1.2 Background of the Study 1.2.1 Inflation Rate Currency buys fewer goods and services when the general price level rises. Inflation also reflects loss in the purchasing power of money which means a loss of real value in the internal medium of exchange and unit of account in the economy. The main measure of price inflation is the inflation rate, the annualized percentage change in a general price index, normally the Consumer Price Index, over time. Inflation’s effects on an economy are various and can be simultaneously positive and negative. Negative effects of inflation include a decrease in the real value of money and other monetary items over time, uncertainty over future inflation may discourage investment and savings, and high inflation may lead to shortages of goods if consumers begin hoarding out of concern that prices will increase in the future. Economists generally agree that high rates of inflation and hyperinflation are caused by an excessive growth of the money supply. Views on which factors determine low to moderate rates of inflation are more varied. Low or moderate inflation may be attributed to fluctuations in real demand for goods and services, or changes in available supplies such as during scarcities, as well as to growth in the money supply. However, the consensus view is that a long sustained period of inflation is caused by money supply growing faster than the rate of economic growth.

High or unpredictable inflation rates are regarded as harmful to an overall economy. They add inefficiencies in the market, and make it difficult for companies to budget or plan long-term. Inflation can act as a drag on productivity as companies are forced to shift resources away from products and services in order to focus on profit and losses from currency inflation. Uncertainty about the future purchasing power of money discourages investment and saving. Friedman (1977) argues that high in¬‚ation can give rise to political pressure to reduce it. The monetary authority, however, may or may not be reluctant to lower in¬‚ation, resulting in future in¬‚ation uncertainty. He further contends that uncertainty could cloud economic decisions, reducing economic growth. Ball (1992) formalizes this relationship in a model of asymmetric information between policy makers and the public. Conversely, Cukierman and Meltzer (1986) suggest the possibility that in¬‚ation uncertainty could cause higher in¬‚ation as the central bank takes advantage of an uncertain environment to produce in¬‚ation surprises to stimulate the economy. This relation may further encourage a central bank’s in¬‚ationary bias, leading to lower long-run economic growth. And inflation can impose hidden tax increases, as inflated earnings push taxpayers into higher income tax rates unless the tax brackets are indexed to inflation. With high inflation, purchasing power is redistributed from those on fixed nominal incomes, such as some pensioners whose pensions are not indexed to the price level, towards those with variable incomes whose earnings may better keep pace with the inflation. This redistribution of purchasing power will also occur between international trading partners. Where fixed exchange rates are imposed, higher inflation in one economy than another will cause the first economy’s exports to become more expensive and affect the balance of trade. There can also be negative impacts to trade from an increased instability in currency exchange prices caused by unpredictable inflation.

1.2.2 Exchange Rate Fluctuation Depreciation lowers the foreign currency price of exports and should increase export quantity. Export revenue in domestic currency, however, may not rise and can fall. Perfectly inelastic foreign import demand would imply no increase in export revenue. If there is high import content in export production, depreciation could result in higher price of exports. With appreciation, exporters might price to market and lower their domestic currency price to maintain market share. Exporters may also actively hedge in option markets to avoid exchange rate effects. Foreign exchange risk refers to the risk faced due to fluctuating exchange rates. For example, a Malaysian trader who exports palm oil to India for future payments in Rupees is faced with the risk of Rupees depreciating against the Ringgit when the payment is made. This is because if Rupee depreciates, a lesser amount of Ringgit will be received when the Rupees are exchanged for Ringgit. Such risks are quite common in international trade and finance. A significant number of international investment, trade and finance dealings are shelved due to the unwillingness of parties concerned to bear foreign exchange risk. Hence it is imperative for businesses to manage this foreign exchange risk so that they may concentrate on what they are good at and eliminate or minimize a risk that is not their trade. Even if there were a positive effect of depreciation on export revenue, associated exchange risk might discourage exporters and mitigate the positive effect. Exchange risk has become an issue since the collapse of fixed exchange rates in the early 1970s but there is no consensus regarding its impact on export revenue. Exchange risk could theoretically lower export revenue due to profit risk as developed by Ethier (1973). De Grauwe (1988) suggests, however, that exporters might increase volume to offset revenue loss. On the other side of the transactions, importers may seek other sources when facing exchange risk. Broll and Eckwert (1999) note the return on an option to export should increase along with risk. Exchange risk could also alter the currency inventory practices of exporters and importers.

The 1997 East Asian currency crisis made apparent how vulnerable currencies can be. The speculative attacks on the Ringgit almost devastated the economy if not for the quick and bold counter actions taken by the Malaysian government, particularly in checking the offshore Ringgit transactions. It also became apparent the need for firms to manage foreign exchange risk. Many individuals, firms and businesses found themselves helpless in the wake of drastic exchange rate movements. Malaysia being among the most open countries in the world in terms of international trade reflects the degree of Malaysia’s exposure to foreign exchange risk.

