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The Role Of Stock Market In The Economy Finance Essay

Many observers believe that stock market plays its role in economy as barometer; it has traditionally been viewed as an indicator or predictor of the economy. Many believe that large decrease in stock prices is reflective of future recession, whereas increase in the stock prices suggests the economic growth which means stock market presents itself as an indicator of economic activity.

Another relationship focuses on the possible impact of stock market is on demand, specifically through consumption and investment. The impact generates when people see decline in share prices. If the decline is significant it will directly and strongly affect the financial position. They might be anyone, small investors or giants. And if they are losing their wealth on stocks then they will be more afraid to spend money; and this behavior leads to decrease in consumer prices.

Changes in the stock market, may cause of fluctuation in macroeconomic variables, e.g. Increase or decrease the consumption expenditure or Investment spending, Gross domestic Product (GDP) and the effect of stock markets development on employment. Higher stock prices can stimulate consumption spending by increasing the wealth of the consumers and higher stock prices can also stimulate investment spending by enabling firms to raise their funds more effectively by issuing Initial Public offering (IPO). Decreasing in share prices can creates problems on firm’s ability to increase its financing from stock market. Those companies who think to raise their equity through Initial public offering (IPO); will face much difficulty because of bad performance of stock market and investors will not ready to invest when trend of stock market is on decreasing side.

Stock markets can also affect investor’s confidence. As discussed earlier many believe that movement in stock prices will reflect economic conditions. E.g. recent decline in stock prices of Karachi Stock Exchange (KSE) indicates a recession in our economy. Bad news regarding decreasing stock prices is one of the major psychological impacts that discourage investors from spending. Often people who ready to take risk and purchase shares are ready to lose wealth; and their spending independent of stock prices because they really don’t care of short term losses.

Many researchers include Pakistanis were focused to check the relationship of stock with major economic variables like Gross Domestic Product (GDP), Gross National Product (GNP), Investments, unemployment and Inflation (CPI) in recent years. Our study is distinctive in a way that we use to test stock with imports and exports of Goods and Services of Pakistan.

It is very important to know about Karachi Stock Exchange’s (KSE) role as a leading indicator of Pakistan imports and exports. Because a large number of experts here in Pakistan criticize stock market due to its irregularity by rumors which destroy its role as a predictor to economy. A number of times here in Pakistan it happens when economic figures shows upward trend but the news coming from stock market was horrible especially for small investors.

Stock market’s of Pakistan sometimes predict well about the future of economy, as we have seen in 2007, the decrease in stock prices leads to a recession in economy.

But in 2005, the crash in Karachi stock market didn't able to hit economic variables, and generated "False Signals" about the economy, so therefore, how should we rely on stock market as predictor / Indicator?

The main reason to investigate about stock’s role in economy is that, fluctuations in stock prices/markets may not have appropriate impact on economic variables. Share prices can rise/fall without creating impact on economy or by economy.

1.2 Problem Statement

Is the stock market reliable enough to predict upcoming movements in economy? And how stock market plays its significant role in the context of Pakistan’s economy?

1.3 Hypotheses

H1. Stock market is a significant predictor of economy because the movement in Stock Prices precedes fluctuations in Imports activities.

H2. Stock market is a significant predictor of economy because the movement in Stock Prices precedes fluctuations in Exports activities.

1.4 Outline of the study

Although stock market is a leading indicator but this does not means causality, since both the stock market and the economy are influenced by other factors, but stock market reacts faster.

The purpose of this research is to investigate the reliability of stock market which presents itself as a leading predictor of economic fluctuations worldwide. And to find any relationship between these variables, if there is any relation then the reasons that why and how movements in stock prices precede fluctuations in economic activity.

Simple linear regression model is used to test the hypotheses and to determine the strength of relationship among these variables. Two models are formed for these three variables. The independent variable is monthly stock prices of Karachi Stock Exchange (KSE), and monthly exports and imports of Goods and services of Pakistan is the dependent variable. Two new independent variables are added to improve the results. These are Lag_1 Exports and Lag_1 Imports.

CHAPTER 2: LITRATURE REVIEW

Relationship between stock market and macro economic variables, focus on the relationship of stock prices with consumption expenditure, investment spending and economic activity. The economic activity generally measure by Gross Domestic Product (GDP) and/or Index of Industrial production.

