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Selasa, 10 November 2015
Public Goods - Simply Explained
This is simple explanation of public goods which is essentially important to understand economic policies and phenomenon.
Public Goods can be defined as Goods that simultaneously provide benefits to more one individual at the same time (joint consumption) . This type of goods has distinct characteristics as follows.
(1) Non rivalry. It means that there's no competition (rivalry) to get a similar quality of certain services. In other words, once the good is provided then additional cost to consume is zero.
(2) Non excludable. It means that it is almost impossible or very costly to exclude particular consumers (free rider problems).
Therefore, we can plot the two characteristics as x-axis and y-axis as follows.It is shown that goods cannot define clearly whether it is public or private. In the end, we can fine other types of goods i.e. club goods and common property.
or it can be explained by using following diagram.
Few examples of public goods are services of defense, public broadcasts (radio and TV services), flood control, law enforcement, etc.
To define public or private goods, we can use simple method as follows.
Sources:
(1) Hyeon Park. International School of Urban Studies, Lecture materials
(2) Non excludable. It means that it is almost impossible or very costly to exclude particular consumers (free rider problems).
Therefore, we can plot the two characteristics as x-axis and y-axis as follows.It is shown that goods cannot define clearly whether it is public or private. In the end, we can fine other types of goods i.e. club goods and common property.
 |
| Public vs Private Goods |
 |
| Public vs Private Goods |
Few examples of public goods are services of defense, public broadcasts (radio and TV services), flood control, law enforcement, etc.
To define public or private goods, we can use simple method as follows.
 |
| Public vs Private |
Sources:
(1) Hyeon Park. International School of Urban Studies, Lecture materials
(2) Our Lovely Uncle Google
Senin, 09 November 2015
Key Success Factors - Korean Experience
It's great opportunity to have insightful lecture from Mr. Lee about what Korea has been through towards its industrialization era. This is first article about Korean experience shared to followers of SiBernas Blog.
Understanding Korean History of industrialization may give us a better picture that inspire many developing countries to cope with their problems. Korea shows tremendous successes especially in economic development. Data tells us that in terms of GDP per capita Korea (104 USD) performed well below Mexico (369 USD), Phillipines (157 usd), and Srilanka (140 usd) in 1960. However, it only took 41 years for Korea to flip the position. In 2013 GDP per capita of Korea (25,977 usd) was far above those three countries (10307 usd, 2765 usd, and 3280 usd respectively).
The question is what are key factors leading to current result?
In short, there are three (3) aspects underpinned Korea development up to current stage: (1) Good Government; (2) Friendly Market Environment; and (3) Supporting Condition (internal and external).
Good Government involved continuation of reforms as well as promoting coherent bureaucracies. During 60s to 70s, President showed commitment through chairing monthly Trade and Export Promotion Meeting. Reform was also being done through transparent and competitive selection processes for government employees. In the past, government had experienced to layoff 1/8 of its employees.
Moreover, Korean Government set friendly environment for market in few ways. (1) it changed policy from import substitution to export promotion even though it's primarily because Korea was lacking of domestic fund.; (2) Government provided necessary infrastructure (energy, transportation) in timely manner; and (3) government was set currency exchange such that it promoted export policy. It is also worth to noting that Korea government set export policy that focus on few industries that grouped in heavy and chemical industries. In 1970s government decided to focus on 5 industries: (1) ship building; (2) electronics; (3) automobile; (4) steel; and (5) chemical. The focus was decided based on three criteria: (1) a chance to be very successful; (2) backward linkages; and (3) generate employment.
Finally, Government set up necessary condition to support industrialization era in two directions human and social capital. Government promoted equal education to all people which 'created ' more egalitarian people. In terms of social capital, the well known program that changed the whole story is Sae Maul movement. It succeeded to encourage citizens to change their habit to superior work ethics (competent and competitive). In fact, Korea was also gifted by external condition that bolstered economic development. China was sleeping when Korea started its development.
