Table 1 shows Augmented Dickey-Fuller (ADF) statistics for the main variables in our analysis. Using standard unit root tests, all variables appear to be integrated in either the first or second order, with m − p, R^d, R^f, Δp, Δe, and Δs as I(1) and p, e, s as I(2). For y, the statistical evidence is less conclusive. The estimated root for Δy, however, is 0.19 (= 1 - 0.81), which is much less than unity, suggesting that y is in fact I(1).

Table 1.

China: Unit Root Tests (ADF statistics)^1,2,3

¹The deviation from unity of the estimated largest root is in parentheses. It should be approximately zero if the series has a unit root.

²The lag length of the reported ADF regression is based on minimizing the Akaike Information Criterion, starting with a maximum of four lags.

³Here and elsewhere in this paper, asterisks * and ** denote rejection of the null hypothesis at the 5 percent and 1 percent critical values, respectively.

Table 1.

China: Unit Root Tests (ADF statistics)^1,2,3

	Variable
Null Order	m − p	y	R d	R f	P	e	S	Δp	Δe	Δs
I(1)	-0.70 (-0.05)	-0.32 (-0.01)	-0.41 (-0.01)	-1.07 (-0.06)	-0.82 (-0.03)	3.45 (0.24)	-3.17 (-0.26)	-3.33 (-0.41)	1.64 (0.34)	-3.29 (-0.91)
I(2)	-3.61* (-1.00)	-2.68 (-0.81)	-5.59** (-1.07)	-6.42** (-0.87)	-3.33 (-0.41)	1.64 (0.34)	-3.29 (-0.91)	-6.49** (-2.50)	-12.66** (-1.55)	-9.72** (-2.39)
I(3)	-3.15 (-2.35)	-7.94** (-4.09)	-6.79** (-4.23)	-5.58** (-2.43)	-6.49** (-2.50)	-12.66** (-1.55)	-9.72** (-2.39)	-9.49** (-5.70)	-5.87** (-4.80)	-8.97** (-3.94)

¹The deviation from unity of the estimated largest root is in parentheses. It should be approximately zero if the series has a unit root.

²The lag length of the reported ADF regression is based on minimizing the Akaike Information Criterion, starting with a maximum of four lags.

³Here and elsewhere in this paper, asterisks * and ** denote rejection of the null hypothesis at the 5 percent and 1 percent critical values, respectively.

View Table

Table 1.

China: Unit Root Tests (ADF statistics)^1,2,3

	Variable
Null Order	m − p	y	R d	R f	P	e	S	Δp	Δe	Δs
I(1)	-0.70 (-0.05)	-0.32 (-0.01)	-0.41 (-0.01)	-1.07 (-0.06)	-0.82 (-0.03)	3.45 (0.24)	-3.17 (-0.26)	-3.33 (-0.41)	1.64 (0.34)	-3.29 (-0.91)
I(2)	-3.61* (-1.00)	-2.68 (-0.81)	-5.59** (-1.07)	-6.42** (-0.87)	-3.33 (-0.41)	1.64 (0.34)	-3.29 (-0.91)	-6.49** (-2.50)	-12.66** (-1.55)	-9.72** (-2.39)
I(3)	-3.15 (-2.35)	-7.94** (-4.09)	-6.79** (-4.23)	-5.58** (-2.43)	-6.49** (-2.50)	-12.66** (-1.55)	-9.72** (-2.39)	-9.49** (-5.70)	-5.87** (-4.80)	-8.97** (-3.94)

¹The deviation from unity of the estimated largest root is in parentheses. It should be approximately zero if the series has a unit root.

²The lag length of the reported ADF regression is based on minimizing the Akaike Information Criterion, starting with a maximum of four lags.

³Here and elsewhere in this paper, asterisks * and ** denote rejection of the null hypothesis at the 5 percent and 1 percent critical values, respectively.

Cointegration

Cointegration analysis helps clarify the long-run relationships between integrated variables.Johansen’s (1991) procedure is maximum likelihood for a finite-order vector autoregression (VAR), which is easily calculated for such systems and used here. Based on the data’s statistical properties and the economic and historical context discussed in earlier sections, tests for cointegration are conducted among the I(1) variables, or m − p, y, R^d, R^f, Δs, Δp, and Δe.

