Vector Autoregression (VAR) Theory

When we have several time series, we need to take into account the interdependence between them. The VAR model is a very useful starting point in the analysis of the interrelationships between the different time series.
The VAR is just a multiple time-series generalization of the AR model. The VAR model is easy to estimate because we can use the OLS method. The VAR is commonly used for forecasting systems of interrelated time series and for analyzing the dynamic impact of random disturbances on the system of variables.
where yt is a k vector of endogenous variables, xt is a d vector of exogenous variables, A1,…, Ap and β are matrices of coefficients to be estimated, and εt is a vector of innovations that may be contemporaneously correlated with each other but are uncorrelated with their own lagged values and uncorrelated with all of the right-hand side variables.
 In practice, since we are not considering any moving average errors, the autoregressions would probably have to have more lags to be useful for prediction. Otherwise, univariate ARMA models would do better. Suppose that we consider say six lags for each variable and we have a small system with four variables. Then each equation would have 24 parameters to be estimated and we thus have 96 parameters to estimate overall. This overparameterization in one of the major problems with VAR model. The unrestricted VAR models have not been found very useful for forecasting and other extensions using some restrictions on the parameters of the VAR models have been suggested.
 A VAR is in a sense a systems regression model i.e. there is more than one dependent variable.
 Simplest case is a bivariate VAR
where uit is an iid disturbance term with E(uit)=0, i=1,2; E(u1t u2t)=0.
Vector Autoregressive Models: Notation and Concepts
 This model can be extended to the case where there are k lags of each variable in each equation:
     yt = b0 + b1 yt-1 + b2 yt-2 +...+ bk yt-k + ut
     gx1 gx1 gxg gx1 gxg gx1 gxg gx1 gx1
 We can also extend this to the case where the model includes first difference terms and cointegrating relationships (a VECM).
Vector Autoregressive Models Compared with Structural Equations Models
 Advantages of VAR Modelling
- Do not need to specify which variables are endogenous or exogenous - all are endogenous
- Allows the value of a variable to depend on more than just its own lags or combinations of white noise terms, so more general than ARMA modelling
- Provided that there are no contemporaneous terms on the right hand side of the equations, can simply use OLS separately on each equation
- Forecasts are often better than “traditional structural” models.
Problems with VAR’s
- VAR’s are a-theoretical (as are ARMA models)
- How do you decide the appropriate lag length?
- So many parameters! If we have g equations for g variables and we have k lags of each of the variables in each equation, we have to estimate (g+kg2) parameters. e.g. g=3, k=3, parameters = 30
- Do we need to ensure all components of the VAR are stationary?
- How do we interpret the coefficients?
Impulse Response Functions
 VAR models are often difficult to interpret: one solution is to construct the impulse responses and variance decompositions.
 Impulse responses trace out the responsiveness of the dependent variables in the VAR to shocks to the error term. A unit shock is applied to each variable and its effects are noted.
 Consider for example a simple bivariate VAR(1):
 A change in u1t will immediately change y1. It will change y2 and also y1 during the next period.
 We can examine how long and to what degree a shock to a given equation has on all of the variables in the system.
 A shock to the i-th variable not only directly affects the i-th variable but is also transmitted to all of the other endogenous variable through the dynamic (lag) structure of the VAR. An impulse response function traces the effect of a one standard deviation shock to one of the innovations on current and future values of the endogenous variables.
 If the innovations εt are contemporaneously uncorrelated, interpretation of the impulse response is straightforward. The i-th innovation εi,t is simply a shock to the i-th endogenous variable yi,t.
 For stationary VARs, the impulse responses should die out to zero and the accumulated responses should asymptote to some (non-zero) constant.
Variance Decomposition
 Variance decompositions offer a slightly different method of examining VAR dynamics. They give the proportion of the movements in the dependent variables that are due to their “own” shocks, versus shocks to the other variables.
 This is done by determining how much of the s-step ahead forecast error variance for each variable is explained innovations to each explanatory variable (s = 1,2,…).
 The variance decomposition gives information about the relative importance of each shock to the variables in the VAR.
Impulse Responses and Variance Decompositions: The Ordering of the Variables
 But for calculating impulse responses and variance decompositions, the ordering of the variables is important.
 The main reason for this is that above, we assumed that the VAR error terms were statistically independent of one another.
 This is generally not true, however. The error terms will typically be correlated to some degree.
 Therefore, the notion of examining the effect of the innovations separately has little meaning, since they have a common component.
 What is done is to “orthogonalise” the innovations.
 In the bivariate VAR, this problem would be approached by attributing all of the effect of the common component to the first of the two variables in the VAR.
 In the general case where there are more variables, the situation is more complex but the interpretation is the same.
Granger Causality Tests
 We can test Granger causality by running a VAR on the system of equations and testing for zero restrictions on the VAR coefficients. The Granger (1969) approached to the question of whether x causes y is to see how much of the current y can be explained by past values of y and to see whether adding lagged values of x can improve the explanation. The y is said to be Granger-caused by x if x helps in the prediction of y, or equivalently if the coefficients on the lagged x’s are statistically significant. Note that the two-way causation is frequently the cases; x Granger causes y and y Granger causes x.