1.2.3 Gold As a hedge The asset’s returns, which offset the effect of inflation, are termed as hedge against inflation. Different assets play a role as hedging against inflation Bodie (1976). For example society hedge against inflation if it eliminate or reduces the possibility of the real rate of return falling below the specified level or as an asset whose real return is independent of the inflation rate. One of the properties of hedging is to reduce the variability of future wealth Bonnekamp (1978). Hedges is not important for individual only who want to maintain the purchasing power but also important for institutional investors Bodie (1979).

Real investors are concerned about the real values of their assets because their liabilities are linked to inflation. Various physical and financial assets are used as hedging inflation. These are foreign currency, gold, real estate, saving deposits, silver, stock prices, treasury bills and government securities. In the International Conference on Gold Dinar Economy 2007, Tun Dr Mahathir noted that in the case of paper people will have risk in losing their value and also purchasing power. He stressed back that only Gold Dinar really has a value in it. Using monthly gold price data from 1976 until 1999, and cointegration regression techniques, Ghosh et al. (2004) investigate the contradiction between short-run and long-run movements in the gold price and find that the gold price rises over time at the general rate of inflation and hence is an effective hedge against inflation under a set of conditions. When it comes to inflation, the value of gold is considered to be preserved, for its price will increase along with the rise in the general level of prices. In other words, it is believed that changes in the price of gold reflect inflationary pressure. However, the issue is not whether gold hedges against inflation, but how well it does. Each country has its own economic conditions or characteristics. Therefore, this study applies the non-linear model to examine the inflation hedging ability of gold in each country instead of the linear model, in order to find out the more adequacy results.

Having gold as money, or as the basis of the monetary system, meant linking a currency to gold at a fixed price. The behavior of prices was thus taken outside the control of government and central banks, and depended on the gold supply relative to the demand for it. In such a situation an automatic stabilizing mechanism was in place. Suppose that for some reason the price of goods rose relative to gold; this fall in the relative price of gold reduced incentives to produce gold, and also diverted some of the existing stock to non-monetary uses such as jewelries. Conversely, if the price of goods fell there was a rise in the relative price of gold, and thus a stimulus to its production. Hence, a considerable degree of price stability in terms of gold was to be expected. Crucial to this mechanism was gold being the basis of the monetary system. When gold no longer had that role, the automatic stabilizing mechanism, working from changes in the relative gold price, through changes in gold output and use, to changes in the money supply, was no longer in place. That does not mean that gold could no longer be a good store of value or protection against exchange rate change. But whether it is or not depends on different forces. It depends on whether, when currencies weaken, people switch to gold; and on when currencies strengthen; they become more confident about the value of currencies, and switch from gold. Even though gold no longer has any role in the monetary system of any major country, such behavior could still be sensible. For, as de Gaulle pointed out, gold has no nationality and is not controlled by governments. In determining the extent to which gold acts as an exchange rate hedge, it is, therefore, well worth exploring the past, to see how well gold protected against currency fluctuations. That, in itself, is of interest, and it may also be of interest in the future.

1.2.4 Inflation in Malaysia Figure 1

Low inflation and sustainable GDP growth has been one of the main features of the Malaysian economy in the last two decades. Despite its robust economic growth in 1980s and 1990s, Malaysia’s inflation rate had been relatively low by international standards. Even after the severe Asian financial crisis (1997 and 1998) and sharp depreciation of the ringgit in1997-98, Malaysia’s inflation rate has been contained at a relatively low level as shown in figure 1.In the early 1970s; Malaysia experienced a single-digit episode of inflation only 2%. During the second half of 1970s, inflation rate gradually increase to 4%. The sharp oil price increase in 1973 and 1974 was the principal reason for the escalation of world inflation in 1973-1974. Consequently, consumer prices in Malaysia began to rise and had reached to double-digit level of 10.56 % by the end of the year of 1973. In 1974, the surge in the oil price by over 230 per cent put strong fuel on inflation, and the inflation rate in Malaysia increased to its record high of 17.32%. A year later, the Malaysian economy slumped into its great recession, with a GDP growth rate of only 0.8% in 1975, compared to8.3% and 11.7% in 1973 and 1974 respectively. On the other hand, inflation rate reduced to the level of 4.5% in 1975.Malaysia experienced a second episode of high prices in 1980 and 1981, which were due mainly to external factors. Oil prices rose by 47% in 1979 and 66% in 1981. As a result, inflation in Malaysia accelerated from 3.6% in 1979 to 6.6% and 9.7% in 1980 and 1981 respectively. Since 1982, inflation rate kept decreasing and amounted to less than 1% in 1985 and 1986. The development of the Malaysian economy was at an important crossroad in 1985. The economic performance of the country had slumped into its greatest recession, with -1.1% and 1.1% growth rate recorded in 1985 and 1986 respectively. From 1990 until 2012, the rate can be said steadily revolve around 1 to 5%.

