Kết quả nghiên cứu trên cho thấy tương quan âm giữa thanh khoản cổ phiếu trên thị trường chứng khốn và đầu tư cơng ty (thể hiện qua tăng trưởng hàng tồn kho và tăng trưởng tài sản cố định) khơng giống như nghiên cứu của Moz (2012) một phần có thể do hạn chế về việc thu thập dữ liệu theo quý (bài nghiên cứu này thu thập dữ liệu theo năm). Với dữ liệu công ty và dữ liệu thanh khoản theo quý, những thay đổi có tính chất thời vụ hay có tính chất bất thường của thanh khoản sẽ được
54
phản ảnh lên đầu tư công ty. Tuy nhiên, đối với dữ liệu theo năm, các thay đổi mang tính chất thời vụ hoặc bất thường có thể bị làm phẳng nên kết quả hồi quy có thể khơng phản ảnh được bản chất của mối quan hệ này. Tuy nhiên, dữ liệu công ty của các cơng ty niêm yết ở thị trường chứng khốn Việt Nam theo quý không được thơng tin đầy đủ và khơng có tính tin cậy cao do khơng được kiểm tốn, vì vậy hạn chế về mặt dữ liệu hiện tại chưa thể khắc phục.
Mẫu dữ liệu quan sát là thời gian hậu khủng hoảng và hồi phục, sau khủng hoảng tài chính thế giới năm 2007. Trong đó, các cơng ty chịu ảnh hưởng trầm trọng về hoạt động sản xuất kinh doanh và đầu tư, thị trường chứng khoán chịu nhiều ảnh hưởng của việc dòng vốn ồ ạt rút khỏi thị trường gây biến động trong dòng vốn và tạo áp lực dẫn đến hành vi thiển cận của nhà quản lý.
Vấn đề có thể bỏ sót biến tương quan với cả biến thanh khoản cổ phiếu trên thị trường chứng khốn và biến đại diện đầu tư cơng ty. Theo Fang và cộng sự (2012), một số vấn đề nội sinh có thể phát sinh quan hệ nghịch đảo giữa thanh khoản và đầu tư, do thị trường thanh khoản cũng có thể chịu tác động ngược lại của đầu tư dự kiến. Vì thế, vấn đề nội sinh vẫn có thể diễn ra trong mơ hình và làm sai lệch kết quả, mặc dù nghiên cứu đã cố gắng giảm bớt vấn đề nội sinh.
Ngoài ra, khi xem xét các cơng ty có phát hành hay khơng phát hành cổ phiếu, nghiên cứu này chưa chú ý đến vấn đề việc phát hành cổ phiếu liệu có đem lại dịng tiền cho công ty để đầu tư hay không. Nếu việc phát hành cổ phiếu không đem lại dòng thu, nghiên cứu này cần phải loại trừ những trường hợp này để kết quả hồi quy không bị sai lệch.
TÀI LIỆU THAM KHẢO
Tài liệu tham khảo Tiếng Việt
Hoàng Ngọc Nhậm, 2008. Giáo trình kinh tế lượng, Bộ mơn Tốn – Khoa Toán Thống kê, Đại học Kinh tế Thành phố Hồ Chí Minh.
Nguyễn Thị Ngọc Trang, Trang Thúy Quyên, 2013. Mối quan hệ giữa sử dụng địn bẩy tài chính và quyết định đầu tư. Những vấn đề kinh tế - tài chính & tăng trưởng, Số 9 (19), P10-15.
Trần Ngọc Thơ, Nguyễn Thị Ngọc Trang, Phan Thị Bích Nguyệt, Nguyễn Thị Liên Hoa, Nguyễn Thị Uyên Uyên, 2007. Tài chính cơng ty hiện đại, NXB Thống kê, TP. HCM.
Trần Ngọc Thơ, Đặng Như Ý, 2015. Tác động của thanh khoản thị trường chứng khốn đến đầu tư của các cơng ty niêm yết: Bằng chứng tại Việt Nam. Tạp chí Phát triển Kinh tế, 26 (11), 63-79.
Võ Xuân Vinh, 2014. Cấu trúc sở hữu, hiệu quả hoạt động và giá trị cơng ty trên thị trường chứng khốn Việt Nam. Phát triển & Hội nhập, Số 16, 26-31.
Tài liệu tham khảo Tiếng Anh
Admati, Anat, Pfleiderer, Paul, 2009. The Wall Street walk and shareholder activism: exit as a form of voice. Rev. Finance. Stud. 22 (7), 2645-2685.
Almeida, Heitor, Campello, Murillo, 2007. Financial constraint, assets tangibility and corporate investment. Review of Finance Studies 20 (5), 1429-1460.
Almeida, Heitor, Campello, Murillo, Galvao, Antonio, 2010. Measurement errors in investment equations. Review of Finance Studies 23, 3279-3382.
securities values: Evidence from the Tel Aviv Stock Exchange. Journal of Financial Economics, 45 (3), 365-390.
Amihud, Yakov, 2002. Illiquidity and stock returns: cross-section and time series effects. J. Financ. Mark. 5 (1), 31-56.