The restricted model is therefore 

 The test statistic is standard Wald F-statistic 
 Where T is the number of obesrvations used in the unrestricted model, ESSU is the error sum of squares, and ESSR is the error sum of squares for restricted model

Presented by Dr. Babar Zaheer Butt to the students of MS/PhD at Iqra University Islamabad

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Why Profit Shouldn’t be Your Top Goal - a Commentary

This article says that profit should not be the top priority of the business. Infact every business aims at earning more and more profit but stakeholders are much more important. Companies must consider the stakeholders when they make mission statement. A failure to recognize and include essential stakeholders in the mission statement may be costly in the long run, particularly when competitors are better able to address these stakeholders (Nimwegen et al., 2008). For the social economist the goal of the economy is not private profit nor is it improvement in the fertility of the soil nor capital accumulation for their own sakes and that of their owners, but the material, moral and spiritual well-being of homo sapiens (Brien, 1984). Sustainability is the central part of the corporate strategy and companies manage it proactively (Kolk and Pinkse, 2007). This study shows that CEO’s who put stakeholders’ interests ahead of profit generate greater workforce engagement and thus deliver the superior financial results that they have made a secondary goal (Washburn, 2009).
References: 
Brien, J.C.O. (1984), “Social values, social goals & manpower”, International Journal of Sociology and Social Policy”, Vol. 4 Issue.1, pp. 49-62, ISSN: 0144-333X
Kolk, A. and Pinkse, J. (2007), “Towards strategic stakeholder management? Integrating perspectives on sustainability challenges such as corporate responses to climate change”, Corporate Governance, Vol. 7 Issue. 4, pp. 370-378, ISSN: 1472-0701
Nimwegen, G.V., Bollen, L., Hassink, H., and Thijssens, T. (2008), “A stakeholder perspective on mission statements: an international empirical study”, International Journal of Organizational Analysis, Vol. 16 issue 1/2, pp. 61-82, ISSN: 1934-8835
Washburn, N.T. (2009), “Why profit shouldn’t be your top goal”, Harvard Business Review, Vol. 87 Issue 12, pp. 23-23, (AN 45359642

UNIT ROOT TEST



Stationarity and Unit Root Testing
l  The stationarity or otherwise of a series can strongly influence its behaviour and properties - e.g. persistence of shocks will be infinite for nonstationary series
l  Spurious regressions. If two variables are trending over time, a regression of one on the other could have a high R2 even if the two are totally unrelated
l  If the variables in the regression model are not stationary, then it can be proved that the standard assumptions for asymptotic analysis will not be valid. In other words, the usual “t-ratios” will not follow a t-distribution, so we cannot validly undertake hypothesis tests about the regression parameters.

Stationary and Non-stationary Time Series
  1. Stationary Time Series
l  A series is said to be stationary if the mean and autocovariances of the series do not depend on time.
(A) Strictly Stationary :
n  For a strictly stationary time series the distribution of  y(t) is independent of t.  Thus it is not just the mean and variance that are constant.  All higher order moments are independent of t.
 (B) Weakly Stationary
n  A time series is said to be weakly stationary if its mean in constant and its autocovariances function depends only on the lag. No assumptions are made about higher order moments.
  1. Nonstationarity  
n  Most of the time series we encounter are nonstationary.  Any series that is not stationary is said to be nonstationary. A simple nonstaionary time-series model is                   , where the mean      is a function of time and      is a weakly stationary series.

