Minhhoa bai giang kinh te luong
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The document reports the results of a regression analysis with LUONGHANG as the dependent variable. The regression found GIA and QUANGCAO to be statistically significant predictors of LUONGHANG, while C was also a statistically significant predictor and positively related to LUONGHANG. Additional tests found C to be statistically different from -1.
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Dam phan bang thu trong xuat nhap khau
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The document is a letter from the Khoa Th動董ng m畉i- Du l畛ch- Marketing (Faculty of Commerce - Tourism - Marketing) at the University of Economics Ho Chi Minh City. It assigns Nguy畛n Th畛 Kim Hoa of class TM01 the topic "Business Negotiation by Letter" to complete under the supervision of Professor on Th畛 H畛ng V但n. The letter provides Kim Hoa with her topic and supervisor for her assigned work.
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This document contains statistical summaries of closing prices and trading volumes for stocks from 2003 to 2011. It includes measures like mean, median, maximum, minimum, standard deviation, skewness, kurtosis, and observations. It also shows the results of a least squares regression model analyzing the relationship between closing price (dependent variable) and volume with an R-squared of 0.250886. A second regression analyzes the relationship between closing price and dividends from 2006 to 2010 with an R-squared of 0.414438.Output
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This document contains the results of three regressions analyzing the relationship between poverty (dependent variable) and income inequality and growth (independent variables) in 8 cross-sections over the period of 2002-2012. The normal regression shows a negative relationship between poverty and growth but the results are not statistically significant. The fixed effects regression finds a positive relationship between poverty and income inequality and negative relationship with growth. The random effects regression finds a negative relationship between poverty and growth but the other coefficients are not statistically significant.Lampiran error correction model
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1. The document analyzes conventional and Islamic monetary policy models in Indonesia from 1997 to 2003 using error correction models. It finds that changes in monetary aggregates (M1 and M2) are positively correlated with prior changes in the monetary base for both conventional and Islamic models.
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The document describes the Cobb-Douglas production function and the results of estimating its parameters. It finds that labor (L) and capital (K) explain 95% of the variation in output (Q) according to the estimated equation Q=-0.31+0.20K+0.95L. Diagnostic tests show the variables are stationary and there is no multicollinearity. The model is found to be statistically significant and a good fit to the data based on the F-statistic and R-squared value. It is determined that industry exhibits increasing returns to scale since the estimated coefficients of K and L sum to above 1.working data(Group5)
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The document contains 6 tables summarizing the results of regressions analyzing factors that influence CO2 emissions:
Table 1.1 shows a regression of logCO2 on log population and log vehicle density. Tables 1.2-1.6 report additional results including mean error terms, correlation of error terms and variables, variance inflation factors, heteroskedasticity and normality tests.
Table 2.1 expands the regression to include an indicator for countries with high GDP. Tables 2.2-2.6 report similar additional results as the first model.
Table 3.1 includes dummy variables for regions. Tables 3.2-3.6 report test results.
Table 4.1 further expandsEconometrics Project
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The study examines the effect of inflation, investment, life expectancy and literacy rate on per capita GDP across 20 countries using ordinary least squares regression. Initially, the regression results show inflation, investment and literacy rate have a negative effect, while life expectancy has a positive effect on per capita GDP. Sri Lanka, USA and Japan are identified as potential outliers based on their high residuals. Running the regression after removing these outliers improves the model fit and explanatory power of the variables. Diagnostic tests find no evidence of misspecification or heteroskedasticity, validating the OLS estimates.Durbib- Watson D between 0-2 means there is a positive correlati
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Durbib- Watson D between 0-2 means there is a positive correlation at 92% of 1st Order Correlation(1 time unit lag).
The summary shows a linear regression of CO2 emissions vs time. The p-value(<0.05) suggests that the model is significant. The model also defines 68% of the variability in data(R-square=0.6844). The equation for our regression model will be:
CO2 emissions = 0.00002008*Date+0.11162
The residual values doesnt seem random but normal. We can also see that for higher values of CO2 emissions (>0.5) the variance increases. So, we will check a squared model to see if that explains the data prediction better.
The squared regression shows better R square of 78% and p-values show that Date and Date^2 coefficients are significant but intercept is not. New equation will be:
CO2 emissions = 5.9E-10*(Date^2) +0.0000246*Date-0.009480
Now we will run the time series using ARIMA that includes Auto Regression and Moving Average. To run an ARIMA model we need to define p (lags in Auto Regression), d (non-seasonal difference) and q (lagged forecast errors in Moving Average). These attribute will be defined by checking for seasonality, ACF and PACF plots.
