econometric project

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hzr88

Economics

Abcott Institute

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I need help doing the first part of the project using Gretl.

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observation_date Disposable Income real interest rate 1 year real consumption 1985-01-01 5768.8 4.82351210 5029.959 1985-04-01 5881.0 4.74253160 5076.136 1985-07-01 5862.7 4.27875183 5172.550 1985-10-01 5924.6 4.24991710 5183.741 1986-01-01 6002.5 4.69799443 5229.041 1986-04-01 6079.3 4.85809973 5286.019 1986-07-01 6118.1 3.28548273 5379.527 1986-10-01 6127.4 2.62523037 5412.515 1987-01-01 6183.9 2.68752940 5417.904 1987-04-01 6125.7 2.48904733 5491.837 1987-07-01 6235.5 2.40726957 5554.512 1987-10-01 6319.6 3.16681363 5566.263 1988-01-01 6412.3 2.67632250 5664.593 1988-04-01 6478.4 2.94669133 5706.414 1988-07-01 6540.9 3.03894797 5757.120 1988-10-01 6602.7 3.14464353 5822.579 1989-01-01 6677.8 3.37649407 5849.592 1989-04-01 6658.2 4.33897030 5876.896 1989-07-01 6702.4 3.86855060 5933.790 1989-10-01 6759.2 3.90637900 5959.580 1990-01-01 6811.0 3.87371880 6009.735 1990-04-01 6858.4 4.18567797 6028.041 1990-07-01 6856.1 3.68167533 6051.922 1990-10-01 6797.5 2.24309040 6005.882 1991-01-01 6814.5 2.84223597 5983.298 1991-04-01 6864.4 3.17691737 6032.885 1991-07-01 6892.4 2.73314670 6062.964 1991-10-01 6949.7 1.95482623 6059.930 1992-01-01 7089.5 1.44294417 6173.393 1992-04-01 7159.9 1.49809730 6215.300 1992-07-01 7193.1 1.10201091 6281.923 1992-10-01 7225.7 0.91430723 6356.796 1993-01-01 7252.9 0.75000537 6381.168 1993-04-01 7275.3 0.85743360 6439.211 1993-07-01 7281.6 0.84058137 6510.874 1993-10-01 7332.2 0.73695630 6568.028 1994-01-01 7380.7 0.66690804 6643.151 1994-04-01 7455.0 1.36951563 6694.839 1994-07-01 7498.5 1.83579563 6745.887 1994-10-01 7606.1 2.02862067 6818.819 1995-01-01 7671.0 2.24947053 6835.791 1995-04-01 7692.8 3.06101230 6895.718 1995-07-01 7763.3 2.38563037 6958.210 1995-10-01 7808.7 2.63641577 7006.747 1996-01-01 7882.6 2.38525810 7071.369 1996-04-01 7954.9 2.42318850 7147.441 1996-07-01 8019.3 2.60201713 7190.713 1996-10-01 8062.0 2.32265390 7247.987 1997-01-01 8138.8 2.17722903 7324.388 1997-04-01 8210.9 2.65207710 7357.362 1997-07-01 8307.6 2.80907233 7482.713 1997-10-01 1998-01-01 1998-04-01 1998-07-01 1998-10-01 1999-01-01 1999-04-01 1999-07-01 1999-10-01 2000-01-01 2000-04-01 2000-07-01 2000-10-01 2001-01-01 2001-04-01 2001-07-01 2001-10-01 2002-01-01 2002-04-01 2002-07-01 2002-10-01 2003-01-01 2003-04-01 2003-07-01 2003-10-01 2004-01-01 2004-04-01 2004-07-01 2004-10-01 2005-01-01 2005-04-01 2005-07-01 2005-10-01 2006-01-01 2006-04-01 2006-07-01 2006-10-01 2007-01-01 2007-04-01 2007-07-01 2007-10-01 2008-01-01 2008-04-01 2008-07-01 2008-10-01 2009-01-01 2009-04-01 2009-07-01 2009-10-01 2010-01-01 2010-04-01 2010-07-01 8429.9 8607.4 8725.7 8813.6 8883.4 8980.4 8991.1 9059.8 9181.0 9341.5 9453.0 9575.4 9634.9 9713.4 9688.4 9899.8 9735.4 9963.9 10044.5 10059.5 10123.4 10117.6 10244.6 10424.1 10452.8 10499.2 10608.2 10684.3 10819.2 10688.3 10792.0 10837.1 10928.1 11147.9 11190.4 11212.7 11355.3 11439.4 11491.3 11511.2 11529.3 11548.8 11772.3 11537.5 11662.0 11636.7 11699.3 11557.4 11579.7 11667.4 11860.9 11936.6 2.44497387 2.62856940 2.72494557 2.28409893 2.16714473 2.10610570 2.70443527 2.63098597 2.08955297 2.65081010 2.55431553 3.19865843 2.70722900 3.19237153 1.90213187 1.17941495 0.62769968 0.90776111 0.36150419 0.14081937 -0.17994169 -0.18404371 -0.41644626 0.02025907 -0.32072253 -0.20112586 -0.63716278 -0.75682138 0.30751095 0.48485206 0.65849409 1.44594903 1.10665087 2.46307753 2.25684930 2.36254127 3.20885617 2.28604803 1.53388863 1.94817313 1.79837537 0.68387997 -0.22560473 -1.10238488 2.17588137 2.45034300 0.20863721 -0.98495101 -1.01170224 -1.16592762 -0.44433184 -0.29786953 7572.147 7648.741 7783.430 7884.929 7998.842 8080.996 8204.750 8296.988 8418.349 8545.639 8625.435 8708.074 8784.155 8816.014 8833.726 8864.465 9007.494 9027.548 9073.212 9136.709 9187.527 9232.782 9338.495 9470.541 9535.741 9624.649 9677.166 9790.836 9901.580 9964.496 10074.922 10158.513 10177.238 10288.217 10341.556 10407.966 10507.248 10572.053 10599.795 10670.506 10712.556 10697.867 10727.347 10644.668 10548.872 10522.083 10470.358 10540.769 10529.156 10590.177 10685.145 10761.376 2010-10-01 2011-01-01 2011-04-01 2011-07-01 2011-10-01 2012-01-01 2012-04-01 2012-07-01 2012-10-01 2013-01-01 2013-04-01 2013-07-01 2013-10-01 2014-01-01 2014-04-01 2014-07-01 2014-10-01 2015-01-01 2015-04-01 2015-07-01 2015-10-01 2016-01-01 