The Random Walk (A nonstationary series)
1.            The random walk is a difference stationary series since the first difference of y is stationary:
2.            A difference stationary series is said to be integrated and is denoted as I(d) where d is the order or integration.
3.            The order of integration is the number of unit roots contained in the series, or the number of differencing operations it takes to make the series stationary.  Each integration order corresponds to differencing the series being forecast.  A first-order integrated component means that the forecasting model is designed for the first difference of the original series. For example, a stationary series is I(0). The random walk is a nonstationary with one unit root: I(1).

Unit Root Tests
l  Standard inference procedures do not apply to regressions which contain an integrated dependent variable or integrated regressors.  Therefore, it is important to check whether a series is stationary or not before using it in a regression.  The formal method to test the stationarity of a series is the unit root test.
  1. The Dickey-Fuller (DF) Test
  2. The Augmented Dickey-Fuller Test (ADF)
  3. The Phillips-Peron (PP) Test
As with the ADF test, you have to specify whether to include a constant, a constant and linear trend, or neither in the test regression.
  
l  The DF and ADF tests are frequently used in testing for unit roots although there are several problems (size distortions and low power).  With the ADF test there is the problem of selection of lag length.  AIC and SBC are used often but they have been found to select a low value of the lag length k.
The Phillips-Peron (PP) Test
l  Phillips and Perron (1988) propose a nonparametric method of controlling for higher-order serial correlation in a series.  The test regression for the PP test is the AR(1) process:
l  while the ADF test corrects for higher order serial correlation by adding lagged differenced terms on the right-hand side, the PP test makes a correction to the t-statistic of the r coefficient from the AR(1) regression to account for the serial correlation in εt.

Performing Unit Root Tests
c) Include in test equation: Intercept, Trend and intercept, None. Note that the choice is important since the distribution of the test statistic under the null hypothesis differs among these three cases. After running a unit root test, you should examine the estimated test regression reported by EViews, especially if you are not sure about the lag structure or deterministic trend in the series.  You may want to re-run the test equation with a different selection of right-hand variables (add or delete the constant, trend, or lagged differences) or lag order.
d) Lagged differences.
l  The null hypothesis of a unit root is rejected against the one-sided alternative if the t-statistic (absolute value) is less than the critical value. We reject the null hypothesis of a unit root in the CS at any of the reported significance levels.
l  For the ADF test, the test statistic is the t-statistic for the lagged dependent variable in the test regression reported at the bottom part of the table.  For the PP test, the test statistic is a modified t-statistic as described above.

Characteristics of I(0), I(1) and I(2) Series
l  An I(2) series contains two unit roots and so would require differencing twice to induce stationarity.
l  I(1) and I(2) series can wander a long way from their mean value and cross this mean value rarely.
l  I(0) series should cross the mean frequently.
l  The majority of economic and financial series contain a single unit root, although some are stationary and consumer prices have been argued to have 2 unit roots.

Criticism of Dickey-Fuller and Phillips-Perron-type tests
·         Main criticism is that the power of the tests is low if the process is stationary but with a root close to the non-stationary boundary. e.g. the tests are poor at deciding if
                                                                f=1 or f=0.95,
                especially with small sample sizes.
·         If the true data generating process (dgp) is
                                                                yt = 0.95yt-1 + ut
                then the null hypothesis of a unit root should be rejected.
·         One way to get around this is to use a stationarity test as well as the unit root tests we have looked at.

Presented by Dr. Babar Zaheer Butt to the MS/Ph.D students at Iqra University Islamabad

Brunei Darussalam Government Scholarships 2011-2012

The Government of Brunei Darussalam is offering annual scholarships under a special scholarship award scheme, for 2011-2012 academic session.

Eligibility
Applicants must be citizens of ASEAN, OIC, Commonwealth Member Countries and others and must be between the age of 18-25 at the commencement of the academic session for which they are applying for admission. However, the age limit requirement may be waived for candidates who are applying for Masters and PhD programmes.
 
Deadline: 15 Dec 2010

For more details click here 

International Scholarships

Iqra Research World: Scholarships: "This page provides information of scholarships in detail. Scholarships in USA Fulbright Scholarships• Exchange Programs• You need GRE, TOE..."

