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GDP nowcasting using high frequency asset price, commodity price and banking data

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(1)GDP NOWCASTING USING HIGH FREQUENCY ASSET PRICE, COMMODITY PRICE AND BANKING DATA. A Master’s Thesis. by BI·NNUR BALKAN. Department of Economics I·hsan Do¼ gramac¬Bilkent University Ankara June 2011.

(2) GDP NOWCASTING USING HIGH FREQUENCY ASSET PRICE, COMMODITY PRICE AND BANKING DATA. Graduate School of Economics and Social Sciences of · Ihsan Do¼ gramac¬Bilkent University by BI·NNUR BALKAN. In Partial Ful…llment of the Requirements For the Degree of MASTER OF ARTS. in. THE DEPARTMENT OF ECONOMICS ¼ I·HSAN DOGRAMACI BILKENT UNIVERSITY ANKARA. June 2011.

(3) I certify that I have read this thesis and have found that it is fully adequate, in scope and in quality, as a thesis for the degree of Master of Arts in Economics.. ——————————————————– Assoc. Prof. Dr. Refet S. Gürkaynak Supervisor. I certify that I have read this thesis and have found that it is fully adequate, in scope and in quality, as a thesis for the degree of Master of Arts in Economics.. —————————————————– Assist. Prof. Dr. Taner Yi¼ git Examining Committee Member. I certify that I have read this thesis and have found that it is fully adequate, in scope and in quality, as a thesis for the degree of Master of Arts in Economics.. —————————————— Assoc. Prof. Dr. Asl¬han Altay Salih Examining Committee Member. Approval of Graduate School of Economics and Social Sciences. ————————— Prof. Dr. Erdal Erel Director.

(4) ABSTRACT GDP NOWCASTING USING HIGH FREQUENCY ASSET PRICE, COMMODITY PRICE AND BANKING DATA BALKAN, Binnur M.A., Department of Economics Supervisor: Assoc. Prof. Dr. Refet Gürkaynak June 2011 Knowing the current state of the economy is important especially when we consider that GDP information comes with a lag of quarter. From this perspective, employing high frequency variables in GDP nowcasting may contribute to our knowledge of economic conditions, since they are timelier compared to GDP. This paper deals with nowcasting US GDP using an expectation maximization algorithm in a Kalman …lter estimation, which includes asset prices, commodity prices and banking data as explanatory variables together with real variables and price indices. As a result of the estimations, asset prices and other high frequency variables are found useful in nowcasting US GDP contrary to previous studies. Model predictions beat the traditional methods with the medium size model, which includes …fteen variables, yielding the best nowcast results. Finally, this paper also proposes a new route for achieving better nowcast results by changing system speci…cations of the state variables.. Keywords: Nowcasting, Kalman Filter, EM Algorithm, Asset Prices iii.

(5) ÖZET VARLIK FI·YATLARI, EMTI·A FI·YATLARI VE BANKACILIK VERI·LERI· KULLANILARAK GSYH’NIN S ¸I·MDI·KI· ZAMAN TAHMI·NI· BALKAN, Binnur Yüksek Lisans, Ekonomi Bölümü Tez Yöneticisi: Doç. Dr. Refet Gürkaynak Haziran 2011 GSYH’nin bir çeyreklik gecikme ile ö¼ grenildi¼ gi göz önünde bulunduruldu¼ gunda, ekonominin bulundu¼ gu durum ile ilgili gerçek zamanda do¼ gru bilgi sahibi olman¬n önemi bir kez daha ortaya ç¬kmaktad¬r. GSYH’nin ¸simdiki zaman tahminine yüksek frekansl¬ varl¬k …yatlar¬n¬, emtia …yatlar¬n¬ ve bankac¬l¬k verilerini koymak ekonominin güncel durumu hakk¬ndaki bilgimize katk¬sa¼ glayabilir. Bu çal¬¸smada Amerika Birle¸sik Devletleri GSYH’sinin tahmininde bahsi geçen yüksek frekansl¬ veriler, dü¸sük frekansl¬ reel veriler ve …yat endeksleri ile birlikte bir Kalman …ltresi içerisinde beklenti maksimizasyonu algoritmas¬ ile kullan¬lm¬¸st¬r. Önceki çal¬¸smalar¬n tersine, yüksek frekansl¬ verilerin GSYH’nin e¸s zamanl¬ tahmininde hatay¬ azaltt¬g¼¬ bulunmu¸stur. Farkl¬ modeller aras¬nda, on be¸s farkl¬ seri içeren orta ölçekli model di¼ ger modellerden ve geleneksel tahmin metodlar¬ndan daha iyi sonuçlar vermi¸stir. Bunlara ek olarak, bu çal¬¸sma farkl¬model kurgular¬kullanarak daha iyi tahminlerine ula¸sman¬n mümkün oldu¼ gunu göstermi¸stir.. Anahtar Kelimeler: Simdiki Zaman Tahmini, Kalman Filtresi, Beklenti Maksimizasyonu Algoritmas¬, Varl¬k Fiyatlar¬ iv.