1.2.5 Exchange Rate Against USD in Malaysia Figure 2

Malaysian Ringgit (RM) was formerly known as Malaysia Dollar (M$). M$ was created in June 1967 to replace the old Sterling-link Malaysian/Straits Dollar. In year 1971, M$ was linked to Pound Sterling (â‚¤) at fixed rate of 7.4369M$/â‚¤. With floating of Sterling and dismantling of Sterling Area, Malaysia adopted US Dollar with fluctuation range for Effective Rate as intervention currency in place of Sterling in 1972. The intervention of Malaysian Central Bank was to maintain the stability in the value of domestic currency in relation to basket of foreign currencies. Due to devaluation of US Dollar in February 1973, the Official Rate of Malaysian Dollar was realigned to 2.53M$/US$, based on currency’s unchanged gold content. In 21 June 1973, Malaysia placed a controlled, floating effective rate In 1975, the Malaysian Dollar was officially changed to Ringgit (RM) and the controlled, floating effective rate was replaced. The external value of Ringgit was determined based on the weighted basket of foreign currencies of the Malaysia major trading partners.

The same exchange rate determination was sustained up till the Asian Financial Crisis 1997/98. During the crisis year, the overvalued Ringgit depreciated sharply against the US dollar by more than 40%. To stabilize the financial market, Malaysia imposed capital control and returned to fixed exchange rate that pegged to US dollar at RM3.80 in September 1998. As part of the economic recovery strategy, Malaysia has committed to export-led growth policy based on maintenance of their undervalued and pegged currencies against the USD. On July 21, 2005, Malaysia responded to China’s de-pegging announcement within an hour after the 7-year pegging. Akin to the Chinese policy, BNM allows the ringgit to operate in a managed floating system based on a basket of several major currencies. From 1973 until 1997 as before the major economic crisis started, the exchange rate between RM and USD float steadily around RM2.5 per USD. It rises greatly in 1998 to nearly RM4 per USD and been pegged to RM3.8 from then until 2005.

1.2.5 Gold Price in Malaysia Figure 3

As clear as can be seen from the figure, the overall pattern of gold price is increasing over time. It rises steadily from 1970, which started at RM110 in 1970, to RM671 in 1979. Then it has a sharp increase in 1980, to RM1333. Dropped a bit to RM877 until 1982, then gold price was steadily revolved around that price. From 2000 until 2011, gold has experienced a great increase in its price, resulted in 353% increment from 2000. This steady increment and sharp for the last decade is what interests us to see whether gold can be used to hedge both inflation and exchange rate fluctuation.

1.3 Problem Statement In the macroeconomics literature, there are numerous studies and researches conducted to validate this controversial proposition. The problem with inflation and exchange rate fluctuations are something that cannot be avoided. The best thing that any entity could do is to manage them. The problem with this is to choose the best way and the best tool to achieve it. In addition, it was found that most studies are interested on testing the hypothesis in developed countries like European countries, United States and new emerging economy like India and China. However, it is interesting to test the hypothesis in Malaysia because this association is consisted with different level of economic structure countries within the South East Asia region, therefore; indisputably require a concrete analysis to examine the differences.

Figure 4

Even though figure 3 shows stable increment in gold price and somehow offers security for investor, but it is still does not provide enough reason as to why we have to study the ability of gold as a hedging tool against inflation. As can be seen in figure 4, overall, the increment in the price of gold is rather higher than the inflation rate, meaning that the rise in price of gold is higher than the rises in the price index. So, it come to the understanding that rises in price of gold can offset the rise the price of good. This also supported by figure 5 where log of price of gold from 1970 until 2011 totally offset the consumer price index for Malaysia. But this is just the overall idea of the hypothesis. Technically and empirically, it has to be proven.

Figure 5

Figure 6

Same as to hedge exchange rate fluctuation. By seeing figure 3, one could simply conclude that gold can easily hedge exchange rate fluctuation in Malaysia. As shown in figure 6, in overall, changes in gold price is clearly offset the changes in exchange rate. According to figure 6, if gold can really hedge against exchange rate fluctuation, a lot of entities would be benefited by seeing how much difference between both changes. Producers, for example, would be making much profit if the quotation of material were quoted in gold when the RM depreciates and the price of gold is much higher than the depreciation. But, as stated above, this still have to be proven empirically.

1.4 Objective of the Study The general objective of this study is to show the hedging ability of gold. Likewise, there are two other specific objectives that may help to strengthen this study, namely;

1. To examine the hedging ability of gold against inflation rate in Malaysia,

2. To examine the hedging ability of gold against exchange rate fluctuation in Malaysia.

1.5 Significance of the study As the objective of the study is to prove the hedging ability of gold against inflation and exchange rate fluctuation in Malaysia, hence, this study definitely will give benefits to both macro and micro level of economics in Malaysia. At macro level, policy makers could warn or suggest to people to hold gold if it is proven its ability to hedge against inflation and/ exchange rate fluctuation. For example, if it is proven that gold can hedge against both, policymaker could launch campaign on suggesting household and firms to hold few gram of gold per unit of economies. The significant for that reason is remarkable as this issue is related to the macroeconomic development through the fiscal policy framework. At micro level, individuals or households could diversified their portfolio in a better and safe way as gold, if proven, could hedge against inflation and exchange rate risk. Firms that deal with international trading which are prone to the exchange rate risk could minimize their risk by holding gold if proven that it can hedge against exchange rate fluctuation. The findings of this study will benefit those who are directly and indirectly involve in the decision making process likely the politicians, economists, policy makers, firms and households. It is crucial to test the objectives of this study especially for the betterment of Malaysia’s economic growth and its people well-being.