Relationship between stock prices and consumption expenditures which based on the life cycle theory developed by Ando and Modigliani (1963), states that individuals base their consumption decision on their expected lifetime wealth. Part of their wealth may be held in the form of stocks linking stock price changes to changes in consumption expenditure. Thus, an increase in stock prices will increase the expected wealth, which, in turn, will increase the consumption expenditures, suggesting the direction of causality from stock prices to consumption expenditures. On the other hand, an increase in consumption expenditures may result in an increase in the corporate sector’s earnings, which will result in higher stock prices, implying causality from consumption expenditures to stock prices.

The relationship between stock prices and investment spending which based on the q theory of Tobin (1969), where q is the ratio of total market value of firms to the replacement cost of their existing capital stock at current prices. According to the theory, the firms would increase their capital stocks if q is greater than one, implying that the market value of firms is expected to rise by more than the cost of additional physical capital. Thus an increase in stock prices will result in an increase in the market value of firms, implying that firms would increase their capital stocks reflecting an increase in investment spending.

Another link, though less direct, between stock prices and investment spending is based on the neoclassical or cost-of-capital model. The model assumes that firms first determine the desired stock of real capital on the basis of prices of labor, capital, and expected sales and then determine the rate of investment depending on how fast they wish to reach the desired capital stock in the face of significant adjustment cost. Thus, the expected changes in sales and planned output are the major factors affecting investments. However, as noted by Bosworth (1975), if higher earnings are implied by higher expected output that increases stock prices, then the market valuation model implicitly accounts for the effect of expected output. Finally, the relationship between stock prices and economic activity is investigated to examine whether the stock market leads or lags economic activity.

Moreover, the relationship of stock prices with the components of aggregate demand, consumption, and investment sometimes provide conflicting results, causing an ambiguity concerning the direction of causality between stock price changes and macro variables. As mentioned above, the economic activity is generally measured by GDP and/or IIP.

Husain (2001) proposes that Pakistani stock markets are not that much developed to play their role in influencing on aggregate demand. The lifecycle hypothesis and Tobin’s q theory, which provide the basis of linkages between stock prices and consumption and investment expenditures respectively, do not seem to be valid in Pakistan.

Another important but most worrying feature of Pakistani stock markets is that, they cannot be characterized as the leading indicator of economic activity. A study clearly indicates that it lags economic activity. It can be said that individuals, institutions, and government should be aware of speculative bubbles. In the absence of other strong economic indicators, shooting up of stock prices should be dealt with care.

Regardless of Pakistani market, International economies also do not have that much influence by stock prices.

Atesoglu (2002) also mentioned in that fluctuations in stock prices should not have an appreciable impact on the economy should not however rule out the possibility that favorable developments in stock prices can trigger a pessimistic or an optimistic economic development and alter long-term expectations. Same in the case of Pakistan because our stock market is highly speculative market and can’t have much impact on economy.

Park (1997) defines the same effect of macroeconomic variables on stock returns. Stock’s reaction to an economic variable reflects the variable's effects on future corporate cash flows and inflation. Stock returns were found to be related most negatively with employment growth and most positively with GDP.

According to Christos (2004); there is no correlation between the current value and the past values, and therefore, the stock returns and inflation are characterized as independent factors in Greece market. And hence, his research also finds evidence of no long-run relationship between the two variables. As discussed earlier stock market in Pakistan is not that much developed yet to plays its role in economy, so it needs improvements to match macro economic variables.

Jain (1988) discuss that there is a strong impact of economic news/Information on stock prices, which reflected in a reactively short period of 1 hour. The advantage to use hourly stock returns data in research projects investigating the effects of announcements of economic variables. Same situation we have seen many times in Pakistan, here stock market responds quickly too because of economic announcements e.g. Monetary policy. That carries interest rates which directly relates to the stock markets and the major players of market are also keen to know about new developments in such macro economic variables.

Studies made by Sara & Levine (1996) investigate about the role of financial system in economic growth. In this regard the research found that the predetermined component of stock market development is positively associated with economic growth. The cross country growth regression involves a strong link between stock market development and economic growth.

Another study made by Levine (1997) uses existing theory of goldsmith (1969) which acknowledged the relationship between financial and economic development. The research suggests through analysis, which includes firm level, individual country level, industry level and broad cross country level comparisons, which explain a strong positive relationship between financial system and economic growth in long run.

Both Sara & Levine (1998) work together again to investigate weather measure of stock market liquidity, size, violability and integration with world capital markets are robustly associated with economic variables such as current and future rate of economic growth, productivity improvements, capital accumulation and saving rates of 47 different countries.