In case of Indonesia, the key factors above can be adapted or adopted for its own good.
Sources:
(1) Lecture materials; Mr. Siwook Lee (Executive Director of KDI
(2) Our best uncle Google
 |
| Korean Flag |
The question is what are key factors leading to current result?
In short, there are three (3) aspects underpinned Korea development up to current stage: (1) Good Government; (2) Friendly Market Environment; and (3) Supporting Condition (internal and external).
Good Government involved continuation of reforms as well as promoting coherent bureaucracies. During 60s to 70s, President showed commitment through chairing monthly Trade and Export Promotion Meeting. Reform was also being done through transparent and competitive selection processes for government employees. In the past, government had experienced to layoff 1/8 of its employees.
Moreover, Korean Government set friendly environment for market in few ways. (1) it changed policy from import substitution to export promotion even though it's primarily because Korea was lacking of domestic fund.; (2) Government provided necessary infrastructure (energy, transportation) in timely manner; and (3) government was set currency exchange such that it promoted export policy. It is also worth to noting that Korea government set export policy that focus on few industries that grouped in heavy and chemical industries. In 1970s government decided to focus on 5 industries: (1) ship building; (2) electronics; (3) automobile; (4) steel; and (5) chemical. The focus was decided based on three criteria: (1) a chance to be very successful; (2) backward linkages; and (3) generate employment.
Finally, Government set up necessary condition to support industrialization era in two directions human and social capital. Government promoted equal education to all people which 'created ' more egalitarian people. In terms of social capital, the well known program that changed the whole story is Sae Maul movement. It succeeded to encourage citizens to change their habit to superior work ethics (competent and competitive). In fact, Korea was also gifted by external condition that bolstered economic development. China was sleeping when Korea started its development.
In case of Indonesia, the key factors above can be adapted or adopted for its own good.
Sources:
(1) Lecture materials; Mr. Siwook Lee (Executive Director of KDI
(2) Our best uncle Google
Senin, 02 November 2015
Types of Data in Research based on Scale
Data is an essential ingredient when we conduct a research even though we only employ descriptive analysis. Understanding the types of data therefore is necessary to improve quality of the research. There are many different types of data based on different categories such as based on (1) structure; (2) character; 3) source; (4) collection time; or (5) based measurement scale. This article extracts data based on the scale.
In general, there are four types of data called as NomOrVaRio which stands for Nominal, Ordinal, Interval, and Ratio. NomOrVaRio is not universally accepted but it seems widely used. Explanation for each of those is described as follows.
First, Nominal is the lowest level data because it captures very limited information i.e. IDs or identities or discrete. It differentiates one data from the rest. There is no rank among them as well as no arithmetical equation can be done (addition, subtraction, etc). Usually, we create the Ordinal data by categorization or classification. Statistically, we can count the data as well as find the mode of the data-set. Examples of this data are:
(1) Group of female as 1 and male as 2. We cannot say 2 is better than 1.
(2) Identification for Golkar Party is 1, PDIP Party is 2, etc.
Second, Ordinal has attribute more advance than Nominal because Ordinal data shows a rank (or level) between the data even though it cannot be used in a mathematics expression. Similarly, we create the Ordinal data by categorization or classification. Few examples are:
(1) We set 1 as elementary school, 2 as junior high school, and 3 as senior high school. Therefore we can conclude that 2 is higher than 1.
(2) Customer satisfaction is identified using 1 for very satisfied, 2 for somewhat satisfied, and 3 dissatisfied.
Third, Interval has more characteristics than the previous data. It shows an interval or distance between one data and the others. There is no classification or categorization. Using Interval we can calculate arithmetical equation (addition and subtraction). However, there is no 'true (absolute) zero'. Examples of this type are temperature, IQ scores, test scores. Here is an example.
(1) Test performances are shown as E for 1, D for 2, C for 3, B for 4, and A for 5. therefore we may say that B people has 2 level better than D (4-2=2), but we cannot B is twice as good as D.