1.6 Scope of the study In this study, we will mainly investigate the hedging ability of gold. Interestingly, to meet the objectives, we will use Yuan and Kuang (2011) models and estimate the selected model using the Threshold Vector Auto Regressive test approach to capture the long run and short run effects of the stated objectives. Although there are various techniques to empirically analyze the objectives and estimate the models, however this study only limited to the proposed undertakings.

1.7 Organization of the study The study is separated into 5 main chapters. Next, the Literature Review will contain some shortcomings of the hypothesis, the frameworks and empirical evidences on the hedging ability of gold against inflation and exchange rate fluctuation. In the third chapter, Methodology and Estimation Procedures, we will disclose the models that will be used in estimating the objectives and state the analytical and diagnostic procedures. After estimate the model, we will show the results and discuss them in the fourth chapter, Results and Discussion. Here, we will display all the analysis that we obtain in proper tables and elaborate further the results and necessarily relate the findings with previous studies. Finally, we will conclude the findings in the last chapter, Summary, Conclusion and Recommendation. We will make general conclusion based on the findings, state few implications on the policy and recommend some improvements for the betterment of future study.

## Adf And Kpss Tests Economics Essay

Before presenting the results, it is importance to check for the time series data existence of stationary property of each variable whether the variables under consideration are stationary in level form. It is important to ensure that the variables used in the regression are not subject to spurious correlation. We are using unit root tests to investigate the stationary status of each variable by Augmented Dickey-Fuller (ADF) and Kwiatkowski, Phillips, Schmidt and Shin (KPSS). These two tests will test for with and without time trend at level form and indicate lag lengths based on the Akaike Information Criterion (AIC). Table 1 is the result for ADF while Table 2 is KPSS.

Table 1 show that CPI and RGFCF, both are significant at 5% level at the level form without linear trend; significant at 1% level for the level form with linear trend. RGDP is significant at 10% level for the level form without trend and significant at 5% level with linear trend. While for the POP, it cannot significant at level form without linear trend. However, POP significant at 5% level for the level form with linear trend.

Table 2 shows that all variables are significant at 1% level for both with and without linear trend at level form. Which is explained that the t-statistic have sufficient evidence do not reject the null hypothesis at level form in both with and without linear trend. These mean that the series are stationary at level form.

In short, all the variables show stationary at the level form, I(0). Which it obeys the theory or concept of stationarity of financial data where it is predicted to be non-stationary at level form and stationary after the first difference. Thus this allows us to proceed to the cointegration tests.

Variables Augmented Dickey-Fuller level Constant without linear trend Constant with linear trend CPI -3.042517 ** (3) -6.276123 *** (7) RGFCF -3.393829 ** (1) -12.31312 *** (1) RGDP -2.632101 * (4) -4.034734 ** (3) POP -0.659324 (1) -4.187427 ** (6) Table 1: Results of Unit Root tests with ADF Notes: figures within parentheses indicate lag lengths. Lag length for ADF tests have been decided on the basis of Akaike Information Criterion (AIC) (Akaike, 1974). The ADF tests are based on the null hypothesis of unit roots. ***, **, and * indicate significant at 1%, 5% and 10% levels respectively, based on the critical t statistics as computed by Mackinnon (1996).

Table 2: Results of Unit Roots tests with KPSS Variables

Kwiatkowski, Phillips, Schmidt and Shin (KPSS)

level

Constant without linear trend

Constant with linear trend

CPI

0.170339 ***

0.172165 ***

RGFCF

0.686537 ***

0.086532 ***

RGDP

0.120369 ***

0.065365 ***

POP

0.491314 ***

0.117854 ***

Notes: figures within parentheses indicate lag lengths. Lag length for ADF tests have been decided on the basis of Kwiatkowski, Phillips, Schmidt and Shin (KPSS). The KPSS tests are based on the null hypothesis of unit roots. ***, **, and * indicate significant at 1%, 5% and 10% levels respectively, based on the critical t statistics as computed by Mackinnon (1996).

4.2 Granger Causality Tests After identify ADP and KPSS test to ensure all the variables are stationary, we used Granger Causality test to estimate the linear causation between inflation and economic growth and the results are shown in Table 3.

Table 3: Pair wise Granger Causality Tests

Sample: 1960 – 2005

Lags: 1

Null Hypothesis:

Obs

F-Statistic

Prob.

CPI does not Granger Cause RGDP

44

4.68436

0.0363

RGDP does not Granger Cause CPI

21.2996

4.E-05

Both the null hypothesis is rejected at 1-5 percent level of significance, which implies that inflation rate does Granger Causality real GDP growth and real GDP growth does Granger Causality inflation rate. This test statistic shows that the causality between two variables is bi-directed. The variables are co-integrated because there is a long-run relationship between inflation rate and real GDP growth. We further applied the Akaike Information Criterion (AIC) and Schwarz Information Criterion (SIC) to define the lag length used for inflation rate and real GDP growth. The result is important to identify the choice of dependent and independent variable for the threshold model specification. In addition, the inflation rate is causing growth at lag one (lag=1) for the period from 1961 to 2005. Hence, we generate the equation by adding lag one for the inflation rate in the estimation model.