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(17) TABLE OF CONTENTS  . ABSTRACT ........................................................................................ iii ÖZET ................................................................................................... iv ACKNOWLEDGEMENTS ............................................................... v TABLE OF CONTENTS .................................................................. vi LIST OF TABLES ............................................................................ vii LIST OF FIGURES ......................................................................... viii CHAPTER 1: INTRODUCTION ...................................................... 1 CHAPTER 2: DATA AND MODELING FRAMEWOK ............... 5 2.1. Data Description ........................................................................ 5 2.2. State Space Representation ........................................................ 6 2.3. Modeling Quarterly and Daily Variables .................................. 8 CHAPTER 3: METHODOLOGY AND ESTIMATIONS ............. 9 3.1. Signal Extraction ........................................................................ 9 3.2. Estimation .................................................................................. 9 CHAPTER 4: RESULTS ................................................................. 11 4.1. Model Predictions and Nowcast Uncertainty .......................... 11 4.2. Gains from Asset Prices and Other Non-Real Variables ......... 12 4.3. Robustness ............................................................................... 13 CHAPTER 5: CONCLUSION AND FUTURE WORK ............... 16 BIBLIOGRAPHY ............................................................................. 18 APPENDICES ................................................................................... 19 A. Complete Data Set ..................................................................... 19 B. Estimation Results ...................................................................... 23 vi   .

(18) LIST OF TABLES     . 1. Appendix A: Complete Data List ........................................................................ 19  2. Appendix B: Predictions in Billion Dollars ......................................................... 22  3. Appendix B: Squared Forecast Errors ................................................................ 26  4. Appendix B: SFE Comparison for AR(3) Process ................................................ 27  5. Appendix B: SFE Comparison for 1994 Vintage ................................................. 28   .   vii  .

(19) LIST OF FIGURES    . 1. Appendix B: US GDP Nowcast ........................................................................ 24 2. Appendix B: 2008 Crisis US GDP Nowcasts ................................................... 25  .  viii  .

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(101) BIBLIOGRAPHY. Aruoba, S. B., Diebold, F. X., Scotti, C. (2009), "Real Time Measurement of Business Conditions", Journal of Business & Economic Statistics, 27, 417- 427. Aruoba, S. B., Diebold, F. X. (2010), "Real-Time Macroeconomic Monitoring: Real Activity, Inflation and Interactions", NBER Working Papers 15657. Banbura, M., Giannone, D., Reichlin, L.(2010a), "Nowcasting", CEPR Discussion Papers 7883. Banbura, M. and Modugno, M. (2010b), "Maximum Likelihood Estimation of Factor Models on Data Sets with Arbitrary Pattern of Missing Data", NBER Working Papers 1189. Forni, M., Hallin, M., Lippi, M., Reichlin, L. (2005), "Do financial variables help forecasting real activity and inflation in the euro area?", Journal of Monetary Economics, 50, 1243-1255. Hamilton, J.D. (1994), "State-Space Models", Handbook of Econometrics, 4, 3041 3068. Stock, J.H., and Watson, M. W. (2000), "Forecasting Output and Inflation: The Role of Asset Prices", Journal of Economic Literature, 41(3), 788-829. Stock, J. H., and Watson, M. W. (1989), "New Indexes of Coincident and Leading Economic Indicators", NBER Macroeconomics Annual, 4, 351-394. 18.