The Autocorrelation check is used to test for white noise. If the p-value is significant, we can say that the data is correlated else the data is independent. Here the tables shows the autocorrelations at different lags and p-values suggest that the data is correlated.
The ACF and PACF plots are used to identify p (lag for auto regression) and seasonality. We can see that ACF plot starts with a positive value and then continues with negative values till 12. But there is no pattern following.
So, we can say that AR is explained very well using lag-1. Also, the PACF plot cuts off at 2. We will iterate through different pdq values and get the best estimates with lowest AIC score.
The pdq (2,1,1) shows better AIC -2426 compared to other pdq values as well as squared regression. The p-value <0.05 also signifies that the parameters we have selected are good. We will predict using these parameters
The distribution of residuals are normal unlike regression and squared regression
The tables show the equation for Autoregression and Moving Average prediction of ARIMA model
This tables shows the forecast of next 12 months of data
Graphical Forecast highlighted by line at the end and connected with the existing data. So this plot shows the complete trend of historical data+predicted data
The last table shows the outliers with row number and values of the observations.
Monday, 21 June 2021 00:08:42 1
Model: MODEL1
Dependent Variable: CO2
Number of Observations Read 1680
Number of Observations Used 1680
Analysis of Variance
Source DF
Sum of
Squares
Mean
Square F Value Pr > F
Model 0 0 . . .
Error 1679 215.11655 0.12812
Corrected Total 1679 215.11655
Root MSE 0.35794 R-Square 0.0000
Dependent Mean 0.03483 Adj R-Sq 0.0000
Coeff Var 1027.75772
Parameter Estimates
Variable D ...Lampiran uji kointegrasi
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This document reports the results of cointegration tests between various monetary aggregates (GM1, GM2) and their determinants for both conventional and Islamic monetary systems in Indonesia from 1997 to 2003. The tests show that:
1. The monetary aggregates are cointegrated with their determinants, indicating they help control monetary policy.
2. The monetary aggregates are linked to the main objectives of monetary policy.
3. Credit instruments are cointegrated with the monetary aggregates, though the relationship is stronger for Islamic than conventional aggregates.Regression analysis
Regression analysisArun Prasad R
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The document appears to be an analysis of a dataset containing consumption, income, and liquid asset values for multiple observations.
(i) A regression model was developed relating consumption to income and liquid assets. High R-squared and variance inflation factors indicate potential multicollinearity between the predictors.
(ii) Additional analyses excluding variables one at a time were performed, continuing to show high correlation between predictors and potential multicollinearity issues.
(iii) A Theil's inequality coefficient calculation further supported the existence of multicollinearity within the dataset.Lampiran error correction model
Lampiran error correction modelChenk Alie Patrician
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The document contains analyses of monetary policy transmission mechanisms in Indonesia using error correction models. It estimates relationships between monetary aggregates and their determinants, as well as inflation, for both conventional and Islamic monetary frameworks. The analyses find that monetary aggregates respond to their determinants and influence inflation in both frameworks, though the effects are stronger under the Islamic framework based on the higher R-squared values.
Data equation 
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The document contains economic data from Indonesia from 2000 to 2012 including GDP, current expenditures, development expenditures, and income tax. A regression analysis was conducted to analyze the relationship between the variables. It found GDP to be positively correlated with income tax and negatively correlated with current and development expenditures. Diagnostic tests showed the model was a good fit with no multicollinearity or normality issues.Appendix
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This document contains 4 tables summarizing economic indicators in North Sumatra, Indonesia from 1989 to 2008. Table 1 shows annual data on employment, inflation rate, domestic investment, and foreign direct investment. Tables 2, 3, and 4 show the results of linear regression models analyzing the relationships between employment, inflation, domestic investment, and foreign direct investment. The regressions found relationships between employment and foreign direct investment, but not domestic investment or inflation.