2016-04-01 2016-07-01 2016-10-01 2017-01-01 2017-04-01 2017-07-01 2017-10-01 2018-01-01 2018-04-01 2018-07-01 2018-10-01 2019-01-01 2019-04-01 2019-07-01 2019-10-01 11980.3 12094.9 12064.1 12117.7 12152.3 12375.7 12486.4 12404.7 12750.9 12248.6 12339.8 12387.2 12424.7 12580.5 12747.1 12890.1 13069.5 13243.4 13280.1 13357.0 13439.8 13541.6 13519.5 13584.8 13656.9 13795.2 13931.9 14015.5 14084.2 14253.3 14373.0 14491.8 14599.1 14729.6 14679.3 14763.1 14850.5 -1.03488270 -1.33853780 -1.87851237 -1.53625992 -1.73031647 -1.52837420 -1.16123002 -1.50045833 -1.89573293 -0.52444344 -0.65569163 -1.03265036 -1.33583117 -1.20694756 -1.34408717 -1.69183160 -1.32109279 1.19416465 -1.00139055 -1.43790663 -0.77173496 -0.29339821 -0.97097101 -1.70712559 -1.92900585 -0.90382333 -0.09542070 -0.61079391 -1.54626170 -0.08816192 0.65689980 -0.30980604 0.13845352 2.28780981 2.27797392 0.66055224 -0.18442748 10827.332 10868.689 10879.873 10912.991 10931.828 11010.694 11030.026 11052.368 11096.365 11155.481 11172.647 11214.417 11304.300 11347.636 11453.014 11565.682 11694.917 11772.949 11852.652 11943.043 12003.146 12091.178 12152.627 12223.822 12283.076 12372.247 12430.081 12500.402 12631.980 12707.641 12816.396 12900.579 12955.501 12975.144 13088.759 13192.261 13248.981 discount rate 8.000 7.770 7.500 7.500 7.367 6.610 5.827 5.500 5.500 5.500 5.650 6.000 6.000 6.000 6.290 6.500 6.697 7.000 7.000 7.000 7.000 7.000 7.000 6.930 6.167 5.660 5.400 4.563 3.500 3.500 3.007 3.000 3.000 3.000 3.000 3.000 3.000 3.247 3.753 4.383 5.083 5.250 5.250 5.250 5.080 5.000 5.000 5.000 5.000 5.000 5.000 5.000 5.000 5.000 5.000 4.663 4.500 4.500 4.603 4.870 5.193 5.737 6.000 6.000 5.110 3.827 3.060 1.643 1.250 1.250 1.250 0.943 2.250 2.167 2.000 2.000 2.000 2.083 2.500 3.000 3.500 4.000 4.500 5.000 5.583 6.000 6.250 6.250 6.250 6.250 5.750 4.917 3.167 2.250 2.250 1.000 0.500 0.500 0.500 0.500 0.667 0.750 0.750 0.750 0.750 0.750 0.750 0.750 0.750 0.750 0.750 0.750 0.750 0.750 0.750 0.750 0.750 0.750 0.750 0.750 0.750 0.750 0.750 0.833 1.000 1.000 1.000 1.083 1.333 1.583 1.750 1.833 2.083 2.333 2.583 2.833 3.000 3.000 2.750 2.250 Professor: Sarah Carroll 2022 Week 5 & 6 project Econ 436 Summer Your project for this week is to address potential problems with your estimates for the aggregate consumption function for the U.S. economy for the period 1985–2019. 1. The residual plots of the interest rate showed a non-random process in the error terms. It looks like the spread around the mean of the error term is declining with higher interest rates. Include a residual plot and answer the following questions: a. How is this problem called? How will it affect the unbiasedness and efficiency of your estimator? Please describe! b. Please test your model for the problem described in a.. What is the intuition behind the test you chose? Please explain the process step by step. What is your conclusion? c. How can you overcome the problem described in a.? Please compare your results to the results you found without controlling for the problem described in a.? Does your estimated coefficient change? Does significance of the estimated coefficient change? 2. Your estimator for the interest rate is positive. Some groups found a significant coefficient for the interest rate; all groups found a positive effect of the interest rate on consumption. This result is contrary to economic theory. What could be the problem here? 3. The residual plots of Income showed a non-random process in the error terms. After some research in google and consulting with your econometrics Professor, you found out that this could be indicative of autocorrelation being present in the error term. You further noticed that both, Consumption, and Income seem to be growing at a constant rate which might be indicative of a trend in your data. a. Describe briefly why ignoring a trend in either the exogenous or the endogenous variables will lead to unreliable estimators. b. Describe 2 methods to account for a trend in your dataset c. Your quarterly data of income and consumption (monthly data) appears to show a pattern within the year. How would you control for that? 4. The residual plot of your model against time indicates that there might be a structural break in your model. a. Please indicate the time period where you seem to be able to see the structural break b. Re-estimate your model and test for a structural break. c. How would you resolve this issue? Please describe, execute and report your results. Your project for this week is to address potential problems with your estimates for the aggregate consumption function for the U.S. economy for the period 1985–2019. 