4.3 Threshold Model estimation We run the data using ordinary least squares (OLS) econometric technique and the inflation rate are kept at lag one after estimating for Granger Causality test. The optimal threshold level is the minimum value of RSS (residual sum of squares) as shown in table 4. Table 4 also illustrates the result of t-statistics and P-values for the estimation equation. From the result, it shows that both 3 and 5 percent inflation level also get the minimum value of RSS. This means that the optimal level of threshold is about 3 to 5 percent. In order to find out the threshold level of inflation, we further specified the value of k from the range of 3 to 5 percent into percentage point and the estimation value is shown as Table 5. The results in Table 5 indicated that 3.1 percent inflation level is the optimal level of threshold in which the value of k is the one with minimizes the residual sum of squares (RSS).

4.3 .1 Estimation of OLS regression

Table 4: Estimation of OLS regression at k = 1 to 5%

(Dependent Variable: real GDP growth)

K (%)

Variable

Coefficient

Std. Error

t-statistics

P- value

RSS

1

Inflation

Inflation (-1)

(inf>1)*(inf-1)

Investment growth

Population growth

C

1.538332

-0.564809

-0.976010

0.170379

-1.939174

9.314812

0.583956

0.298513

0.663202

0.035571

0.444884

1.162268

2.634331

-1.892076

-1.471665

4.789804

-4.358832

8.014344

0.0121

0.0661

0.0000

0.0001

0.1493

0.0000

7.223265

2

Inflation

Inflation (-1)

(inf>2)*(inf-2)

Investment growth

Population growth

C

0.969290

-0.582696

-0.420789

0.175398

-1.982495

9.648625

0.336110

0.302481

0.337848

0.040910

0.459113

1.116383

2.883850

-1.926386

-1.245497

4.287397

-4.318101

8.642757

0.0064

0.0616

0.2206

0.0001

0.0001

0.0000

7.335498

3

Inflation

Inflation (-1)

(inf>3)*(inf-3)

Investment growth

Population growth

C

0.826579

-0.599289

-0.359792

0.188432

-2.344833

10.63177

0.304288

0.283725

0.202986

0.039605

0.522635

1.144140

2.716439

-2.112214

-1.772499

4.757815

-4.486557

9.292370

0.0099

0.0413

0.0843

0.0000

0.0001

0.0000

7.051917

4

Inflation

Inflation (-1)

(inf>4)*(inf-4)

Investment growth

Population growth

C

0.811575

-0.749978

0.113362

0.128502

-1.700261

9.726264

0.315729

0.291090

0.257392

0.038080

0.532298

1.201991

2.570482

-2.576447

0.440428

3.374569

-3.194193

8.091795

0.0142

0.0140

0.6621

0.0017

0.0028

0.0000

7.596177

5

Inflation

Inflation (-1)

(inf>5)*(inf-5)

Investment growth

Population growth

C

0.826574

-0.789422

0.821631

0.110625

-1.472938

9.327557

0.304094

0.277041

0.459772

0.032438

0.475637

1.123008

2.718158

-2.849476

1.787040

3.410344

-3.096766

8.305869

0.0098

0.0070

0.0819

0.0016

0.0037

0.0000

7.043056

*(inf>k)*(inf-k) denotes the dummy variable

Table 5: Estimation of OLS regression at k = 3 to 5%

(Dependent Variable: real GDP growth)

K (%)

Variable

Coefficient

Std. Error

t-statistics

P- value

RSS

3

Inflation

Inflation (-1)

(inf>3)*(inf-3)

Investment growth

Population growth

C

0.826579

-0.599289

-0.359792

0.188432

-2.344833

10.63177

0.304288

0.283725

0.202986

0.039605

0.522635

1.144140

2.716439

-2.112214

-1.772499

4.757815

-4.486557

9.292370

0.0099

0.0413

0.0843

0.0000

0.0001

0.0000

7.051917

3.1

Inflation

Inflation (-1)

(inf>3.1)*(inf-3.1)

Investment growth

Population growth

C

0.819686

-0.599990

-0.365668

0.189027

-2.365807

10.68969

0.303305

0.282258

0.198960

0.039084

0.522171

1.147435

2.702515

-2.125684

-1.837890

4.836480

-4.530714

9.316161

0.0102

0.0401

0.0739

0.0000

0.0001

0.0000

7.011681

3.2

Inflation

Inflation (-1)

(inf>3.2)*(inf-3.2)

Investment growth

Population growth

C

0.813290

-0.609849

-0.346033

0.185869

-2.341698

10.67837

0.304849

0.283432

0.201610

0.039245

0.527866

1.161110

2.667843

-2.151662

-1.716349

4.736069

-4.436160

9.196697

0.0112

0.0378

0.0942

0.0000

0.0001

0.0000

7.085655

3.3

Inflation

Inflation (-1)