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The document is a letter from the Khoa Th動董ng m畉i- Du l畛ch- Marketing (Faculty of Commerce - Tourism - Marketing) at the University of Economics Ho Chi Minh City. It assigns Nguy畛n Th畛 Kim Hoa of class TM01 the topic "Business Negotiation by Letter" to complete under the supervision of Professor on Th畛 H畛ng V但n. The letter provides Kim Hoa with her topic and supervisor for her assigned work.Similar to Minhhoa bai giang kinh te luong (20)
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This document contains statistical summaries of closing prices and trading volumes for stocks from 2003 to 2011. It includes measures like mean, median, maximum, minimum, standard deviation, skewness, kurtosis, and observations. It also shows the results of a least squares regression model analyzing the relationship between closing price (dependent variable) and volume with an R-squared of 0.250886. A second regression analyzes the relationship between closing price and dividends from 2006 to 2010 with an R-squared of 0.414438.Output
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This document contains the results of three regressions analyzing the relationship between poverty (dependent variable) and income inequality and growth (independent variables) in 8 cross-sections over the period of 2002-2012. The normal regression shows a negative relationship between poverty and growth but the results are not statistically significant. The fixed effects regression finds a positive relationship between poverty and income inequality and negative relationship with growth. The random effects regression finds a negative relationship between poverty and growth but the other coefficients are not statistically significant.Lampiran error correction model
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1. The document analyzes conventional and Islamic monetary policy models in Indonesia from 1997 to 2003 using error correction models. It finds that changes in monetary aggregates (M1 and M2) are positively correlated with prior changes in the monetary base for both conventional and Islamic models.
2. Monetary aggregates are also found to be correlated with the main objective of monetary policy, inflation. Lags of changes in M1 and M2 are found to influence current inflation in both conventional and Islamic models.
3. Credit instruments are also analyzed, finding that lags of changes in credit are negatively correlated with the current change in credit for both conventional and Islamic models, indicating credit adjusts over time based on prior levels.Cobb-douglas production function
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The document describes the Cobb-Douglas production function and the results of estimating its parameters. It finds that labor (L) and capital (K) explain 95% of the variation in output (Q) according to the estimated equation Q=-0.31+0.20K+0.95L. Diagnostic tests show the data and estimated model meet the assumptions of regression analysis. Specifically, the variables are stationary and there is no multicollinearity while the model and estimated coefficients are statistically significant with a goodness of fit of 95%. Therefore, the Cobb-Douglas production function appropriately captures the relationship between L, K, and Q in the data.Cobb-douglas production function
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The document describes the Cobb-Douglas production function and the results of estimating its parameters. It finds that labor (L) and capital (K) explain 95% of the variation in output (Q) according to the estimated equation Q=-0.31+0.20K+0.95L. Diagnostic tests show the variables are stationary and there is no multicollinearity. The model is found to be statistically significant and a good fit to the data based on the F-statistic and R-squared value. It is determined that industry exhibits increasing returns to scale since the estimated coefficients of K and L sum to above 1.working data(Group5)
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The document contains 6 tables summarizing the results of regressions analyzing factors that influence CO2 emissions:
Table 1.1 shows a regression of logCO2 on log population and log vehicle density. Tables 1.2-1.6 report additional results including mean error terms, correlation of error terms and variables, variance inflation factors, heteroskedasticity and normality tests.
Table 2.1 expands the regression to include an indicator for countries with high GDP. Tables 2.2-2.6 report similar additional results as the first model.
Table 3.1 includes dummy variables for regions. Tables 3.2-3.6 report test results.
Table 4.1 further expandsEconometrics Project
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The study examines the effect of inflation, investment, life expectancy and literacy rate on per capita GDP across 20 countries using ordinary least squares regression. Initially, the regression results show inflation, investment and literacy rate have a negative effect, while life expectancy has a positive effect on per capita GDP. Sri Lanka, USA and Japan are identified as potential outliers based on their high residuals. Running the regression after removing these outliers improves the model fit and explanatory power of the variables. Diagnostic tests find no evidence of misspecification or heteroskedasticity, validating the OLS estimates.Durbib- Watson D between 0-2 means there is a positive correlati
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Durbib- Watson D between 0-2 means there is a positive correlation at 92% of 1st Order Correlation(1 time unit lag).
The summary shows a linear regression of CO2 emissions vs time. The p-value(<0.05) suggests that the model is significant. The model also defines 68% of the variability in data(R-square=0.6844). The equation for our regression model will be:
CO2 emissions = 0.00002008*Date+0.11162
The residual values doesnt seem random but normal. We can also see that for higher values of CO2 emissions (>0.5) the variance increases. So, we will check a squared model to see if that explains the data prediction better.
The squared regression shows better R square of 78% and p-values show that Date and Date^2 coefficients are significant but intercept is not. New equation will be:
CO2 emissions = 5.9E-10*(Date^2) +0.0000246*Date-0.009480
Now we will run the time series using ARIMA that includes Auto Regression and Moving Average. To run an ARIMA model we need to define p (lags in Auto Regression), d (non-seasonal difference) and q (lagged forecast errors in Moving Average). These attribute will be defined by checking for seasonality, ACF and PACF plots.
The Autocorrelation check is used to test for white noise. If the p-value is significant, we can say that the data is correlated else the data is independent. Here the tables shows the autocorrelations at different lags and p-values suggest that the data is correlated.
