Recommended
More Related Content
More from humphrieskalyn
More from humphrieskalyn (20)
Recently uploaded
Recently uploaded (20)
BUSI 405Research Paper Instructions1. Define the chosen variab.docx
- 1. BUSI 405 Research Paper Instructions 1. Define the chosen variable · New one-family houses sold (NHS) 2. Collect the data for the variable · Data source · www.economagic.com or other sources · Monthly data and not seasonally adjusted (NSA) · Time period – January 1975 to the present 3. Enter the data in Excel according to ForecastX format · 1/1/1975 for Jan. 1975 and 2/1/1975 for Feb. 1975 4. Plot the graph for whole series · ForecastX -- Preview 5. ACF for the whole series · ForecastX – Analyze · Identify the trend 6. ACF for the first differenced series · ForecastX – Analyze – Differencing – Non-Seasonal >1 · Identify seasonality 7. Decide the sample period for model selection · Historical period: 1/1/1975-12/1/2010 · Holdout period: 1/1/2011-12/1/2011 8. Select models according to the data pattern · Table 2.1, p. 58 · Time-series models: Modified naïve model, Winters’ exponential smoothing, Time-series Decomposition, and ARIMA · Regression mode: · NHS = f (IR, DPI, dummy variables)—need data for IR (30- year mortgage rate), DPI(disposable personal income) and dummy variables (for seasonality) 9. Perform estimation and forecasting for the data in the historical period, 1/2001 – 12/2010 · ForecastX – Forecast Method – choose models
- 2. · ForecastX – Statistics -- RMSE · For regression model, standard report shows the forecasts for independent variables 10. Compare MAPEs and RMSEs of different models for the historical and holdout periods 11. Perform ex-ante forecast – 11/2011-12/2012 · Use the whole series, 1/2001 – 10/2011 · Either choose the best model or combine two models Sheet1New One-Family Houses Sold (NHS): US - Thousands: NSA Dates Hutchins, Jessica Nicole: Hutchins, Jessica Nicole: Changed font to Times New Roman 12pt Font Hutchins, Jessica Nicole: Hutchins, Jessica Nicole: Added a Header and a FooterNHS (economagic.com)30-year Conventional Mortgage Rate (FRB of St. Louis)Disposable Personal Income (billion) SA (FRB of St. Louis) Hutchins, Jessica Nicole: Hutchins, Jessica Nicole: Stretched cells 1/1/75299.431120.82/1/75349.111126.33/1/75448.901130.64/1/ 75548.821156.05/1/75578.911229.26/1/75518.891193.77/1/7551 8.891186.08/1/75538.941200.19/1/75469.131210.410/1/75469.2 21223.311/1/75469.151231.212/1/75399.101239.51/1/76419.021 253.72/1/76538.811264.23/1/76558.761271.94/1/76628.731277. 25/1/76558.771284.56/1/76568.851291.57/1/76578.931306.48/1 /76599.001316.49/1/76588.981324.310/1/76558.931329.711/1/7 6498.811348.512/1/76478.791360.01/1/77578.721364.02/1/7768 8.671361.63/1/77848.691388.84/1/77818.751400.55/1/77788.82 1408.96/1/77748.861423.07/1/77648.941439.98/1/77748.941453 .39/1/77718.901467.610/1/77638.921488.311/1/77558.921506.7 12/1/77518.961517.41/1/78579.021519.92/1/78639.161535.93/1 /78759.201559.84/1/78859.361577.95/1/78809.581588.36/1/787
- 6. 61026.609853.26/1/06986.689916.47/1/06836.769939.38/1/0688 6.529985.89/1/06806.4010022.510/1/06746.3610061.111/1/0671 6.2410097.412/1/06716.1410175.21/1/07666.2210198.22/1/0768 6.2910252.93/1/07806.1610315.34/1/07836.1810340.25/1/07796 .2610363.96/1/07736.6610371.77/1/07686.7010413.68/1/07606. 5710436.99/1/07536.3810520.210/1/07576.3810558.511/1/0745 6.2110614.712/1/07446.1010697.01/1/08445.7610822.32/1/0848 5.9210880.73/1/08495.9710935.74/1/08495.9210913.25/1/08496 .0411482.66/1/08456.3211264.17/1/08436.4311147.58/1/08386. 4811047.49/1/08356.0411048.710/1/08326.2011006.611/1/0827 6.0910929.812/1/08265.3310815.41/1/09245.0610847.02/1/0929 5.1310746.33/1/09315.0010705.74/1/09324.8110763.05/1/09344 .8610923.96/1/09375.4210784.37/1/09385.2210743.58/1/09365. 1910747.19/1/09305.0610770.010/1/09334.9510753.311/1/0926 4.8810805.912/1/09244.9310875.91/1/10245.0310958.02/1/1027 4.9910983.33/1/10364.9711032.64/1/10415.1011108.15/1/10264 .8911166.06/1/10284.7411176.67/1/10264.5611208.58/1/10234. 4311258.59/1/10254.3511254.210/1/10234.2311310.911/1/1020 4.3011321.912/1/10234.7111377.31/1/11214.7611427.52/1/1122 4.9511483.53/1/11284.8411532.14/1/11304.8411550.85/1/11284 .6411559.76/1/11284.5111566.97/1/11274.5511573.28/1/11254. 2711563.59/1/11254.1111576.410/1/11254.0711603.611/1/1122 3.9911598.612/1/113.96 &"Times New Roman,Regular"&10BUSI 405 &"Times New Roman,Regular"&10Page &P of &N Sheet2 NHS (economagic.com)27395274262745427485275152754627576276 07276382766827699277292776027791278202785127881279122 79422797328004280342806528095281262815728185282162824 62827728307283382836928399284302846028491285222855028 58128611286422867228703287342876428795288252885628887 28915289462897629007290372906829099291292916029190292 21292522928129312293422937329403294342946529495295262