Fourth, Ratio is the highest level of data because it has all of characteristics owned by the other data types. It has 'true zero' therefore we can set all mathematics expressions using the data.
(1) Weight of babies A, B, and C are 4 kg,3 kg, and 2 kg respectively. It can be concluded that ratio of baby A and C is 2 (4/2=2).
In general, there are four types of data called as NomOrVaRio which stands for Nominal, Ordinal, Interval, and Ratio. NomOrVaRio is not universally accepted but it seems widely used. Explanation for each of those is described as follows.
First, Nominal is the lowest level data because it captures very limited information i.e. IDs or identities or discrete. It differentiates one data from the rest. There is no rank among them as well as no arithmetical equation can be done (addition, subtraction, etc). Usually, we create the Ordinal data by categorization or classification. Statistically, we can count the data as well as find the mode of the data-set. Examples of this data are:
(1) Group of female as 1 and male as 2. We cannot say 2 is better than 1.
(2) Identification for Golkar Party is 1, PDIP Party is 2, etc.
Second, Ordinal has attribute more advance than Nominal because Ordinal data shows a rank (or level) between the data even though it cannot be used in a mathematics expression. Similarly, we create the Ordinal data by categorization or classification. Few examples are:
(1) We set 1 as elementary school, 2 as junior high school, and 3 as senior high school. Therefore we can conclude that 2 is higher than 1.
(2) Customer satisfaction is identified using 1 for very satisfied, 2 for somewhat satisfied, and 3 dissatisfied.
Third, Interval has more characteristics than the previous data. It shows an interval or distance between one data and the others. There is no classification or categorization. Using Interval we can calculate arithmetical equation (addition and subtraction). However, there is no 'true (absolute) zero'. Examples of this type are temperature, IQ scores, test scores. Here is an example.
(1) Test performances are shown as E for 1, D for 2, C for 3, B for 4, and A for 5. therefore we may say that B people has 2 level better than D (4-2=2), but we cannot B is twice as good as D.
Fourth, Ratio is the highest level of data because it has all of characteristics owned by the other data types. It has 'true zero' therefore we can set all mathematics expressions using the data.
(1) Weight of babies A, B, and C are 4 kg,3 kg, and 2 kg respectively. It can be concluded that ratio of baby A and C is 2 (4/2=2).
Rabu, 03 April 2013
Cointegration Test
Cointegration test is one of important procedures in econometrics. It's confusing.
Here is the basic procedure:
http://davegiles.blogspot.com/2011/05/cointegrated-at-hips.html
Here is the basic procedure:
- Test each series to see if both of them are non-stationary, and have the same order of integration.
- If both series are (say) I(1), then construct a VAR model for the 2 variables - this will require choosing the maximum lag-length.
- Test the residuals of the VAR model to see if the errors are (a) independent; and (b) normally distributed.
- Use Johansen's methodology to test if the 2 series are cointegrated.
- In step 6, take account of the structural breaks in the trends of the data, if any. To do this, use the methods introduced by Johansen et al. (2000).
http://davegiles.blogspot.com/2011/05/cointegrated-at-hips.html
Rabu, 21 November 2012
Model identification in time series - ARMA (p,q)
ARMA (p,q) model is commonly used in time series analysis. Therefore it is essential to be able to identify the appropriate ARMA (p,q) model for the particular series. Employing correlogram and partial correlogram to help in the identification process is common. Basically, correlogram and partial correlogram are plot diagrams of the autocorrelation function (ACF) and partial autocorrelation function (PACF) respectively.
The auto-correlation function (ACF) can be defined as a set of correlation coefficients between the series and lags of itself over time. While the definition of partial auto-correlation function (PACF) is the partial correlation coefficients between the series and lags of itself over time.
1. Autoregressive (AR) model
The AR model is a model which includes lagged terms of the time series itself.