From Regional Star to Global Leader - a Commentary

No doubt that Jianguo is very competitive and sincere in his job and he has proved it but the thing which is needed now is to develop a global mindset. The most important attribute required for effective global leadership is not a new set of skills or experience, but rather a new perspective called a global mindset (Cohen, 2010). Rapid change in the organization’s environment resulting in uncertainty of goals in the issues together with continually increasing complexity of issues present considerable challenges on analytic and deductive modeling and knowledge acquisition approaches. The key function of a leader and managers in this situation is to form visions of the organization in the future (Pispa, 2003). In this way, IT can help to redefine the vision of the organizations and to make the manager’s global mindset. It is critical for top management and IT to agree on where IT is to provide leadership and vision, and where IT is expected to partner and support (Motwani et al, 2000). Top management should work with IT to translate the business vision to technical reality (Kalkan, V.D. 2008).


References:
Cohen, S.L. (2010), “Effective global leadership requires a global mindset”, Industrial and Commercial Training, Vol. 42 No. 1, pp. 3-10
Kalkan, V.D. (2008), “An overall view of knowledge management challenges for global business”, Business Process Management Journal, Vol. 14 No. 3, pp. 390-400
Motwani, J. et al. (2000), “Information technology in managing global supply chains”, Logistics Information Management, Vol. 1 No. 5, pp. 320-327
Pispa, J. and Eriksson, I.V. (2003), “Aligning organizations and their information technology infrastructure: how to make information technology support business”, Production Planning & Control, Vol. 14 No.2, pp.193-200