The study gives empirical proof on the most important theoretical debates about the association between stock markets and long term economic growth.

The research studied the empirical relationship between various measures of stock development, banking development and long rum economic growth. And find that after controlling for many factors associated with growth, stock market liquidity and banking development are both positively and robustly correlated with at the same time and future’s economic growth rate, productivity and capital accumulation.

Research done by Borja & Braun in (2005) finds the performance of companies during recessions. The paper shows that those companies which are more dependent on external financing hits harder during recessions specially when they are located in countries with poor financial contractibility, and when their assets are less protective of financer.

Luigi & Rajan (1998) examines whether financial development facilitates economic growth by examine one rationale for such a relationship that financial development reduces the costs of external finance to firms. Specifically, research find whether industrial sectors that are relatively more in need of external finance develop disproportionately faster in countries with more- developed financial markets.

The research findings may bear on three different areas of current research. First, it suggest that financial development has a substantial supportive influence on the rate of economic growth Second, in the context of the literature on financial constraints, the research provides fresh evidence that financial market imperfections have an impact on investment and growth.

Finally, in the context of the trade literature, the findings suggest a potential explanation for the pattern of industry specialization across countries. The level of financial development can also be a factor in determining the size composition of an industry as well as its concentration.

Nagaishi (1999) find out the answer of stock markets positive role in the process of economic growth in the context of Indian market. The investigation results indicate that so far as the function of domestic savings mobilization is concerned, Indian stock market development form 1980s inwards has not played a prominent role, secondly, if there is further deregulation of the stock market to attract more foreign portfolio inflows into India, there seems to be no way to avoid similar problems as Mexico, Korea and Thailand such as more volatile movement of domestic stock prices. Lastly bank credit to the commercial sector has no positive correlation ship with indicators of stock market developments. These findings indicate that the fundamental relation between stock markets and economic growth is at least so far a fond hope in the Indian context.

Ann & Lyigun (2004) observe the relation between income in aggregate consumption growth. In high income countries, greater income inequality appears to be associated with more volatility in consumption growth; on the other hand countries having low income, high levels of inequality in income tend to be associated with less volatility. Research presents evidence that variability in real GDP growth is also related to income inequality in the same way. The results suggest that countries with low income, having high level of inequality associated with lesser fluctuations in consumption growth, and those who have high income, have more inequality seems to b associated with greater fluctuations. Some preliminary results indicate that financial development may help to explain the relationship between inequality and aggregate consumption variability.

Odedokun (1998) attempted to identify whether financial intermediation affects economic growth, and also for determining the channels by which any affects it may have on growth. Study aims at enhance the earlier ones and, especially, at suggesting a new framework for evaluating the role of financial intermediation and applying the framework to actual data for developing countries.

The findings suggests, Growth of financial aggregates in real terms have positive impacts on economic growth of developing countries. Financial aggregates in relation to overall economic activities or GDP, promotes economic growth in the low-income developing countries but has no perceptible effect in the high-income developing countries. The combined effects of financial intermediation, which are externality and intersectional factor productivity differential effects, on economic growth are significantly positive.

Liow, Ibrahim, & Huang (2005) investigate the relationship between the expected risk premiums on property stocks and having some main macroeconomic risk factors which are reflected in financial conditions and in general business at international level.

Findings suggest that predicted risk premium and conditional instabilities of the risk premium on property stocks are time unreliable and energetically linked to the conditional instabilities of main macro economics risk factors. Though there are some differences in significance, as well as path of impact in the macro economics risk factors across the property stocks.

CHAPER 3: RESEARCH METHODS

3.1 Method of Data Collection

Secondary data is the main source to collect the data for this research. We collect this data from websites of concerned variables. Also include various visits to State bank and Karachi stock exchange for maximum knowledge form experts to check the relationship between these variables.

3.2 Sample Size

The sample period used in this study covers 5-years period beginning at the start of 2005 and ending at the close of 2009. Quarterly data of 5-years is used and hence we have a size of 60 observations for each variable.

3.3 Research Model

Imports = α+β (stock prices) + έ

Exports = α+β (stock prices) + έ

3.4 Statistical technique

Regression analysis (Simple Liner Regression) is used to investigate the data. Because regression include any techniques for modeling and examining several variables, when focus is on the relationship between one dependent variable and one or more than one independent variables, regression model assumes that there is a linear or you can say “Straight Line” relationship between the dependent variable and each predictor.