(inf>3.3)*(inf-3.3)

Investment growth

Population growth

C

0.808974

-0.630219

-0.292763

0.177525

-2.253800

10.55958

0.308498

0.286697

0.208103

0.039526

0.534224

1.177629

2.622301

-2.198207

-1.406819

4.491332

-4.218832

8.966817

0.0125

0.0341

0.1676

0.0001

0.0001

0.0000

7.256989

3.4

Inflation

Inflation (-1)

(inf>3.4)*(inf-3.4)

Investment growth

Population growth

C

0.805122

-0.650546

-0.232901

0.168643

-2.156660

10.42119

0.311662

0.289750

0.214940

0.039689

0.538534

1.190453

2.583316

-2.245200

-1.083561

4.249078

-4.004684

8.753971

0.0138

0.0307

0.2854

0.0001

0.0003

0.0000

7.406122

3.6

Inflation

Inflation (-1)

(inf>3.6)*(inf-3.6)

Investment growth

Population growth

C

0.807381

-0.691159

-0.122278

0.153417

-1.988913

10.17585

0.315266

0.291690

0.230561

0.039680

0.543951

1.207686

2.560950

-2.369499

-0.530351

3.866320

-3.656419

8.425900

0.0145

0.0230

0.5990

0.0004

0.0008

0.0000

7.578855

3.7

Inflation

Inflation (-1)

(inf>3.7)*(inf-3.7)

Investment growth

Population growth

C

0.807354

-0.706396

-0.069755

0.146940

-1.915185

10.06400

0.316081

0.292153

0.238509

0.039497

0.543696

1.210660

2.554263

-2.417897

-0.292465

3.720337

-3.522527

8.312827

0.0148

0.0205

0.7715

0.0006

0.0011

0.0000

7.617805

3.8

Inflation

Inflation (-1)

(inf>3.8)*(inf-3.8)

Investment growth

Population growth

C

0.807750

-0.720644

-0.015039

0.140795

-1.844324

9.954601

0.316440

0.292325

0.246159

0.039191

0.541739

1.210691

2.552617

-2.465216

-0.061095

3.592527

-3.404451

8.222247

0.0148

0.0183

0.9516

0.0009

0.0016

0.0000

7.634203

3.9

Inflation

Inflation (-1)

(inf>3.9)*(inf-3.9)

Investment growth

Population growth

C

0.809120

-0.735339

0.046430

0.134571

-1.771822

9.840773

0.316347

0.291994

0.252579

0.038722

0.537991

1.207920

2.557700

-2.518335

0.183822

3.475323

-3.293408

8.146872

0.0146

0.0161

0.8551

0.0013

0.0021

0.0000

7.628169

4

Inflation

Inflation (-1)

(inf>4)*(inf-4)

Investment growth

Population growth

C

0.811575

-0.749978

0.113362

0.128502

-1.700261

9.726264

0.315729

0.291090

0.257392

0.038080

0.532298

1.201991

2.570482

-2.576447

0.440428

3.374569

-3.194193

8.091795

0.0142

0.0140

0.6621

0.0017

0.0028

0.0000

7.596177

4.1

Inflation

Inflation (-1)

(inf>4.1)*(inf-4.1)

Investment growth

Population growth

C

0.813492

-0.760287

0.170993

0.124043

-1.646754

9.638877

0.314833

0.289823

0.265606

0.037537

0.527469

1.196228

2.583886

-2.623281

0.643784

3.304522

3.121994

8.057725

0.0137

0.0125

0.5236

0.0021

0.0034

0.0000

7.552578

4.2

Inflation

Inflation (-1)

(inf>4.2)*(inf-4.2)

Investment growth

Population growth

C

0.814782

-0.767124

0.220848

0.120950

-1.609142

9.576530

0.313960

0.288694

0.278565

0.037145

0.523851

1.191449

2.595175

-2.657221

0.792806

3.256148

-3.071754

8.037718

0.0134

0.0115

0.4328

0.0024

0.0039

0.0000

7.510721

4.3

Inflation

Inflation (-1)

(inf>4.3)*(inf-4.3)

Investment growth

Population growth

C

0.816150

-0.773634

0.278128

0.117888

-1.571341

9.512792

0.312816

0.287248

0.291258

0.036629

0.518916

1.184728

2.609045

-2.693259

0.954918

3.218462

-3.028123

8.029515

0.0129

0.0105

0.3457

0.0026

0.0044

0.0000

7.456034

4.4

Inflation

Inflation (-1)

(inf>4.4)*(inf-4.4)

Investment growth

Population growth

C

0.817551

-0.779375

0.342362

0.115024

-1.535224

9.450467

0.311364

0.285454

0.303176

0.035972

0.512475

1.175766

2.625705

-2.730304

1.129254

3.197581

-2.995704

8.037712

0.0124

0.0095

0.2659

0.0028

0.0048

0.0000

7.387056

4.5

Inflation

Inflation (-1)

(inf>4.5)*(inf-4.5)