Empirically, the lag order of the VAR is not known a priori, so testing for this may be fruitful to ensure reasonable power of the Johansen procedure. Beginning with a fourth-order VAR for m − p, y, R^d, R^f, Δs, Δp, and Δe and including an intercept and seasonal dummies, it is statistically acceptable to simplify to a first-order VAR (see Table 2).

Table 2.

China: Order of Vector Autoregressions¹

¹For each order of vector autoregressions, the number of unrestricted parameters k, the log-likelihood £, and the Schwarz criterion (SC) are reported. The SC in effect adjusts a measure of the model’s goodness of fit (the log of the determinant of the estimated error covariance matrix) for the model’s degree of parsimony. A smaller SC indicates a better-fitting model for a given number of parameters or a more parsimonious model for a given goodness of fit (see Ericsson and de Brouwer (1998)).

Table 2.

China: Order of Vector Autoregressions¹

Null hypothesis
System	k	£	SC
VAR(4) ↓	224	1444.0	-38.52
VAR(3) ↓	175	1372.3	-39.48
VAR(2) ↓	126	1302.80	-40.53
VAR(1)	77	1266.25	-42.85

¹For each order of vector autoregressions, the number of unrestricted parameters k, the log-likelihood £, and the Schwarz criterion (SC) are reported. The SC in effect adjusts a measure of the model’s goodness of fit (the log of the determinant of the estimated error covariance matrix) for the model’s degree of parsimony. A smaller SC indicates a better-fitting model for a given number of parameters or a more parsimonious model for a given goodness of fit (see Ericsson and de Brouwer (1998)).

View Table

Table 2.

China: Order of Vector Autoregressions¹

Null hypothesis
System	k	£	SC
VAR(4) ↓	224	1444.0	-38.52
VAR(3) ↓	175	1372.3	-39.48
VAR(2) ↓	126	1302.80	-40.53
VAR(1)	77	1266.25	-42.85

¹For each order of vector autoregressions, the number of unrestricted parameters k, the log-likelihood £, and the Schwarz criterion (SC) are reported. The SC in effect adjusts a measure of the model’s goodness of fit (the log of the determinant of the estimated error covariance matrix) for the model’s degree of parsimony. A smaller SC indicates a better-fitting model for a given number of parameters or a more parsimonious model for a given goodness of fit (see Ericsson and de Brouwer (1998)).

Standard estimates using Johansen’s procedure are derived and applied to this first-order VAR (Table 3). The maximal eigenvalue and trace eigenvalue statistics λ_max and λ_trace strongly reject the null hypothesis of no cointegration in favor of at least two cointegrating vectors. However, parallel statistics with a degree-of-freedom adjustment ${\lambda }_{\max }^a$ and ${\lambda }_{\mathit{\text{trace}}}^a$ suggest at least one cointegrating vector, and thus also likely indicate two cointegration relationships.

Table 3.

China: Cointegration Analysis

Table 3.

China: Cointegration Analysis

Eigenvalues Hypotheses	0.72 r = 0	0.62 r <1	0.44 r <2	0.30 r<3	0.24 r<4	0.09 r <5	0.007 r <6
λmax	66.85 **	50.93 **	29.74	18.42	14.50	5.14	0.37
${\lambda }_{\max }^a$	57.85 **	44.07 *	25.74	15.94	12.55	4.45	0.32
λtrace	185.94 **	119.09 **	68.17	38.42	20.00	5.50	0.37
${\lambda }_{trace}^a$	160.91 **	103.06 *	58.99	33.25	17.31	4.76	0.32
Standardized eigenvectors β′
Variable	m-p	y	R d	R f	Δs	Δp	Δe
	1.000	-0.758	-0.034	0.177	-1.139	21.247	27.527
	-0.809	1.000	-0.033	-0.030	0.307	5.164	-3.996
	-448.83	772.73	1.000	-5.758	12.878	-1035.2	2916.3
	85.375	-123.89	1.247	1.000	-2.755	27.902	-271.53
	10.782	-15.332	0.204	0.261	1.000	-1.108	9.623
	-1.071	1.220	-0.216	0.172	0.033	1.000	1.125
	0.176	-0.284	0.003	-0.006	0.032	0.075	1.000
Standardized adjustment coefficients α′
m-p	-0.011	0.002	0.00005	-0.002	-0.011	-0.002	0.033
y	0.006	0.0003	0.00002	0.001	0.0001	-0.002	0.019
R d	0.224	1.921	0.007	0.049	-0.358	0.243	0.347
R f	-0.776	0.622	0.004	0.032	-0.255	-0.174	-1.170
Δs	0.244	-1.518	-0.002	0.015	-0.172	-0.045	-0.218
Δp	-0.014	-0.073	0.00009	0.001	0.004	0.002	-0.003
Δe	-0.014	0.003	-0.0002 Weak ex	0.0003 eneity test	-0.005 statistics	0.0002	-0.003
Weak exogeneity test statistics
	m-p	y	R d	R f	Δs	Δp	Δe
χ2(1)	1.36	1.45	0.31	6.68**	1.56	2.28	8.02 **
Statistics for testing the significance of a given variable
	m-p	y	R d	Rf	Δs	Δp	Δe
χ2(1)	0.44	0.12	1.07	11.94 **	10.19**	10.95 **	11.54 **
Multivariate statistics for testing stationarity
	m-p	y	R d	R f	Δs	Δp	Δe
χ2 (6)	45.13 **	43.94**	56.94**	54.07**	33.80**	39.74 **	54.88**