CHAPTER 4: RESULTS

4.1 Findings and Interpretation of the results

4.1.1 Model Summary Imports

Table 1: Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

Durbin-Watson

1

.803a

.645

.632

378.23201

2.466

a. Predictors: (Constant), lag imp, stock

b. Dependent Variable: imports

After creating one more independent variable the model improves and Durbin Watson value reaches 2.466 which mean that the data shows positive correlation between imports and stock. R square is now explaining 64.5 to stock.

Table 2: ANOVAb

Model

Sum of Squares

Df

Mean Square

F

1

Regression

1.454E+07

2

7.272E+06

50.833

Residual

8.011E+06

56

143059.454

 

Total

2.256E+07

58

 

 

a. Predictors: (Constant), lag imp, stock

b. Dependent Variable: imports

ANOVA table shows that significant value is less than 0.05 mean which means model is perfect and regression is required.

Table: 3 Coefficientsa

Unstandardized

Coefficients

T

Collinearity statistics

Model

B

Std. Error

Tolerance

1 (Constant)

530.801

262.050

2.023

Stock

.053

.022

2.448

.785

Lag_imp

.660

.088

7.534

.785

a. Dependent Variable: imports

Coefficient table shows that significant value of stock is less than 0.05, which means stock has a significant impact on import.

4.1.2 Model Summary Exports

Table 4: Model Summaryb

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.512a

.262

.236

236.47400

a. Predictors: (Constant), LAG_EXPO, stock

b. Dependent Variable: export

Creating one more independent variable the model improves and Durbin Watson value reaches 2.224 which mean that there is a positive correlation between Exports and Stock Prices. R square is now improves and it shows Exports are now explaining 26.2 to Stock Prices.

Table 5: ANOVAb

Model

Sum of Squares

df

Mean Square

F

1

Regression

1111975.169

2

555987.584

9.943

Residual

3131517.339

56

55919.952

Total

4243492.508

58

a. Predictors: (Constant), LAG_EXPO, stock

b. Dependent Variable: export

ANOVA table show that significant value is less than 0.05 mean which means this model is also perfect and regression is required.

Table 6: Coefficientsa

Unstandardized

Coefficients

Model

B

Std. Error

1 (Constant)

878.708

217.665

Stock

.0.26

.12

Lag_EXPO

.381

.116

a. Dependent Variable: export

Coefficient table shows that significant value of Stock Prices is less than 0.05, which means Stock Prices has a significant impact on Exports.

4.2 Hypothesis Assessment Summary

Table 7

Hypothesis

Independent Variables

t value

sig.

Comments

H1. Stock market is a significant predictor of economy because the movement in Stock Prices precedes fluctuations in Imports activities.

Stock Prices

2.023

0.48

Accepted - there is significant positive relationship between Stock Prices and Imports of Goods and Services of Pakistan.

Dependent Variable : Imports of Goods and Services of Pakistan

Hypothesis

Independent Variables

t value

sig.

Comments

H2. Stock market is a significant predictor of economy because the movement in Stock Prices precedes fluctuations in Exports activities.

Stock Prices

4.073

0.000

Accepted - there is significant positive relationship between Stock Prices and Exports of Goods and Services of Pakistan.

Dependent Variable : Exports of Goods and Services of Pakistan

CHAPTER 5: DISCUSSIONS, IMPLICATIONS, FUTURE RESEARCH AND CONCLUSIONS

5.1 Discussions

The effect of fluctuation of stock prices movement have been found impressive with respective of imports of Pakistan. As we have seen statically results have shown that there is strong positive correlation between these variables. Imports of goods and services of Pakistan are explaining 64.5% to stock prices. That means there is an enormous impact of fluctuation of stock prices on imports of goods and services of Pakistan. On the other hand exports of goods and services of Pakistan are also showing positive correlation with stock prices but it only explaining 26.2% to stock prices which is very much low as it was expected.

But it is also to note down that results of both imports and exports seems very much real in the context of Pakistan in the sense that Imports are higher than exports and results shown strong relation with stocks. Whereas exports of Pakistan are always lesser than imports and the data shown same behavior with it that recommends weaker relationship between exports but still our hypothesis is accepted in the case of Exports as well.