ECONOMETRICS FOR FINANCE - a lecture

ECONOMETRICS FOR FINANCE
The Nature and Purpose of Econometrics
          Financial econometrics:
                The application of statistical and mathematical techniques to problems in finance.
EXAMPLES OF THE FINANCIAL PROBLEMS
  1. Testing whether financial markets are informational efficient.
  2. Testing whether the CAPM or APT represent superior models for the determination of returns on risky assets.
  3. Measuring and forecasting the volatility of stock returns.
  4. Modelling long-term relationships between prices and exchange rates
  5. Testing technical trading to determine which makes the most money.
  6. Testing the hypothesis that earnings or dividend announcements have any effect on stock prices.
  7. Testing whether markets react to news.
  8. Forecasting the correlation between the returns to the stock indices.
TYPES OF DATA AND NOTATION
          There are 3 types of data which econometricians might use for analysis:
                1. Time series data
                2. Cross-sectional data
                3. Panel data, a combination of 1. & 2.
          The data may be quantitative (e.g. exchange rates, stock prices, interest rates), or qualitative (e.g. day of the week).
          Examples of time series data
                Series                                                    Frequency
                GNP or unemployment                 monthly, quarterly or annually
                Budget deficit                               annually
                Money supply                              weekly, monthly
                Stock market index                      daily, weekly, monthly
Time Series versus Cross-sectional Data
          Time Series Data
                - The variation in the value of a country’s stock index with that of economic fundamentals.
                - The variation in the value of a company’s stock price with the announced value of its dividend                                payment.
                - The effect on a country’s currency of an increase in its interest rate
          Cross-sectional data are data on one or more variables collected at a single point in time, e.g.
                - A survey of usage of internet stock  broking services
                - A survey of usage of internet banking
                - Investors behaviour
PANEL DATA
          Panel Data has the dimensions of both time series and cross-sections, e.g. the daily prices of a number of blue chip stocks over two years.
          It is common to denote each observation by the letter t and the total number of observations by T for time series data, and to denote each observation by the letter i and the total number of observations by N for cross-sectional data.
Cardinal, Ordinal and Nominal Numbers
          Another way in which we could classify numbers is according to whether they are cardinal, ordinal, or nominal.
          Cardinal numbers are those where the actual numerical values that a particular variable takes have meaning, and where there is an equal distance between the numerical values.
        Examples of cardinal numbers would be the price of a share or the number of houses in a street.
          Ordinal numbers can only be interpreted as providing a position or an ordering.
        Examples of ordinal numbers would be the position of a runner in a race or stock or bond rating.
          Nominal numbers occur where there is no natural ordering of the values at all.
        Such data often arise when numerical values are arbitrarily assigned..
          Cardinal, ordinal and nominal variables may require different modeling approaches or at least different treatments.
Returns in Financial Modelling
          It is preferable not to work directly with asset prices, so we usually convert the raw prices into a series of returns. There are two ways to do this:
          We normally ignore any dividend payments, or alternatively assume that the price series have been already adjusted to account for them.
A Disadvantage of using Log Returns
          There is a disadvantage of using the log-returns. The simple return on a portfolio of assets is a weighted average of the simple returns on the individual assets.
          But this does not work for the continuously compounded returns.
Some Points to consider when reading papers in the finance literature
1. Does the paper involve the development of a theoretical model or is it merely a technique looking for an application, or an exercise in data mining?
2. Is the data of “good quality”? Is it from a reliable source? Is the size of the sample sufficiently large for asymptotic theory to be invoked?
3. Have the techniques been validly applied? Have diagnostic tests for violations been conducted for any assumptions made in the estimation of the model?
4. Have the results been interpreted sensibly? Is the strength of the results exaggerated? Do the results actually address the questions posed by the authors?
5. Are the conclusions drawn appropriate given the results, or has the importance of the results of the paper been overstated?
TIME SERIES DATA ISSUES
          Heteroscedasticity
          Auto correlation
          Multi colinearity
Heteroscedasticity
          If the errors do not have a constant variance, we can say that they are heteroscedastic. It implies that values in a series have different numbers and dimensions. 
          White’s general test for heteroscedasticity is one of the best approaches because it makes few assumptions about the form of the heteroscedasticity.
          Transforming the variables into logs or reducing by some other measure of “size”.
          The concept of a lagged value
Consequences of Using OLS in the Presence of Heteroscedasticity
          OLS estimation still may gives biased coefficient estimates.
          This implies that if we still use OLS in  the presence of heteroscedasticity, our standard errors could be inappropriate and hence any inferences we make could be misleading.
          Whether the standard errors calculated using the usual formulae are too big or too small will depend upon the form of the heteroscedasticity.
          If the form (i.e. the cause) of the heteroscedasticity is known, then we can use an estimation method which takes this into account (called generalised least squares, GLS or ARCH/GARCH).
Autocorrelation
          If there are patterns in the residuals from a model, we say that they are autocorrelated or values in a series are following a pattern.
  1. Positive Autocorrelation
  2. Negative Autocorrelation
  3. No pattern in residuals – No autocorrelation
Detecting Autocorrelation
The Durbin-Watson (DW) is a test for first order autocorrelation - i.e. it assumes that the relationship is between an error and the previous one.
                Unfortunately, DW has 2 critical values, an upper critical value (du) and a lower critical value (dL), and there is also an intermediate region where we can neither reject nor not reject H0.
The Durbin-Watson Test: Interpreting the Results
Conditions which Must be Fulfilled for DW to be a Valid Test
                1. Constant term in regression
                2. Regressors are non-stochastic
                3. No lags of dependent variable
Consequences of Ignoring Autocorrelation
          The coefficient estimates derived using OLS are still unbiased, but they are inefficient, i.e. even in large sample sizes.
          Thus, if the standard error estimates are inappropriate, there exists the possibility that we could make the wrong inferences.
          R2 is likely to be inflated relative to its “correct” value for positively correlated residuals.
Multicollinearity
          This problem occurs when the explanatory variables are very highly correlated with each other.
          Problems if multicollinearity is present but ignored
                - R2 will be high but the individual coefficients will have high standard errors.
                - The regression becomes very sensitive to small changes in the specification.
                - Thus confidence intervals for the parameters will be very wide, and significance tests might     therefore give inappropriate conclusions.
          The easiest way to measure the extent of multicollinearity is simply to look at the matrix of correlations between the individual variables.
Solutions to the Problem of Multicollinearity
          “Traditional” approaches, such as principal components. But these usually bring more problems than they solve.
          Some econometricians argue that if the model is otherwise OK, just ignore it.
          The easiest ways to “cure” the problems are
                - drop one of the collinear variables
                - transform the highly correlated variables into a ratio
                - go out and collect more data e.g.
                                                - a longer run of data
                                                - switch to a higher frequency

Presented by Dr. Babar Zaheer Butt to the MS/PhD Students at Iqra University Islamabad 

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How Toxic Colleagues Corrode Performance - a commentary