View Table

Table 3.

China: Cointegration Analysis

Eigenvalues Hypotheses	0.72 r = 0	0.62 r <1	0.44 r <2	0.30 r<3	0.24 r<4	0.09 r <5	0.007 r <6
λmax	66.85 **	50.93 **	29.74	18.42	14.50	5.14	0.37
${\lambda }_{\max }^a$	57.85 **	44.07 *	25.74	15.94	12.55	4.45	0.32
λtrace	185.94 **	119.09 **	68.17	38.42	20.00	5.50	0.37
${\lambda }_{trace}^a$	160.91 **	103.06 *	58.99	33.25	17.31	4.76	0.32
Standardized eigenvectors β′
Variable	m-p	y	R d	R f	Δs	Δp	Δe
	1.000	-0.758	-0.034	0.177	-1.139	21.247	27.527
	-0.809	1.000	-0.033	-0.030	0.307	5.164	-3.996
	-448.83	772.73	1.000	-5.758	12.878	-1035.2	2916.3
	85.375	-123.89	1.247	1.000	-2.755	27.902	-271.53
	10.782	-15.332	0.204	0.261	1.000	-1.108	9.623
	-1.071	1.220	-0.216	0.172	0.033	1.000	1.125
	0.176	-0.284	0.003	-0.006	0.032	0.075	1.000
Standardized adjustment coefficients α′
m-p	-0.011	0.002	0.00005	-0.002	-0.011	-0.002	0.033
y	0.006	0.0003	0.00002	0.001	0.0001	-0.002	0.019
R d	0.224	1.921	0.007	0.049	-0.358	0.243	0.347
R f	-0.776	0.622	0.004	0.032	-0.255	-0.174	-1.170
Δs	0.244	-1.518	-0.002	0.015	-0.172	-0.045	-0.218
Δp	-0.014	-0.073	0.00009	0.001	0.004	0.002	-0.003
Δe	-0.014	0.003	-0.0002 Weak ex	0.0003 eneity test	-0.005 statistics	0.0002	-0.003
Weak exogeneity test statistics
	m-p	y	R d	R f	Δs	Δp	Δe
χ2(1)	1.36	1.45	0.31	6.68**	1.56	2.28	8.02 **
Statistics for testing the significance of a given variable
	m-p	y	R d	Rf	Δs	Δp	Δe
χ2(1)	0.44	0.12	1.07	11.94 **	10.19**	10.95 **	11.54 **
Multivariate statistics for testing stationarity
	m-p	y	R d	R f	Δs	Δp	Δe
χ2 (6)	45.13 **	43.94**	56.94**	54.07**	33.80**	39.74 **	54.88**

Table 3 also reports the standardized eigenvectors and adjustment coefficients, denoted β’ and α, respectively. The first row of β’ is the estimated cointegrating vector, which, from equation (2), can be written in the form of:

$\begin{array}{ccc}m-p={\widehat{\mu }}_0+0.758y+0.034R^d-0.177R^f+1.139{\Delta }s-5.312(4\cdot {\Delta }p)-6.882(4\cdot {\Delta }e),& & (3)\end{array}$

where ^{^} denotes the corresponding estimate. Each coefficient appears to have the expected sign except Δs. Inflation (measured as an annual rate) also has a high semi-elasticity. This could reflect the significant impact in China that the return on real assets may have on money demand (since the inflation rate could be regarded as a proxy for a return on real assets given past interest rate caps and price controls).