5.2 Implications and Future Research

We try to explore the market which is very unpredictable and stock markets of Pakistan are criticized due to its uncertain behavior. This paper provides opportunity to those researchers who want to check the relationship or you can say interdependence between stock market and economic variables especially in the context of Pakistan. Although exports of Pakistan are lesser than imports but still this paper provides chance for future researcher because Pakistan’s exports may increase than imports and balance of payment shifts towards positive and hence that research will be more interesting. This paper also helps to those learners who are keen to know about stock involvement or its impact in the movement of economic variables.

5.3 Conclusion

In concluding remarks we can surly say that our hypothesis are accepted through the results which are derived statically in the case of imports and although exports are explaining lesser to stock to stock prices but still we found strong positive correlation between these two variables. And that indicates our hypothesis is accepted.

CHAPTER 6: REFERENCES

Ajit, Pethe, (2000), Do Indian stock Markets Matter?, Stock market Indices and Macro- Economic Variables. Economic and political weekly. Money, Banking and Finance, Vol. 35, No. 5, PP. 349-356.

Ann, Lyign, (2004). Financial Development and Macroeconomic Fluctuations. The Economic Journal. Vol. 114, No. 495, PP. 352-376.

Atesoglu. H. Sonmez (2002. Stock Prices and Employment. Journal of Post Keynesian Economics. Vol. 24, No. 3 spring, pp. 493-498.

Borja, Brun (2005). Finance and the Business Cycle: International, Industry Evidence. The Journal of Finance. Vol. 60, No. 3, pp. 1097-1128.

Floros & Christos (2004), Stock Returns and Inflation in Greece. Applied econometrics and international development (AEEADE). Vol. 4-2.

Ferreira. Candida, (2008). The banking sector, economic growth and European integration. Journal of economic studies. Vol. 35 No. 6, pp. 512-527.

Henry Blair Peter, (2002), Is Disinflation Good for the stock market?. The Journal of Finance. Vol. 57, No. 4(Aug), PP. 1617-1648

Huang, Muhammad, Liow, (2006), Macroeconomic risk influences on the property stock market. Journal of property Investment & Finance. Vol. 24 No. 4, PP. 295-323.

Husain & Fazal, (2006). Stock Prices, Real Sector and the Causal Analysis. Journal of management and social science. Vol.2 pp 179-185.

Jain C Perm (1988). Response of Hourly Stock Prices and Trading Volume to Economic News. The Journal of Business. Vol. 61, No. 2 (Apr), pp. 219-231.

Levine & Sara (1998). Stock Markets, Banks, and Economic Growth. The American Economic Review. Vol. 88, No. 3 (Jun), PP. 537-558.

Levine (1997). Financial Development and Economic Growth: Views and Agenda. Journal of Economic Literature. Vol. 35, No. 2 (Jun), PP. 688-726.

Levine & Sara (1996) Stock Market Development and Long-Run Growth. The World Bank Economic Review. Vol. 10, No. 2 (May), PP. 323-339.

Luigi, Rajan, (1988). Financial Dependence and Growth. The American Economic Review, Vol. 88, No, pp. 559-586.

Merika, Andreas, (2006), Stock prices response to real economic variable: the case of Germany, Managerial finance, Vol. 32 No. 5, pp. 446-450.

Nagaishi, (1999), Stock Market Development and Economic Growth: Dubious Relationship. Economic and Political Weekly, Vol. 34, No. 29, pp. 2004-2012

Odedokun.O.M, (1998), Financial intermediation and economic growth in developing Countries, Journal of Economics Studies, Vol. 25 No. 3, pp. 203-224.

Park Sangkyun, (1997), Rationality of Negative Stock-Price Responses to Strong Economic Activity. Financial Analysts Journal, Vol. 53, No. 5, pp. 52-56.

Yvonne, Hsing, (2004), Impacts of Macroeconomic Policies and Financial Market Performance on Output in Singapore: A VAR Approach. The Journal of Developing Areas, Vol. 37, No. 2 , pp. 73-98.

http://www.economicshelp.org/blog/stock-market/how-does-the-stock-market-effect-the-economy/

Appendix – A

Date

 

Stock

Exports

Imports

 

 

 

 

 

 

 

Jan-05

 

6747.39

 

1307

 

1966

Feb-05

 

8260.06

 

1440

 

2152

Mar-05

 

7770.33

 

1792

 

2447

Apr-05

 

7104.65

 

1639

 

2118

May-05

 

6857.67

 

1382

 

2323

Jun-05

 

7450.12

 

1618

 