We conclude that the series is AR process if
2. Moving Average (MA) model
The MA model is a model which includes lagged terms on the noise or residuals.
The patterns for MA process as follows
3. ARMA model
This is just a combination of MA and AR terms. Therefore the pattern shows combination of AR and MA process.
Thus, we may head on to following table.
The auto-correlation function (ACF) can be defined as a set of correlation coefficients between the series and lags of itself over time. While the definition of partial auto-correlation function (PACF) is the partial correlation coefficients between the series and lags of itself over time.
1. Autoregressive (AR) model
The AR model is a model which includes lagged terms of the time series itself.
We conclude that the series is AR process if
- the PACF displays a sharp cutoff
- while the ACF decays more slowly (i.e., has significant spikes at higher lags) or oscillates (exponentially decays).
2. Moving Average (MA) model
The MA model is a model which includes lagged terms on the noise or residuals.
The patterns for MA process as follows
- the ACF of the differenced series displays a sharp cutoff
- while the decays slowly or oscillates (exponentially decays).
3. ARMA model
This is just a combination of MA and AR terms. Therefore the pattern shows combination of AR and MA process.
Thus, we may head on to following table.
Selasa, 20 November 2012
Unit Root Test in Eviews (Video)
Unit root test is essential if one wants to perform causality test among variables. This test can be conducted with help popular software Eviews. I have found video tutorial on Youtube and embedded the video here.
You may see the video on Youtube or in here.
You may see the video on Youtube or in here.
Basic Procedure for Causality Test
In empirical work, causality between two or more variables is very popular particularly to find the relationship directions. Two common examples are energy and economic growth nexus, relationship between trade and economic growth.
Basic procedure in testing causality can be summarized as follows:
Step 1: Unit root test
In this stage the order of integration for the variables needs to be checked such that the level of stationarity of the time series can be found.
Common method for this test is Augmented Dickey Fuller (ADF) test.
Step 2: Co-integration test
Co-integration is unique and it represents a long run relationship or equilibrium among tested variables. In short, we need to evaluate whether or not the linear combination is stationary. The non-stationary variables are co-integrated if their linear combination (the error-term) is stationary.
Two common methods used to check existence of cointegration are:
a. Engle and Granger cointegration test.
b. Johansen and Joselius cointegration test.
Step 3: Granger non-causality test
In this step, we determine the relationships between varibles.
If the variables are non cointegrated then they only have short run relationships. To assess the direction of causality between the variables, we use Standard Granger test.
On the other hand, if the variables are cointegrated then the variables have not only short run but also long run relationships. We usually use vector error correction model (VECM) to check the causality direction.
In addition, Toda and Yamamoto developed a procedure to deal with the problems that exist in VECM approach.
Here is very good reference:
http://davegiles.blogspot.com/2011/04/testing-for-granger-causality.html
Basic procedure in testing causality can be summarized as follows:
Step 1: Unit root test
In this stage the order of integration for the variables needs to be checked such that the level of stationarity of the time series can be found.
Common method for this test is Augmented Dickey Fuller (ADF) test.
Step 2: Co-integration test
Co-integration is unique and it represents a long run relationship or equilibrium among tested variables. In short, we need to evaluate whether or not the linear combination is stationary. The non-stationary variables are co-integrated if their linear combination (the error-term) is stationary.
Two common methods used to check existence of cointegration are:
a. Engle and Granger cointegration test.
b. Johansen and Joselius cointegration test.
Step 3: Granger non-causality test
In this step, we determine the relationships between varibles.
If the variables are non cointegrated then they only have short run relationships. To assess the direction of causality between the variables, we use Standard Granger test.
On the other hand, if the variables are cointegrated then the variables have not only short run but also long run relationships. We usually use vector error correction model (VECM) to check the causality direction.
In addition, Toda and Yamamoto developed a procedure to deal with the problems that exist in VECM approach.
Here is very good reference:
http://davegiles.blogspot.com/2011/04/testing-for-granger-causality.html
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