Investment growth

Population growth

C

0.819140

-0.783980

0.411602

0.112607

-1.503814

9.394421

0.309627

0.283369

0.314048

0.035185

0.504553

1.164552

2.645568

-2.766638

1.310632

3.200417

-2.980485

8.066984

0.0118

0.0087

0.1978

0.0028

0.0050

0.0000

7.304748

4.6

Inflation

Inflation (-1)

(inf>4.6)*(inf-4.6)

Investment growth

Population growth

C

0.826182

-0.791957

0.475658

0.111246

-1.488082

9.365990

0.308440

0.282444

0.331298

0.034615

0.497872

1.154816

2.678578

-2.803944

1.435742

3.213786

-2.988884

8.110371

0.0109

0.0079

0.1593

0.0027

0.0049

0.0000

7.242096

4.7

Inflation

Inflation (-1)

(inf>4.7)*(inf-4.7)

Investment growth

Population growth

C

0.831194

-0.797041

0.545831

0.110370

-1.477221

9.344919

0.307201

0.281199

0.351402

0.034007

0.491151

1.145051

2.705703

-2.834436

1.553297

3.245510

-3.007671

8.161138

0.0101

0.0073

0.1286

0.0024

0.0047

0.0000

7.179129

4.8

Inflation

Inflation (-1)

(inf>4.8)*(inf-4.8)

Investment growth

Population growth

C

0.827864

-0.793620

0.626005

0.110009

-1.469475

9.328254

0.305943

0.279437

0.380078

0.033468

0.486068

1.137663

2.705945

-2.840064

1.647046

3.286991

-3.023188

8.199489

0.0101

0.0072

0.1078

0.0022

0.0045

0.0000

7.126222

4.9

Inflation

Inflation (-1)

(inf>4.9)*(inf-4.9)

Investment growth

Population growth

C

0.823792

-0.788270

0.715461

0.110231

-1.468281

9.321785

0.304688

0.277659

0.411601

0.032828

0.479930

1.129002

2.703724

-2.838987

1.738240

3.357893

-3.059364

8.256657

0.0102

0.0072

0.0903

0.0018

0.0041

0.0000

7.072592

5

Inflation

Inflation (-1)

(inf>5)*(inf-5)

Investment growth

Population growth

C

0.826574

-0.789422

0.821631

0.110625

-1.472938

9.327557

0.304094

0.277041

0.459772

0.032438

0.475637

1.123008

2.718158

-2.849476

1.787040

3.410344

-3.096766

8.305869

0.0098

0.0070

0.0819

0.0016

0.0037

0.0000

7.043056

*(inf>k)*(inf-k) denotes the dummy variable

The estimation outputs for the model are generated as shown below:

GROWTH t = 10.6897 0.8197INF t – 0.56INF t -1- 0.3657DUMMY

– 2.3658POP t 0.189INV t

t-stat = (9.3162) (2.7025) (-2.1257) (-1.8379)

(-4.5307) (4.8365)

R-squared = 0.7391

F-stats = 21.5272 ; Prob (F-statistic) = 0.0000

S.E. of regression = 0.4296; Mean dependent variable = 6.7262

Ceteris paribus, equals to 0.8197 means that when inflation rate rises by 1 percent, real GDP growth rate will increase by 0.82 percent on average. is 0.56, which indicates that when previous year inflation rate rises by 1 percent, the real GDP growth rate will decrease by 0.56 percent on average. is – 0.3657, indicates that when the threshold level of inflation increases by 1 percent, the real GDP growth rate will decrease by 0.37 percent on average. is -2.3658, indicating that when population growth increases by 1 percent, the real GDP growth rate will decrease by 2.37 percent on average. is 0.189 means that for every 1 percent increase in real investment, the real GDP growth rate will be increased by 0.19 percent on average.

4.3.2 Test of significance Table 6

T-test:

Coefficient β1 (inf) β2 (inf(–1)) β3 dummy β4 pop β5 inv Exp. Sign positive

negative

negative

negative

positive

t-stat 2.7025

-2.1257

-1.8379

-4.5307

4.8365

P-value 0.0102**

0.0401**

0.0739*

0.0001***

0.0000***

Decision Significant

Significant

Significant

Significant

Significant

The T- tests are based on the null hypothesis. ***, **, and * indicate significant at 1%, 5% and 10% levels respectively, based on the critical t statistics as computed by Mackinnon (1996).

H0: βi = 0

H1: βi ≠ 0, i = 1, 2, 3, 4, 5

Decision rule: Reject H0 if P-value is smaller than α = 0.1, otherwise do not reject H0.

Conclusion: There is sufficient evidence to conclude that all variables are significantly affecting real GDP at α = 0.1.

F-test:

H0: β1 = β2 = β3 = β4= β5 = 0;

H1: At least one β is ≠ 0

Decision rule: Reject H0 if Prob(F-stat) smaller than α = 0.1, otherwise do not reject H0

Decision: Since Prob (F-stat) is 0.0000 which is smaller than 0.1, thus we reject H0

Conclusion: There is sufficient evidence to prove that there is at least one of the explanatory variables is significantly affecting the economic growth in Malaysia.