2262

Jul-05

 

7178.93

 

1512

 

2345

Aug-05

 

7796.86

 

1568

 

2822

Sep-05

 

8225.66

 

1863

 

2711

Oct-05

 

8247.34

 

1527

 

2681

Nov-05

 

9025.93

 

1420

 

2733

Dec-05

 

9556.61

 

2043

 

2721

Jan-06

 

10524.16

 

1467

 

2742

Feb-06

 

11456.27

 

1524

 

2508

Mar-06

 

11485.90

 

1824

 

3072

Apr-06

 

11342.17

 

1857

 

2299

May-06

 

9800.69

 

1759

 

3105

Jun-06

 

9989.41

 

1958

 

3454

Jul-06

 

10497.63

 

1560

 

3153

Aug-06

 

10064.13

 

1645

 

2927

Sep-06

 

10512.48

 

1632

 

2892

Oct-06

 

11327.71

 

1652

 

2850

Nov-06

 

10618.75

 

1910

 

2943

Dec-06

 

10040.50

 

1767

 

3168

Jan-07

 

11272.33

 

1696

 

2788

Feb-07

 

11180.00

 

1643

 

2859

Mar-07

 

11272.00

 

1803

 

2821

Apr-07

 

12370.00

 

1784

 

2865

May-07

 

12961.00

 

1887

 

2871

Jun-07

 

13772.00

 

2439

 

3162

Jul-07

 

13740.00

 

1734

 

3166

Aug-07

 

12214.00

 

1792

 

3017

Sep-07

 

13354.00

 

1698

 

2951

Oct-07

 

14321.00

 

1852

 

3386

Nov-07

 

13965.00

 

1788

 

3926

Dec-07

 

14077.00

 

1839

 

3783

Jan-08

 

14077.00

 

1860

 

4223

Feb-08

 

14934.00

 

2129

 

3664

Mar-08

 

15628.00

 

2170

 

4447

Apr-08

 

15122.00

 

2224

 

4390

May-08

 

12133.00

 

2253

 

4043

Jun-08

 

12353.00

 

2677

 

4447

Jul-08

 

10584.00

 

2241

 

4116

Aug-08

 

9208.00

 

1976

 

3978

Sep-08

 

9180.00

 

2627

 

4527

Oct-08

 

9183.00

 

1693

 

4082

Nov-08

 

8817.00

 

1826

 

2810

Dec-08

 

5865.00

 

1801

 

3206

Jan-09

 

5377.00

 

1629

 

2589

Feb-09

 

5727.00

 

1698

 

2342

Mar-09

 

6860.00

 

1681

 

2734

Apr-09

 

7202.00

 

1771

 

2912

May-09

 

7257.00

 

2178

 

2525

Jun-09

 

7162.00

 

2106

 

3413

Jul-09

 

7721.00

 

1803

 

3377.02

Aug-09

 

8676.00

 

1780

 

2648

Sep-09

 

9350.00

 

1875

 

2960

Oct-09

 

9257.00

 

1975

 

3429

Nov-09

 

9205.00

 

1753

 

2744

Dec-09

 

9387.00

 

1971

 

3335

APPENDIX - B

Exports Output

Variables Entered/Removedb

Model

Variables Entered

Variables Removed

1

stocka

.

a. All requested variables entered.

b. Dependent Variable: export

Model Summaryb

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.371a

.138

.123

259.00788

a. Predictors: (Constant), stock

b. Dependent Variable: export

ANOVAb

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

622243.174

1

622243.174

9.275

.003a

Residual

3890934.759

58

67085.082

Total

4513177.933

59

a. Predictors: (Constant), stock

b. Dependent Variable: export

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

B

Std. Error

Beta

t

1

(Constant)

1422.123

135.479

10.497

stock

.040

.013

.371

3.046

a. Dependent Variable: export

Coefficientsa

Model

Collinearity Statistics

Tolerance

VIF

1

stock

1.000

1.000

a. Dependent Variable: export

Collinearity Diagnosticsa

Model

Dimension

Variance Proportions

Eigenvalue

Condition Index

(Constant)

1

1

1.969

1.000

.02

2

.031

7.978

.98

a. Dependent Variable: export

Residuals Statisticsa

Minimum

Maximum

Mean

Std. Deviation

Predicted Value

1634.7640

2040.1543

1821.9667

102.69613

Residual

-381.95807

841.84094

.00000

256.80352

Std. Predicted Value

-1.823

2.125

.000

1.000

Std. Residual

-1.475

3.250

.000

.991

a. Dependent Variable: export

Variables Entered/Removed

Model

Variables Entered

Variables Removed

Method

1

LAG_EXPO, stocka

.