(102) 19. Daily. Federal Funds Rate. Asset Price Asset Price Asset Price Asset Price Asset Price Asset Price Asset Price Asset Price Asset Price Asset Price Asset Price Asset Price. Daily Daily Daily Daily Daily Daily Daily Daily Daily Daily Daily Daily. Bank Prime Loan. Discount Window Primary Credit. Discount Window Borrowing. 1 Year T-Bill Secondary Market. 6 Months T-Bill Secondary Market. 4 Week T-Bill Secondary Market. 30 year nominal treasury. 20 year nominal treasury. 10 Year Nominal Treasury. 7 Year Nominal Treasury. 5 Year Nominal Treasury. 3 Year Nominal Treasury. Asset Price. Frequency Group. Short Name. Complete Data List in Frequency Order. APPENDIX A. Small. X. X. X. X. Medium Large.

(103) 20. Money Money Money Money Money Asset Price Asset Price Real Real Real Real Real Real Real Real Real Commodity Real Price. Daily Daily Daily Weekly Weekly Weekly Weekly Weekly Weekly Biweekly Biweekly Monthly Monthly Monthly Monthly Monthly Monthly Monthly Monthly Monthly Monthly Monthly Monthly Monthly Monthly. 6 Months Nominal Treasury. 3 Months Nominal Treasury. SP500. US Regular All Formulations Gas Price. US Diesel Sales Price. Initial Jobless Claims. M1 Money Stock. Currency Componenet of M1 billions of dollar. M2 Money Stock billions of dollar. M1 Money Multiplier. St. Louis Adjusted Monetary Base. Corporate bonds/ Moody's seasoned Aaa. Corporate bonds/ Moody's seasoned Baa. Capacity Utilization. Manufacturing (SIC). Manufacturing (NAICS). Durable Manufacturing (NAICS. Wood Product (NAICS). Non-Metalic Mineral Product. Primary Metal. Seasonally Adjusted Industrial Production Index (2007=100). SA Real Retail and Food Services Sales (million dollars). Spot Oil Price: West Texas Intermediate (dollar per barrel). Manufacturer's Shipments, Inventories, and Orders. University of Michigan Inflation Expectation. Real. Commodity. Commodity. Asset Price. Asset Price. Asset Price. Asset Price. Daily. 1 Year Nominal Treasury. Asset Price. Daily. 2 Year Nominal Treasury. X. X. X. X. X. X. X. X. X. X. X. X. X. X. X. X. X. X. X. X. X.

(104) 21. Real Price Price Price Price. Real Real Banking Money Money Banking Price Price Real Real Real Real Real Real Real Real. New Private Housing Units Authorized by Building Permits (thousands Monthly. Consumer Price Index for All Urban Consumers: All Items (82-84=100 Monthly. Consumer Price Index for All Urban Consumers: Energy (82-84 =100) Monthly. Consumer Price Index for All Urban Consumers: Housing (82-82 =100Monthly Monthly Monthly Monthly Monthly Monthly Monthly Monthly. Monthly Monthly Monthly Monthly Monthly Quarterly Quarterly Quarterly Quarterly Quarterly Quarterly Quarterly Quarterly. Job Openings: Total Nonfarm in thousands. Total Nonfarm Private Payroll Employment in thousands. Civilian Unemployment Rate. Average (Mean) Duration of Unemployment. Total Nonfarm Payrolls: All Employees in thousands. Average Hourly Earnings: Total Private Industries. Average Weekly Hours of Production and Nonsupervisory Employees:Monthly Monthly. Consumer Price Index for All Urban Consumers: Food 82-84=100. Total Checkable Deposits. Currency Component of M1 billions of dollars. M2 Money Stock billions of dollars. Savings Deposits - Total billions of dollars. Producer Price Index: All Commodities 82=100. Producer Price Index: Finished Energy Goods. Seasonally Adjusted GDP billion dollars. Personal consumption expenditures. Durable goods. Nondurable goods. Fixed investment. Gross private domestic investment. Services. Goods. Real. Real. Real. Real. Real. Real. Monthly. Housing Starts: Total: New Privately Owned Housing Units Started. X. X. X. X. X. X. X. X. X. X. X. X. X. X. X. X. X.