Goodness of fit:

= 0.7391, which indicates that there is 73.91 percent of the variation in real GDP of Malaysia that can be explained by the variations of inflation rate, previous year inflation rate, threshold level of inflation, population growth and real investment.

Standard error-to-mean ratio:

Standard error-to-mean ratio =(S.E. of regression / Mean dependent variable) *100%

= (0.4296/ 6.7262) *100%

= 6.39%

The standard error of regression is 6.39%, which is considered very small, indicates

that the estimates value is close to the true value. Hence, this model is a good fit.

4.4 Diagnostic Checking We show the optimal level of inflation for the diagnostic checking and it illustrated in Table 6. For diagnostic testing, the problem such as multicolinearity, autocorrelation, heteroskedasticity, model misspecification error and normality test for residual are included. The result is shown as below:

Table 6: Diagnostic tests for equation at k=3.1%

Test

Hypothesis

Statistics

Result

Multicollinearity

VIF= 1/ (1-R2) = 1/ (1-0.4838) =1.9372

Not serious multi. problem

Autocorrelation

H0: There is no autocorrelation problem

Prob.Chi-Square(2) = 0.0000

reject H0

H1: There is autocorrelation problem

Heteroskedasti

-city

H0: There is no heteroskedasticity problem

Prob. F(1,41) = 0.0000

reject H0

H1: There is heteroskedasticity problem

Misspecification test

H0: There is no misspecification error

Prob. F(1,37) = 0.7274

Do not reject H0

H1: There is misspecification error

Note: ** denote significant level at 5percent

Normally distribution:

Figure 1:

For normality test for residual, Figure 1 shows that the P-value of JB-stat test is 0.423364, which is higher than 5% significant level. Thus we do not reject null hypothesis at 5% significant level and conclude that the residual are normally distributed.

For multicollinearity test, we perform regression analysis for the highly correlated pair of independent variables to get the R squared and calculate the VIF. Table 7 is the correlation analysis for every pair of independent variables.

Table 7: Correlation checking

RGDP

CPI

RGFCF

POP

_RGDP

1

0.529119

0.517047

-0.07067

CPI

0.529119

1

0.108155

-0.39804

RGFCF

0.517047

0.108155

1

0.695556 _POP

-0.07067

-0.39804

0.695556

1

From the table above, the result shows that population and real investment are highly correlated which is about 0.695556.

POP = 2.1984 0.04063RGFCF

R-squared = 0.4838

VIF(Variance Inflation Factor )= 1.9372

VIF is 1.9372 which is less than 10, this claim that there is not a serious multicollinearity problem between Population and real investment. Hence, we can leave the model alone if the VIF is not so serious and t-stat is statistically significant.

We use Breusch-Godfrey Serial Correlation LM Test to examine the existence of autocorrelation. From the result showed above, the P-value of the Chi-square test is 0.0000 which is smaller than 5% significant level. Thus we reject null hypothesis at 5% significant level since there is sufficient evidence to conclude that there is first order autocorrelation problem in the model.

The motive of running the ARCH Test is to examine the existence of heteroscedasticity. From the result showed above, the P-value of F-stat test is 0.0000 which is smaller than 5% significant level. Hence, we reject null hypothesis at 5% significant level since there is sufficient evidence to conclude that it is heteroskedasticity problem in the model.

The purpose for Ramsey Reset Test is to test the misspecification error in the model. Based on the output above, the P-value of F-stat test is 0.7274 and it is more than 5% significant level. Therefore, we do not reject null hypothesis at 5% significant level since there is insufficient evidence to prove that there is misspecification error in the model.

The results above indicate that there is an autocorrelation and heteroskedasticity problem take place in the estimated equation. Therefore, we applied White’s procedure to solve this problem and the result is shows in Table 8. We comparing the output with regular OLS output to check whether heteroskedasticity is a serious problem in the model. The White’s Heteroscedasticity-Consistent Variance and standard errors, also known as robust standard errors can be implemented so as to asymptotically valid statistical illation can be made about the true parameter values (Gujarti). Furthermore, we examined White’s Heteroscedasticity-Consistent Variance and standard errors along with the OLS variances and standard errors.

Table 8: White Test at k=3.1%

K (%)

Variable

Coefficient

Std. Error

t-statistics

P- value

RSS

3.1

Inflation

Inflation (-1)

(inf>1)*(inf-1)

Investment growth

Population growth

C

0.819686

-0.599990

-0.365668

0.189027

-2.365807

10.68969

0.331024

0.307544

0.192164

0.029116

0.342021

0.677976

2.476211

-1.950911

-1.902896

6.492182

-6.917141

15.76707

0.0178

0.0585

0.0647

0.0000

0.0000

0.0000

7.011681

GROWTH t = 10.6897 0.8197INF t – 0.56INF t -1- 0.3657DUMMY

– 2.3658POP t 0.189INV t

OLS se = (1.1474) (0.3303) (0.2823) (0.199)

(0.5222) (0.039)

t-stat = (9.3162) (2.7025)

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