Enter

a. All requested variables entered.

Model Summaryb

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.512a

.262

.236

236.47400

a. Predictors: (Constant), LAG_EXPO, stock

b. Dependent Variable: export

ANOVAb

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

1111975.169

2

555987.584

9.943

.000a

Residual

3131517.339

56

55919.952

Total

4243492.508

58

a. Predictors: (Constant), LAG_EXPO, stock

b. Dependent Variable: export

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

B

Std. Error

Beta

t

1

(Constant)

878.708

217.665

4.037

stock

.026

.012

.244

2.050

LAG_EXPO

.381

.116

.391

3.294

a. Dependent Variable: export

Coefficientsa

Model

Collinearity Statistics

Tolerance

VIF

1

stock

.933

1.072

LAG_EXPO

.933

1.072

a. Dependent Variable: export

Collinearity Diagnosticsa

Model

Dimension

Variance Proportions

Eigenvalue

Condition Index

(Constant)

stock

LAG_EXPO

1

1

2.951

1.000

.00

.01

2

.038

8.867

.08

.99

.10

3

.011

16.180

.92

.00

.89

a. Dependent Variable: export

Residuals Statisticsa

Minimum

Maximum

Mean

Std. Deviation

N

Predicted Value

1586.9790

2167.6680

1830.6949

138.46294

59

Residual

-457.87039

761.94464

.00000

232.36109

59

Std. Predicted Value

-1.760

2.434

.000

1.000

59

Std. Residual

-1.936

3.222

.000

.983

59

a. Dependent Variable: export

APPENDIX - C

Imports Output

Variables Entered/Removedb

Model

Variables Entered

Variables Removed

Method

1

stocka

.

Enter

a. All requested variables entered.

b. Dependent Variable: imports

Model Summaryb

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

Durbin-Watson

1

.551a

.304

.292

534.84864

.661

a. Predictors: (Constant), stock

b. Dependent Variable: imports

ANOVAb

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

7.231E+06

1

7.231E+06

25.278

.000a

Residual

1.659E+07

58

286063.070

 

 

Total

2.382E+07

59

 

 

 

a. Predictors: (Constant), stock

b. Dependent Variable: imports

Collinearity Diagnosticsa

Model

Dimension

Eigenvalue

Condition Index

Variance Proportions

(Constant)

stock

1

1

1.969

1.000

.02

2

.031

7.978

.98

a. Dependent Variable: imports

Residuals Statisticsa

 

Minimum

Maximum

Mean

Std. Deviation

N

Predicted Value

2444.0046

3825.9553

3082.1670

350.08488

60

Residual

-949.17706

1570.30786

.00000

530.29666

60

Std. Predicted Value

-1.823

2.125

.000

1.000

60

Std. Residual

-1.775

2.936

.000

.991

60

a. Dependent Variable: imports

Variables Entered/Removed

Model

Variables Entered

Variables Removed

Method

1

lag_imp, stocka

.

Enter

a. All requested variables entered.

Model Summaryb

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

Durbin-Watson

1

.803a

.645

.632

378.23201

2.466

a. Predictors: (Constant), lag_imp, stock

b. Dependent Variable: imports

ANOVAb

Model

Sum of Squares

df

Mean Square

F

1

Regression

1.454E+07

2

7.272E+06

50.833

Residual

8.011E+06

56

143059.454

 

Total

2.256E+07

58

 

 

a. Predictors: (Constant), lag_imp, stock

b. Dependent Variable: imports

Collinearity Diagnosticsa

Model

Dimension

Eigenvalue

Condition Index

Variance Proportions

(Constant)

stock

1

1

2.948

1.000

.00

.01

2

.032

9.629

.28

.96

3

.020

12.034

.71

.03

a. Dependent Variable: imports

Residuals Statisticsa

 

Minimum

Maximum

Mean

Std. Deviation

N

Predicted Value

2266.1663

4267.6650

3101.0851

500.76476

59

Residual

-881.68445

884.62537

.00000

371.65356

59

Std. Predicted Value

-1.667

2.330

.000

1.000

59

Std. Residual

-2.331

2.339

.000

.983

59

a. Dependent Variable: imports

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