(105) 22. Quarterly. Equipment and software. Banking Quarterly Quarterly Quarterly Quarterly Quarterly. Nonperforming Total Loans (ratio to total loans). Net Interest Margin for all U.S. Banks. Velocity of M2 Money Stock. Velocity of M1 Money Stock. Total Net Loan Charge-offs. Banking. Money. Money. Banking. Banking. Price. Real. Real. Real. Real. Real. Real. Real. Real. Real. Real. Real. Real. Real. Real. Real. Real. Real. Real. Household Debt Service Payments as Percent of Disposable Persona Quarterly. Quarterly. House Price Index (Not a Press Release) (index: 1980 Q1=100). Quarterly. State and local. Quarterly. Quarterly. Nondefense. Federal Government Debt: Total Public Debt (million dollars). Quarterly. Quarterly. National defense. Federal. Quarterly. Quarterly. Services. Government consumption expenditures and gross investment. Quarterly. Goods. Quarterly. Services Quarterly. Quarterly. Goods. Imports. Quarterly. Quarterly. Quarterly. Exports. Net exports of goods and services. Change in private inventories. Quarterly. Quarterly. Structures. Residential. Quarterly. Nonresidential. X. X. X.

(106) APPENDIX B. 9/30/1999 12/31/1999 3/31/2000 6/30/2000 9/30/2000 12/31/2000 3/31/2001 6/30/2001 9/30/2001 12/31/2001 3/31/2002 6/30/2002 9/30/2002 12/31/2002 3/31/2003 6/30/2003 9/30/2003 12/31/2003 3/31/2004 6/30/2004 9/30/2004 12/31/2004 3/31/2005 6/30/2005 9/30/2005 12/31/2005 3/31/2006 6/30/2006 9/30/2006 12/31/2006 3/31/2007 6/30/2007 9/30/2007 12/31/2007 3/31/2008 6/30/2008. Predictions in Billion Dollars Large Medium 9412 9419 9597 9609 9721 9727 9925 9932 10012 10012 10128 10119 10190 10169 10318 10295 10347 10320 10411 10386 10523 10502 10625 10609 10722 10707 10795 10777 10911 10890 11022 11001 11238 11222 11392 11384 11564 11562 11750 11746 11929 11925 12121 12119 12341 12342 12485 12486 12709 12705 12896 12886 13151 13143 13327 13317 13444 13431 13593 13579 13772 13753 13992 13967 14152 14123 14292 14256 14354 14304 14509 14446. 23. Small 9419 9609 9727 9932 10012 10119 10169 10295 10320 10386 10502 10609 10707 10777 10890 11001 11222 11384 11562 11746 11925 12119 12342 12486 12705 12886 13143 13317 13431 13579 13753 13967 14123 14256 14304 14446.

(107) 9/30/2008 12/31/2008 3/31/2009 6/30/2009 9/30/2009 12/31/2009 3/31/2010 6/30/2010 9/30/2010 12/31/2010 3/31/2011. 14529 14256 14116 14099 14169 14320 14479 14605 14764 14884 15344. 14488 14242 14099 14079 14148 14296 14455 14588 14748 14875 15154. 24. 14488 14242 14099 14079 14148 14296 14455 14588 14748 14875 15154.

(108) 25.

(109) 26.