- 8. 76884817874647471635551576375858077687268705350535873 72686364686054413543444436445055615046393337404944453 83634282927292829363236343136394039334446575964595150 48514548525863615958524853554238485567606565636154525 14755598984756657526054484953597372625855565252433743 55686864655759545743425251586061586261495147404550585 25050464638373429304051504747434637413936485556535253 52565148424244506066585955575756535146587465655552595 45745404747605863646463545446455468707069656673625654 51616981707171697267626151647581828283757568697061677 68486808278786567615767788878777176737071636572859484 80797674666667666684908688848290827773707682989110110 79910590887675891021231091151059610294101848392109127 11612011511711099105868789881081001029883888074717166 68808379736860535745444448494949454338353227262429313 23437383630332624242736412628262325232023212228302828 2725252522 Sheet3 BUSI 405 Research Paper Grading Rubric Advanced Proficient Developing Knowledge (70 points) Demonstrates a broad base of general knowledge Demonstrates adequate general knowledge Demonstrates inadequate general knowledge Communication (50 points)
- 9. Demonstrates exemplary skills in writing Demonstrates sufficient skills in writing for effective classroom communication Demonstrates insufficient skills in writing for effective classroom communication Structure (30 points) TOPIC & DEVELOPMENT: Articulates a main point or topic; supports or amplifies thesis/topic sentence throughout; draws appropriate conclusions from evidence ORGANIZATION: Clear structure with appropriate transitions where needed PARAGRAPHS: Focused and well organized TOPIC & DEVELOPMENT: Generally articulates a main point or topic early on; mostly supports or amplifies thesis/topic sentence throughout; draws basically appropriate conclusions from evidence ORGANIZATION: Generally clear structure with appropriate transitions where needed PARAGRAPHS: Generally focused and well organized TOPIC & DEVELOPMENT: Contains a point, evidence, and conclusion, but does not necessarily put them in the proper order or make them clearly visible ORGANIZATION: Salvageable structure with some attempt at transition PARAGRAPHS: Mostly on topic though with some shifts and errors in focus Content
- 10. (25 points) FOCUS: Clear and coherent LOGIC: Clear and obvious flow of ideas EVIDENCE: Assertions fully and appropriately supported CONCEPT: Appropriate choice of topic, examples, and style PURPOSE: Meets assignment guidelines and addresses appropriate material FOCUS: Obvious focus, generally maintained LOGIC: Generally logical flow of thought EVIDENCE: Supports most assertions well CONCEPT: Generally appropriate choice of topic, examples, and style PURPOSE: Basically meets assignment guidelines and addresses appropriate material FOCUS: Weakly focused LOGIC: Some leaps in logic or fallacious reasoning EVIDENCE: Some assertions unsupported, or failure to distinguish between assertions and proof CONCEPT: Weak choice of topic, examples, and style PURPOSE: Does not meet assignment guidelines (but does not entirely fail to accomplish the objective) Readability
- 11. (25 points) MECHANICS: Contains only very minor grammar and punctuation errors or typos; errors must not seriously distract or harm clarity DICTION: Appropriate word usage for topic and assignment CLARITY: Easily understandable; clearly expressed MECHANICS: Contains some grammar and punctuation errors or typos; errors may distract but must not seriously harm clarity DICTION: Generally appropriate word usage for topic and assignment CLARITY: Generally understandable and clearly expressed MECHANICS: Contains significant and distracting grammar and punctuation errors or typos; errors may harm clarity (but not render the paper incomprehensible) DICTION: Some errors in word usage for topic and assignment CLARITY: Reader may have to work to understand; thought may seem muddled Total Points: /200 TIME SERIES MODELS FOR FORECASTING NEW ONE- FAMILY HOUSES SOLD IN THE U.S.Comment by InformationServices: The title