Today, most of the executives perceive competence diversity, mutual trust, and team spirit as crucial factors in cultivating knowledge-sharing behaviors. Successful management of tacit organizational knowledge sharing requires a deep understanding of the specific cultural values that underpin both behavior and organizational culture. In some cultures it is taken very seriously. For example, Singaporean-Chinese values are rooted in the Confucian ethos, notions of kiasu (the fear of losing out to others), guanxi (social relationships), and xinyong (the use or the usefulness of trust) (Koki, 2006). It is very important for the organizations to establish a civil and social work environment where employees can share their knowledge willingly and easily. Rude behaviors of managers are a source of a highly toxic and dysfunctional organizational behavior; borderline personality disorder in a manager may serve as a systemic toxin for an organization (Goldman, 2006). There are possible solutions to reduce or even eliminate toxicity in an organization. Recognition, and the use of toxin handlers to eliminate, reduce, or avoid the permeation and spreading of these toxins can be possible solutions for the organizations that suffer from or would like to prevent toxicity in the workplace (Appelbaum and Girard, 2007).

Reference:
Appelbaum, S.H. and Girard, D.R. (2007), “Toxins in the workplace: affect on organizations and employees”, Corporate Governance, Vol. 7 Issue. 1, p.17-28, ISSN: 1472-0701
Goldman, K. (2006), “High toxicity leadership: Borderline personality disorder and the dysfunctional organization”, Journal of Managerial Psychology, Vol. 21 Issue. 8, p.733-746, ISSN: 0268-3946
Koki, H.C. (2006), “Cultivating Knowledge Sharing: An Exploration of Tacit Organization Knowledge in Singapore”, Journal of Asian Business, Vol.22/23 Issue 2/3/1, p.169-187, ISSN: 1068-0055

Call for Papers

1. Special issue call for papers from International Journal of Conflict Management
Areas / Topics
  • Conflict
  • Conflict management
  • Dispute resolution
  • Fairness
  • Justice
  • Mediation and arbitration
  • Negotiation
  • Peace studies
  • Related topics
Manuscript submission deadline: 28-11-2011. For details visit International Journal of Conflict Management


2. Inauguration issue call for papers from Far East Journal of Psychology and Business
Areas / Topics
  • Money, Banking, Finance and Investment
  • Psychology and Human Related Issues
  • Marketing
  • Security Markets, Business Economics
  • Accounting Practices
  • Social Issues and Public policy 
  • Management Organization 
  • Statistics and Econometrics 
  • Administration and Management 
  • International Trade
  • Industrial Relations and Labor 
  • Business Communication 
  • Mass Communication 
  • History and Psychology
  • Cold War and Business 
  • Inter-Cultural Encounters 
  • Peace and Unity for Business Growth
Deadline for Submission: 30-11-2010. For details visit http://www.fareastjournals.com/

3. International conference on Global financial Crisis: Challenges & Opportunities On 13-15 January 2011, organized by Bhupal Nobles (P.G.) College, Udaipur, India
For details please visit www.bnpgcollege.org/conference 

Why multinationals struggle to manage talent - a commentary

Organizational culture and team development have become more and crucial with the development of globalization and virtual organizations (Wang, 2006). In the recent years, teams and interpersonal relationship are becoming more and more important HR factors for organizational effectiveness of technological innovations (Wang and Mobley, 1999).  There is a challenge of balancing the culture of openness and knowledge-sharing with the need to appropriate knowledge as intellectual property (Mason & Pauleen 2003). For advanced global companies, employee development is a pillar of the enterprise-value framework, equal in importance to shareholder support or customer loyalty. Retaining talent is identified as a key business priority for all the companies surveyed by World Economic Forum (Manardo, 2008). Global firms are increasingly using Internet – IT related technologies to enable and necessitate the use of virtual teams around the world to solve complex global problems and foster both knowledge integration and continuous learning, which aid in the motivation and retention of a company’s best people (Manardo, 2008). The rapid organizational re-structuring and globally distributed engineering have called for the needs for integrated strategies and the new ways of HRM in promoting technology innovation, organizational change, and entrepreneurship (Davis et al, 1986).