(110) 9/30/1999 12/31/1999 3/31/2000 6/30/2000 9/30/2000 12/31/2000 3/31/2001 6/30/2001 9/30/2001 12/31/2001 3/31/2002 6/30/2002 9/30/2002 12/31/2002 3/31/2003 6/30/2003 9/30/2003 12/31/2003 3/31/2004 6/30/2004 9/30/2004 12/31/2004 3/31/2005 6/30/2005 9/30/2005 12/31/2005 3/31/2006 6/30/2006 9/30/2006 12/31/2006 3/31/2007 6/30/2007 9/30/2007 12/31/2007 3/31/2008 6/30/2008 9/30/2008 12/31/2008 3/31/2009 6/30/2009 9/30/2009 12/31/2009 3/31/2010 6/30/2010 9/30/2010 12/31/2010 SFE. Squared Forecast Erros Large Medium 49 196 144 1 36 324 49 289 0 25 81 121 441 16 529 36 729 225 625 169 441 9 256 49 225 25 324 100 441 4 441 49 256 1156 64 1089 4 1225 16 1024 16 625 4 676 1 1444 1 961 16 1369 100 900 64 1600 100 961 169 484 196 1089 361 1296 625 1681 841 1225 1296 1225 2500 576 3969 676 1681 9 196 2601 289 2401 400 1936 441 1089 576 361 576 81 289 81 256 9 81 196 Full 439.0217 688.7826 Pre-08 262.9706 637.2941 After-08 795.8182 858.1818. 27. Small 100 289 36 361 121 225 196 841 841 1225 2601 1521 1225 529 1024 1521 4489 3600 3136 2401 1681 1369 1521 361 961 729 1089 289 9 81 81 225 169 144 9 625 676 25 484 1024 729 400 196 16 16 49 853.0435 1029.147 385.4545.

(111) SFE Comparison for AR(3) Process. 9/30/1999 12/31/1999 3/31/2000 6/30/2000 9/30/2000 12/31/2000 3/31/2001 6/30/2001 9/30/2001 12/31/2001 3/31/2002 6/30/2002 9/30/2002 12/31/2002 3/31/2003 6/30/2003 9/30/2003 12/31/2003 3/31/2004 6/30/2004 9/30/2004 12/31/2004 3/31/2005 6/30/2005 9/30/2005 12/31/2005 3/31/2006 6/30/2006 9/30/2006 12/31/2006 3/31/2007 6/30/2007 9/30/2007 12/31/2007 3/31/2008 6/30/2008 9/30/2008 12/31/2008 3/31/2009 6/30/2009 9/30/2009 12/31/2009 3/31/2010 6/30/2010 9/30/2010 12/31/2010 Mean. Large Model 49 144 36 49 0 81 441 529 729 625 441 256 225 324 441 441 256 64 4 16 16 4 1 1 16 100 64 100 169 196 361 625 841 1296 2500 3969 1681 196 289 400 441 576 576 289 256 81 439.0217. Large Model AR(1) 0.9614 2.389 3.101 9.4362 6.3908 0.0002 51.1076 25.4089 50.798 44.6705 21.4387 18.6131 15.0327 22.3671 12.5371 8.2797 0.9074 1.9828 8.0227 10.8972 10.2537 19.6352 40.232 33.6816 41.4933 39.9688 64.025 57.5653 58.1687 60.9074 51.4664 76.8556 34.9417 4.8424 18.9039 7.6995 48.6003 376.19 247.0046 124.2067 74.815 43.1259 26.6537 20.1593 14.8386 17.1527 41.90716. Medium Model 196 1 324 289 25 121 16 36 225 169 9 49 25 100 4 49 1156 1089 1225 1024 625 676 1444 961 1369 900 1600 961 484 1089 1296 1681 1225 1225 576 676 9 2601 2401 1936 1089 361 81 81 9 196 688.7826. 28. Medium Model AR(1) 1.2132 2.7086 3.6531 6.3921 1.8493 3.4575 21.1799 5.4787 22.361 5.7618 2.5136 1.8426 0.9191 1.6837 0.6299 0.2044 0.7397 1.1493 3.8858 5.6063 5.425 10.0586 19.5441 18.8555 25.3284 24.9842 38.2821 31.0783 28.9303 29.772 29.3571 38.9983 27.9665 17.5519 5.5609 15.3142 1.8815 56.7538 74.3689 52.6955 40.332 24.3321 15.6661 13.0262 10.7606 13.0015 16.58816. Small Model 100 289 36 361 121 225 196 841 841 1225 2601 1521 1225 529 1024 1521 4489 3600 3136 2401 1681 1369 1521 361 961 729 1089 289 9 81 81 225 169 144 9 625 676 25 484 1024 729 400 196 16 16 49 853.0435. Small Model AR(1) 6.6064 23.1461 1.1045 38.6686 3.4393 5.9046 0.1906 42.8659 14.6676 36.3418 47.7564 23.0543 9.534 1.2074 9.3995 20.3927 100.9753 46.0882 31.5192 21.5476 8.4336 9.8459 18.0214 2.4562 3.7897 0.909 10.1835 1.6691 0.9943 3.2219 3.4731 7.6054 2.2446 0.4978 2.2001 0.7188 11.5057 15.6337 134.2274 131.6527 67.5606 45.7149 39.8371 23.5166 27.7789 3.877 23.0865.