- 12. Andrew LightComment by InformationServices: name Liberty University INTRODUCTIONComment by InformationServices: Introduction The housing market has been weak since its recent peak in 2005. Then, the sharp drop in the housing prices in 2007 contributed to the subprime loan crisis [1]. This dramatic change in the housing market not only affects the construction industry, but also may have a significant impact on the whole economy [3]. We are still in the midst of the housing problem with the increase in the delinquency rate and foreclosure rate. In this paper, time-series models are specified to forecast new one-family houses sold in the U.S. Through this research, we try to predict the future development of this housing market. THE DATA PATTERN OF NEW ONE-FAMILY HOUSES SOLD IN THE U.S.Comment by InformationServices: The data pattern Figure 1 shows the time-series data for the non-seasonally adjusted monthly new one-family houses sold (NHS) in the U.S. from January 1975 to January 2010, the most recent data available. The period is chosen to be long enough to reveal the cyclical fluctuations in this market. A time series is normally contain some or all of the following components, (1) trend, the long-term change in the level of the data, (2) seasonality, a regular variation in the level of the data that repeats itself at the same time every year, (3) cycles, wavelike fluctuations of the data around the trend, and (4) irregularity, the remaining and random variation in the data. From the figure, we can see that the NHS data have significant seasonality, cycles, and upward trend till 2005. Comment by InformationServices: The figure for the dataComment by InformationServices: Sample periodComment by InformationServices: Describe the data pattern
- 13. FIGURE 1 New One-Family Houses Sold (NHS), in thousands, in the U.S. January 1975 to January 2010 Data source: National Association of Realtors In evaluating a time-series data, it is useful to analyze the autocorrelation which measures the correlation between successive observations over time. A k-period plot of autocorrelations is called an autocorrelation function (ACF). A 12-period plot of autocorrelation function for the NHS data and 24-period plot of ACF for the first differenced NHS data from January 1975 to June 2009, the historical period in our analysis, are shown in Figures 2 and 3. In figure 2, all ACFs are significantly different from zero because they are above the upper limit level and slowly decreasing, this implies there is a trend in the NHS data. In figure 3, ACFs for lag 12 and 24 are above the upper limit level which means they are significantly different from zero. This confirms seasonality in this monthly data.Comment by InformationServices: ACF analysis
- 14. FIGURE 2 12-period plot of autocorrelation functions (ACF) for NHS FIGURE 3 24-period plot of autocorrelation functions (ACF) for first differenced NHS TIME-SERIES MODELS FOR NEW ONE-FAMILY HOUSES SOLDComment by InformationServices: Time-series model selection based on the data pattern. You can also include a regression model in your paper. Since the NHS data have trend and seasonality, three time- series models are chosen for estimation and forecasting purposes: Winter’s exponential smoothing, the decomposition, and the autoregressive integrated moving average (ARIMA) [4]. The data from January 1975 to June 2009 as the historical period are used for model specification. The next six months from July 2009 to December 2009 are chosen as the holdout period in which the actual data are available so we can compare with ex-post forecast to evaluate the accuracy of models. Comment by InformationServices: Separate the historical and holdout periods Excel-based Forecast X software is used for estimation and forecasting [2]. In order to compare how good the estimated values fit the original data, two error measurements, the mean absolute percentage error (MAPE) and the root-mean-squared error (RMSE) are calculated and shown in table 1. To obtain