The adjustment coefficients α in Table 3 measure the feedback effects of the (lagged) disequilibrium in the cointegrating relation for variables in the VAR. Specifically, -0.011 is the estimated feedback coefficient for the money equation. The negative coefficient implies that lagged excess holdings of money induce smaller holdings of current money, with the small numerical value implying slow adjustment to remaining disequilibrium—approximately 1.1 percent in the first quarter. The size of the estimated coefficient lies at the lower end for developed and developing countries: -0.26, -0.15, and -0.20 where found for the Netherlands, Germany, and France, respectively, and -0.12 and -0.08 for Argentina and Greece (see Taylor (1986) and Ericsson and Sharma (1996)). The lower adjustment coefficient for China may reflect the lack of availability of alternative assets to M2 and a generally repressed financial system.

The remainder of Table 3 reports three types of statistics—one for testing the weak exogeneity of a given variable in the cointegrating vector, the second for the significance of a given variable in the cointegrating vector, and the third for the stationarity of a given variable. Each statistic tests the linear restrictions on α or β by direct application of Johansen and Juselius (1990), and each is asymptotically chi-squared under the associated null hypothesis. In the first chi-squared test, y, R^d, Δs, and Δp appear weakly exogenous when tested individually, but not R^f and Δe. Confirmation of weak exogeneity permits analysis of the cointegrating vector in a single equation conditional ECM of money without loss of information, which is done in the next section. In the second chi-squared test, which looks at the significance of individual variables in the cointegrating vector, we see whether the corresponding coefficient in eigenvectors β can be set to zero. R^f, Δs, Δp, and Δe appear to be very significant, suggesting their importance in the VAR. The final chi-squared test on the stationarity of a given variable is reported in Table 3. Empirically, all the tests reject stationarity of these variables at a highly significant level.

D. A Dynamic Model of Money Demand

This section estimates an autoregressive distributed lag (ADL) model and simplifies it to a parsimonious ECM.⁸ Given the choice of variables and the lag lengths in the VAR above, a fourth-order ADL appears a natural starting point for single equation modeling. This regression is modified in two ways. First, a dummy variable D_ cr_t for 1997Q3–1998Q3 is included to account for fluctuations in money demand associated with the Asian financial crisis. Second, a dummy variable D_ ex_t for 2005Q3-2008Q1 is added to reflect RMB exchange rate regime reform since 2005, which is intended to capture one-way expectations of an RMB appreciation.

Most of the individual coefficients in the fourth-order ADL are estimated imprecisely and are of little interest by themselves. However, the long-run solution of the ADL shown in equation (4) is of greater relevance, as estimated coefficients are well determined,

Where t = 52 [1995Q2-2008Q1], with satandard errors shown in parentheses.

ADLs have error correction representations. Thus, the long-run money demand relation can be explicitly embedded in an ADL model and written as an ECM. Appendix II lists the estimated coefficients, standard errors, and test statistics for the ECM representation of the fourth-order ADL model of m − p, y, R^d, R^f, Δs, Δp, and Δe—a starting point for the general-to-specific modeling. However, when the fourth-order ADL is transformed to its unrestricted ECM representation, many of the coefficients are both economically less relevant and statistically insignificant.

The general-to-specific process of streamlining an initial unrestricted model follows either a “liberal strategy,” which minimizes the non-deletion probability of relevant variables to keep as many variables that might matter as possible, or a “conservative strategy,” which minimizes the non-deletion probability of irrelevant variables to avoid retaining them. In this paper, we use the former strategy. PcGets provides a natural path for the simplification of the ADL to a highly parsimonious, economically interpretable, and statistically acceptable ECM in equation (5):⁹

where t = 52 [1995Q2-2008Q1]    R² =0.74    $\widehat{\sigma }=0.795$

Durbin Watson statistics = 2.5 AR: F(4,34) = 1.033 ARCH: F(4,30) = 0.563

Normality: χ² (2) = 5.872 RESET¹⁰: F(1,37) = 0.154 Hetero: F(27,10) = 0.308

Ordinary equation standard errors appear in parentheses.

E. The Model’s Properties

This section considers the economic interpretation and statistical properties of the ECM in equation (5). In the first section, we consider the short- and long-run properties of the model.The second section discusses the model’s statistical properties.