References:
Davis, D.D et al. (1986), Managing Technological Innovation, Jossey-Bass publishers, San Francisco, CA
Mason, D. and Pauleen, D.J. (2003), “Perceptions of knowledge management: a qualitative analysis”, Journal of Knowledge Management, Vol. 7 No. 4, pp. 38-48
Manardo, J. (2008), Globalization at Internet speed, MCB University Press. 1887-BS72
Wang, Z. (2006), “Organizational effectiveness through technology innovation and HRM strategies”, international Journal of Manpower, Vol. 26 No. 6, pp. 481-487

The Cultural Roots of Business - a Commentary

Modern organizations face many significant challenges because of turbulent environments and a competitive global economy (Shachaf, 2008). Every country has its own culture and managers from that particular region must have the essence of their cultures. For example, the passive and polite Chinese communication practices are in direct contrast to the Western tendency to be direct and articulate with co-workers regarding business issues; the proactive, risk-taking behavior of the foreigners to identify and resolve problems early enters into conflict with the Chinese preference to maintain harmony and peace by avoiding initiative taking; and Chinese respect for authority and seniority conflicts with the Western preference for competency-based business practices (Keefe and Keefe, 1997).  Managers doing business in emerging Asian markets need to go beyond traditional national culture stereotypes to capture cultural diversities and paradoxes in terms of, for example, ethnic culture, regional culture, professional culture, and emerging global culture groupings within and across national borders (Fetcher and Fang, 2006).

References:
Fletcher, R. Fang, Tony, “Assessing the impact of culture on relationship creation and network formation in emerging Asian markets”, European Journal of Marketing, Vol. 40, Issue. 3/4, p. 430-446, ISSN: 0109-0566
Kieefe, H.O. and Keefe, M.O. (1997), “Chinese and Western behavioural differences: understanding the gaps”, International Journal of Social Economics, Vol. 24 Issue 1/2/3, p. 190-196, ISSN: 0306-8293
Shachaf (2008), “Cultural diversity and information and communication technology impacts on global virtual teams: An exploratory study”, Information & Management, Vol. 45 Issue. 2, p. 131-142, ISSN: 0378

Tips for getting your research paper accepted in an impact factor journal

Researchers always wish to get their research paper published in an impact factor journal. No doubt that having publications in impact factor journals is a great achievement for the researchers. But impact factor journals require a very high profile research work. Some suggestions are given here which will help the research scholars.
  • There must be some novelty in the research.
  • Manuscript must be written in standard English.
  • Use of passive voice should be avoided.
  • Write in interesting manner and tell the reader what will be learned from the manuscript.
  • Be direct and specific and define all terms used in manuscript.
  • Avoid ambiguous terms, buzzwords, jargon, acronyms, and stringing nouns together.
  • Do not make exaggerated claims.
  • Tell the readers what was learned. 
  • Read your paper several times.
  • Tell some one else (your colleague or professional) to read your paper and give suggestions for improvement.

Very important tip: Whenever  you select  an appropriate journal for you manuscript, then don't forget to use citations in you manuscript from that journal. Citations of those articles should be made which were published in that journal in the previous two years. In this way, if your manuscript was published then the impact factor of that journal would also be increased. Editors will definitely consider your manuscript if it contains citations from their journals.

Free Access to Journals

Free access to following impact factor journals is available for the limited time. Researchers must avail of the this opportunity.

Environmental and Resource Economics 

ISSN: 0924-6460 (print version)
ISSN: 1573-1502 (electronic version)
Journal no. 10640

Journal of Economics

ISSN: 0931-8658 (print version)
ISSN: 1617-7134 (electronic version)
Journal no. 712

The Journal of Real Estate Finance and Economics 

ISSN: 0895-5638 (print version)
ISSN: 1573-045X (electronic version)
Journal no. 11146

Public Choice

ISSN: 0048-5829 (print version)
ISSN: 1573-7101 (electronic version)
Journal no. 11127

Review of World Economics

ISSN: 1610-2878 (print version)
ISSN: 1610-2886 (electronic version)
Journal no. 10290