- 15. MAPE, we first divide the forecast error or error (actual value – forecast value) by the actual value to yield the percentage error and then calculate the mean of the absolute percentage errors. For RMSE, we first square the forecast error and then take the squared root of the mean of the squared errors.Comment by InformationServices: Error measurements TABLE 1 MAPE (mean absolute percentage error) and RMSE (root-mean- squared error) Models Historical period Holdout period Jan. 1975-June 2009 July 2009-Dec. 2009 MAPE RMSE RMSE/Mean* MAPE RMSE RMSE/Mean* Winter’s exponential smoothing 6.46% 4.68 7.61% 8.08% 2.68 8.61% Decomposition with exponential smoothing trend 5.27% 3.97 6.46% 6.72%
- 16. 2.54 8.16% ARIMA(1,0,0)(2,0,0) 6.94% 5.08 8.27% 8.56% 2.85 9.14% *Mean of NHS for the historical period is 61.46 and for the holdout period is 31.17 The best model should be the one with the smallest error. Among these three time-series models, the decomposition with exponential smoothing trend has the smallest MAPE and RMSE for both historical period and holdout period. Therefore, we use this model and the data from January 1975 to December 2009 to perform the ex-ante point and interval forecasts for the first six months of 2010 as show in table 2 and figure 4. Comment by InformationServices: Choose the best model and all data to perform the forecast TABLE 2 Point and interval (5% to 95%) forecasts of NHS for the first six months of 2010Comment by InformationServices: Show point and interval forecasts based on executive report Month Point forecast 5% 95% January 27.56 21.27
- 17. 33.84 February 31.21 24.92 37.49 March 36.46 30.17 42.75 April 35.15 28.86 41.44 May 34.92 28.63 41.21 June 33.90 27.61 40.19 FIGURE 4 Actual and forecast values of NHS IS THE WORST OVER? People would like to know whether the worst of this housing market downturn is over. One tool we can use to somehow predict the turning points of a cycle is by analyzing cyclical factors (CF) of the data. Estimated (July 1995 to December 2009) and forecasted (January 2010 to June 2010) cyclical factors based on the decomposition model are presented in figure 5. As we can see this CF time series does not show any trend or seasonality. In other words, the only time-series
- 18. component shown is the cycle. According to this figure, it seems that the new one-family housing market has passed the trough of this cycle in 2008 and is recovering.Comment by InformationServices: Analyze cyclical factors using decomposition model. FIGURE 5 Cyclical factors CONCLUSIONComment by InformationServices: Conclusion What is the outlook for the housing market in the near future and will this problem be over soon? In this paper, we focus on new one-family houses sold in the U.S. Based on our forecast, it seems that the worst of this market is over and it is on the way to recovery. BUSI 405
- 19. APPENDIXComment by InformationServices: Forecast X output of the selected modelForecastX output for forecasting NHS for the second half of 2010 based on the decomposition model with exponential smoothing trend. Forecast -- Decomposition Selected Forecast 95% - 5% 95% - 5% Date Monthly Quarterly Annual Upper Lower Jan-2010
- 21. 34.92 41.21 28.63 Jun-2010 33.90 103.97 40.19 27.61 Accuracy Measures Value Forecast Statistics Value Mean Absolute Percentage Error (MAPE) 5.28% Mean
- 22. 61.03 R-Square 95.86% Standard Deviation 19.51 Root Mean Square Error 3.96 Method Statistics Value Method Selected Decomposition
- 23. Basic Method Exponential Smoothing Alpha 1.00 Decomposition Type Multiplicative
- 24. REFERENCESComment by InformationServices: References [1] DiMartino, D. and Duca, J, (2007, Nov. 11). The Rise and Fall of Subprime Mortgages, Economic Letter- Insights from the Federal Reserve Bank of Dallas. [2] Forecast X 6.0. John Galt
- 25. Solution s, Inc. [3] Wheelock, D. (2007) Housing Slump Could Lean Heavily on Economy. Federal Reserve Bank of St. Louis. [4]Wilson, H. and Keating, B. (2007) Business Forecasting. New York, NY: McGraw Hill/Irwin.