(112) SFE Comparison for 1994 Vintage. 9/30/1999 12/31/1999 3/31/2000 6/30/2000 9/30/2000 12/31/2000 3/31/2001 6/30/2001 9/30/2001 12/31/2001 3/31/2002 6/30/2002 9/30/2002 12/31/2002 3/31/2003 6/30/2003 9/30/2003 12/31/2003 3/31/2004 6/30/2004 9/30/2004 12/31/2004 3/31/2005 6/30/2005 9/30/2005 12/31/2005 3/31/2006 6/30/2006 9/30/2006 12/31/2006 3/31/2007 6/30/2007 9/30/2007 12/31/2007 3/31/2008 6/30/2008 9/30/2008 12/31/2008 3/31/2009 6/30/2009 9/30/2009 12/31/2009 3/31/2010 6/30/2010 9/30/2010 12/31/2010 Mean. Large Model 49 144 36 49 0 81 441 529 729 625 441 256 225 324 441 441 256 64 4 16 16 4 1 1 16 100 64 100 169 196 361 625 841 1296 2500 3969 1681 196 289 400 441 576 576 289 256 81 439.0217. Large Model 1994 1.0412 4.6296 1.1402 9.5924 0.5729 6.3151 5.5656 8.5774 3.5785 8.2989 41.4361 8.0334 2.0456 29.0709 5.3033 50.406 150.4709 119.9627 105.438 47.8771 22.0121 36.1014 154.2534 73.4033 134.2989 44.6108 225.805 140.0232 103.4789 270.875 183.5125 187.9827 69.0955 6.312 169.4501 40.5717 0.0163 215.0391 514.7612 94.6817 8.3715 2.3839 0.0928 8.7203 0.0463 65.6886 73.49878. Medium Model 196 1 324 289 25 121 16 36 225 169 9 49 25 100 4 49 1156 1089 1225 1024 625 676 1444 961 1369 900 1600 961 484 1089 1296 1681 1225 1225 576 676 9 2601 2401 1936 1089 361 81 81 9 196 688.7826. 29. Medium Model 1994 0.9153 5.081 1.5545 12.823 1.1545 7.6127 4.9725 11.0824 0.7613 9.3062 46.346 6.682 12.5875 0.655 38.033 56.9222 240.7717 134.0221 111.1424 44.7439 6.5118 41.775 168.8882 62.1187 150.163 24.3144 175.6948 86.1399 38.8883 178.3953 186.8468 251.2374 177.811 122.1562 11.3995 1.0486 0.1397 3.9508 42.6666 28.0264 42.9852 25.9133 12.8408 21.7257 0.1082 41.8547 57.62542. Small Model 100 289 36 361 121 225 196 841 841 1225 2601 1521 1225 529 1024 1521 4489 3600 3136 2401 1681 1369 1521 361 961 729 1089 289 9 81 81 225 169 144 9 625 676 25 484 1024 729 400 196 16 16 49 853.0435. Small Model 1994 0.0573 2.9963 5.2573 31.2465 2.129 17.6165 0.0052 3.5067 1.951 4.6078 4.3735 2.2675 26.556 16.5831 13.988 117.9837 240.9853 26.5732 7.3533 10.0604 10.3059 26.9857 149.4721 0.5241 31.5295 7.1288 3.6976 7.7388 7.7722 93.3392 59.637 23.5184 0.809 5.1022 267.23 93.7469 6.3222 142.3127 585.64 145.2616 213.2888 77.7892 21.2586 6.8097 83.7512 7.458 56.83754.

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