1. The residual plots of the interest rate showed a non-random process in the error terms. It looks like the spread around the mean of the error term is declining with higher interest rates. Include a residual plot and answer the following questions: a. How is this problem called? How will it affect the unbiasedness and efficiency of your estimator? Please describe! b. Please test your model for the problem described in a.. What is the intuition behind the test you chose? Please explain the process step by step. What is your conclusion? c. How can you overcome the problem described in a.? Please compare your results to the results you found without controlling for the problem described in a.? Does your estimated coefficient change? Does significance of the estimated coefficient change? 2. Your estimator for the interest rate is positive. Some groups found a significant coefficient for the interest rate; all groups found a positive effect of the interest rate on consumption. This result is contrary to economic theory. What could be the problem here? 3. The residual plots of Income showed a non-random process in the error terms. After some research in google and consulting with your econometrics Professor, you found out that this could be indicative of autocorrelation being present in the error term. You further noticed that both, Consumption, and Income seem to be growing at a constant rate which might be indicative of a trend in your data. a. Describe briefly why ignoring a trend in either the exogenous or the endogenous variables will lead to unreliable estimators. b. Describe 2 methods to account for a trend in your dataset c. Your quarterly data of income and consumption (monthly data) appears to show a pattern within the year. How would you control for that? 4. The residual plot of your model against time indicates that there might be a structural break in your model. a. Please indicate the time period where you seem to be able to see the structural break b. Re-estimate your model and test for a structural break. c. How would you resolve this issue? Please describe, execute and report your results. Your project for this week is to address potential problems with your estimates for the aggregate consumption function for the U.S. economy for the period 1985–2019. 1. The residual plots of the interest rate showed a non-random process in the error terms. It looks like the spread around the mean of the error term is declining with higher interest rates. Include a residual plot and answer the following questions: a. How is this problem called? How will it affect the unbiasedness and efficiency of your estimator? Please describe! b. Please test your model for the problem described in a.. What is the intuition behind the test you chose? Please explain the process step by step. What is your conclusion? c. How can you overcome the problem described in a.? Please compare your results to the results you found without controlling for the problem described in a.? Does your estimated coefficient change? Does significance of the estimated coefficient change? 2. Your estimator for the interest rate is positive. Some groups found a significant coefficient for the interest rate; all groups found a positive effect of the interest rate on consumption. This result is contrary to economic theory. What could be the problem here? 3. The residual plots of Income showed a non-random process in the error terms. After some research in google and consulting with your econometrics Professor, you found out that this could be indicative of autocorrelation being present in the error term. You further noticed that both, Consumption, and Income seem to be growing at a constant rate which might be indicative of a trend in your data. a. Describe briefly why ignoring a trend in either the exogenous or the endogenous variables will lead to unreliable estimators. b. Describe 2 methods to account for a trend in your dataset c. Your quarterly data of income and consumption (monthly data) appears to show a pattern within the year. How would you control for that? 4. The residual plot of your model against time indicates that there might be a structural break in your model. a. Please indicate the time period where you seem to be able to see the structural break b. Re-estimate your model and test for a structural break. c. How would you resolve this issue? Please describe, execute and report your results.
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