Impact Factor Journals - Economics

2009-Economics


Title
Country
Impact
Hungary
0.115
Netherlands
0.673
Czech Republic
0.716
United States
2.531
United States
1.047
United States
0.282
China
0.243
United States
-
United States
-
England
0.404
England
0.241
Australia
0.400
Australia
1.042
Australia
0.615
Belgium
0.731
Australia
0.308
Australia
0.373
Australia
0.200
England
1.055
Australia
0.613
Latvia
0.000
United States
0.470
United States
0.343
United States
0.390
United States
2.107
England
0.837
Canada
0.552
Canada
0.582
Chile
0.316
England
0.565
United States
1.066
China
0.424
United States
0.659
England
0.380
Japan
0.125
United States
0.189
Netherlands
2.422
Chile
0.026
United States
0.863
United States
0.633
United States
0.763
United States
3.452
England
0.885
Netherlands
2.016
United States
0.962
England
1.902
United States
1.069
Switzerland
0.359
Netherlands
0.588
United States
0.595
England
2.375
Italy
0.914
Australia
0.582
England
1.527
United States
0.727
England
0.774
England
0.733
United States
1.745
United States
0.743
England
4.000
England
1.185
Netherlands
0.390
Slovakia
0.237
Croatia
0.036
United States
0.338
Austria
0.579
Netherlands
2.333
United States
1.857
Netherlands
1.314
Netherlands
1.131
Germany
1.337
England
0.196
England
0.860
England
0.634
Netherlands
3.300
United States
0.576
United States
0.481
England
1.205
Germany
0.135
United States
0.378
England
1.606
England
0.669
United States
1.239
England
0.681
Spain
0.375
England
2.011
United States
0.293
Japan
0.080
United States
0.510
England
1.513
United States
0.239
Netherlands
0.745
Netherlands
0.960
United States
1.030
England
0.556
Netherlands
1.064
Germany
0.527
Netherlands
0.924
Italy
0.146
United States
0.556
Netherlands
0.735
Mexico
0.091
Spain
0.370
Netherlands
2.605
England
0.698
Scotland
1.155
United States
0.474
England
2.000
Argentina
0.200
England
0.125
England
0.204
Australia
0.548
Netherlands
1.908
Lithuania
2.015
United States
1.562
United States
1.360
Netherlands
0.906
Netherlands
1.791
England
0.899
Austria
0.592
Netherlands
1.081
Netherlands
1.097
United States
0.145
England
3.937
Netherlands
3.083
United States
0.691
United States
6.919
United States
1.239
United States
3.557
England
0.244
Netherlands
1.473
England
1.228
United States
1.092
United States
2.581
England
1.425
Germany
0.947
Switzerland
4.020
United States
0.897
United States
1.603
Sweden
1.100
Netherlands
1.885
United States
0.946
United States
1.877
England
1.111
Germany
0.368
Netherlands
2.271
England
0.309
United States
0.368
South Korea
0.537
United States
1.511
United States
1.643
United States
1.961
United States
0.372
Switzerland
0.471
United States
0.429
Netherlands
1.755
United States
1.194
United States
1.545
United States
0.763
United States
3.841
United States
0.857
United States
0.333
Netherlands
0.806
Netherlands
1.236
United States
0.500
Netherlands
0.659
United States
0.585
United States
1.132
Netherlands
0.768
United States
0.612
United States
1.519
England
0.795
United States
1.914
Germany
0.254
England
1.316
United States
0.179
Netherlands
0.333
England
0.983
Netherlands
0.992
United States
1.558
United States
0.517
England
0.218
United States
0.492
United States
1.214
United States
0.465
United States
0.796
Netherlands
0.284
England
1.092
England
0.761
England
0.809
Australia
0.244
New Zealand
2.612
Czech Republic
0.500
Germany
0.526
England
0.196
United States
0.400
United States
0.750
United States
5.647
United States
1.200
England
0.621
United States
1.306
United States
0.647
Netherlands
0.910
Netherlands
1.333
Netherlands
0.523
Venezuela
0.066
United States
0.459
Spain
0.125
United States
0.975
Spain
0.032
France
0.169
United States
2.555
England
2.904
United States
3.645
France
0.045
England
0.770
Netherlands
0.441
England
1.288
Germany
0.524
Romania
0.620
South Africa
0.248
South Africa
0.082
Sweden
0.558
England
0.522
Singapore
0.224
Netherlands
1.380
Germany
0.683
United States
0.588
Spain
0.667
United States
0.857
Netherlands
0.641
England
0.717
Lithuania
1.205
Mexico
0.203
United States
3.032
England
1.348
England
1.766
England
1.474
England
1.225
Netherlands
1.159
Croatia
0.185