The ACF and PACF plots are used to identify p (lag for auto regression) and seasonality. We can see that ACF plot starts with a positive value and then continues with negative values till 12. But there is no pattern following.
So, we can say that AR is explained very well using lag-1. Also, the PACF plot cuts off at 2. We will iterate through different pdq values and get the best estimates with lowest AIC score.
The pdq (2,1,1) shows better AIC -2426 compared to other pdq values as well as squared regression. The p-value <0.05 also signifies that the parameters we have selected are good. We will predict using these parameters
The distribution of residuals are normal unlike regression and squared regression
The tables show the equation for Autoregression and Moving Average prediction of ARIMA model
This tables shows the forecast of next 12 months of data
Graphical Forecast highlighted by line at the end and connected with the existing data. So this plot shows the complete trend of historical data+predicted data
The last table shows the outliers with row number and values of the observations.
Monday, 21 June 2021 00:08:42 1
Model: MODEL1
Dependent Variable: CO2
Number of Observations Read 1680
Number of Observations Used 1680
Analysis of Variance
Source DF
Sum of
Squares
Mean
Square F Value Pr > F
Model 0 0 . . .
Error 1679 215.11655 0.12812
Corrected Total 1679 215.11655
Root MSE 0.35794 R-Square 0.0000
Dependent Mean 0.03483 Adj R-Sq 0.0000
Coeff Var 1027.75772
Parameter Estimates
Variable D ...Lampiran uji kointegrasi
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油
This document reports the results of cointegration tests between various monetary aggregates (GM1, GM2) and their determinants for both conventional and Islamic monetary systems in Indonesia from 1997 to 2003. The tests show that:
1. The monetary aggregates are cointegrated with their determinants, indicating they help control monetary policy.
2. The monetary aggregates are linked to the main objectives of monetary policy.
3. Credit instruments are cointegrated with the monetary aggregates, though the relationship is stronger for Islamic than conventional aggregates.Regression analysis
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油
The document appears to be an analysis of a dataset containing consumption, income, and liquid asset values for multiple observations.
(i) A regression model was developed relating consumption to income and liquid assets. High R-squared and variance inflation factors indicate potential multicollinearity between the predictors.
(ii) Additional analyses excluding variables one at a time were performed, continuing to show high correlation between predictors and potential multicollinearity issues.
(iii) A Theil's inequality coefficient calculation further supported the existence of multicollinearity within the dataset.Lampiran error correction model
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The document contains analyses of monetary policy transmission mechanisms in Indonesia using error correction models. It estimates relationships between monetary aggregates and their determinants, as well as inflation, for both conventional and Islamic monetary frameworks. The analyses find that monetary aggregates respond to their determinants and influence inflation in both frameworks, though the effects are stronger under the Islamic framework based on the higher R-squared values.Data equation
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The document contains economic data from Indonesia from 2000 to 2012 including GDP, current expenditures, development expenditures, and income tax. A regression analysis was conducted to analyze the relationship between the variables. It found GDP to be positively correlated with income tax and negatively correlated with current and development expenditures. Diagnostic tests showed the model was a good fit with no multicollinearity or normality issues.Appendix
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This document contains 4 tables summarizing economic indicators in North Sumatra, Indonesia from 1989 to 2008. Table 1 shows annual data on employment, inflation rate, domestic investment, and foreign direct investment. Tables 2, 3, and 4 show the results of linear regression models analyzing the relationships between employment, inflation, domestic investment, and foreign direct investment. The regressions found relationships between employment and foreign direct investment, but not domestic investment or inflation.Lampiran uji kointegrasi
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Minhhoa bai giang kinh te luong
- 1. Dependent Variable: LUONGHANG Included observations: 20 Variable Coefficient Std. Error t-Statistic Prob. GIA -1.331220 0.086149 -15.45251 0.0000 QUANGCAO 3.578441 1.263821 2.831445 0.0115 C 18.57448 1.327863 13.98825 0.0000 R-squared 0.939080 Mean dependent var 15.30000 Adjusted R-squared 0.931913 S.D. dependent var 2.494204 S.E. of regression 0.650825 Akaike info criterion 2.116 Sum squared resid 7.200735 Schwarz criterion 2.265688 Log likelihood -18.1632 F-statistic 131.0274 Durbin-Watson stat 2.816702 Prob(F-statistic) 0.000000
- 2. Wald Test: Equation: EQ01 Null Hypothesis: C(1)= -1 F-statistic 14.78196 Probability 0.001298 Chi-square 14.78196 Probability 0.000121