Andrew W. Lo and Jiang Wang, 2000. Trading Volume: Definitions, Data Analysis and Implications of Portfolio Theory. The Review of Finance Studies, Vol. 13, No. 2, pp. 257-300.
Banerjee, Snehal, Kremer, Ilan, 2010. Disagreement and learning: dynamic patterns of trade. Journal of Financial Economics 65 (4), 1269-1302.
Barber, Brad, Odean, Terrance, 2000. Trading is hazardous to your wealth: the common stock investment performance of individual investors. Journal of Financial Economics 55, 773-806.
Beber, Alessandro, Brandt, Michael W. and Kavajecz, Kenneth A., 2010, What Does Equity Sector Orderflow Tell us about the Economy? Unpublished Working Paper, University of Amsterdam.
Beck, Thorsten, Demirguc-Kunt, Asli, Maksimovic, Vojislav, 2008. Financing patterns around the world: are small firms different? Journal of Financial Economics 89, 467-487.
Bharath, Sreedhar T., Paquariello, Paolo, Guojun, Wu., 2009. Does asymmetric information drive capital structure decisions? Review of Finance Studies 22 (8), 3211-3243.
Bond, Stephen, Van Reenen, John, 2008. Microeconometric Models of Investment and Employment. In: Heckman, J.J., Leamer, E.E. (Eds.). Handbook of Econometrics, vol. 6A. Elsevier, Amsterdam. Chapter 65.
Blundell, R., and S. Bond. 1998. Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics 87: 115-143.
Butler, Alexander W., Grullon, Gustavo, Weston, James P., 2005. Stock market liquidity and the cost of issuing equity. Journal of Financial and Quantitative Analysis, 40 (2), 331-348.
Chen, Qi, Goldstein, Itay, Jiang, Wei, 2007. Price informativeness and investment sensitivity to stock prices. Review of Finance Studies 20 (3), 619-650.
Cremers, Martjin, Pareek, Ankur, 2010. Short-Term Trading and Stock Return Anomalies: Momentum, Reversal, Accruals, Share Issuance and R&D Increase.
Working Paper.
Cummins, Jason G., Hassett, Kevin A., Oliner, Stephen D., 2006. Investment behavior, observable expectations, and internal funds. American Economic Review. 96 (3), 796-810.
Diether, Karl B., Malloy, Christopher J., Scherbina, Anna, 2002. Differences of opinion and the cross section of stock returns. Journal of Finance 57 (5), 2113- 2141.
D. Lesmond, J. Ogden, C. Trzcinka, 1999. A new estimate of transaction costs. The
Review of Financial Studies, Vol. 12, No. 5 (Winter, 1999), pp. 1113-1141
Dong, Ming, Hirshleifer, David, Hong Teoh, Siew, 2007. Stock Market Misvaluation and Corporate Investment. Working Paper. http://mpra.ub.uni- muenchen.de/3109/.
Edmans, Alex, 2009. Blockholder trading, market efficiency, and managerial myopia. Journal of Finance 64 (6), 2481-2513.
Edmans, Alex, Manso, Gustavo, 2011. Governance through trading and intervention: a theory of multiple blockholders. Review of Finance Studies 24 (7), 2395-2428.
Fang, Vivia W., Noe, Thomas H., Tice, Sheri, 2009. Stock market liquidity and firm value. Journal of Financial Economics 94 (1), 150-169.
Fang, Vivia W., Tian, Xuan, Tice, Sheri, 2012. Does Stock Liquidity Enhance or Impede Firm Innovation? Working Paper.
Ferreira, Daniel, Ferreira, Miguel A., Raposo, Clara C., 2011. Board structure and price informativeness. J. Financ. Econ. 99, 523–545.
Gilchrist, Simon, & Himmelberg, Charles, 1999. Investment: Fundamentals and finance. In Ben S. Bernanke, & Julio J. Rotemberg (Eds.). NBER macroeconomics annual 1998 (Vol. 13). MIT Press.
Gilchrist, Simon, Himmelberg, Charles P., Huberman, Gur, 2005. Do stock price bubbles influences corporate investment? J. Monet. Econ. 52 (4), 805-827. Gochoco-Bautista. M. S., Sotocinal, N. R., & Wang. J., 2014. Corporate
Investments in Asian Markets: Financial Conditions, Financial Development, and Financial Constraints. World Development 57, 63-78.
Grinblatt, Mark, Keloharju, Matti, 2009. Sensation seeking, overconfidence, and trading activity. Journal of Finance 64 (2), 549-578.
Hou. Kewei, Peng. Lin, Xiong. Wei, 2006. R2 and Price Inefficiency. Fisher College of Business Working Paper Series. www.ssrn.com/abstract=954559.
Kaul, Aditya, Kayacetin, Volkan, 2009. Forecasting Economic Fundamentals and Stock Returns with Equity Market Order Flows: Macro Information in Micro Measures? Working Paper.
Khanna, Naveen, Sonti, Ramana, 2004. Value creating stock manipulation: feedback effect of stock prices on firm value. Journal of Finance Mark. 7 (3),
237-270.
Lesmond, David A., 2005. Liquidity of emerging markets. Journal of Finance
Mark. 77 (2), 411-452.
Lesmond, David A., O'Connor, Philip F., Senbet, Lemma W., 2008. Capital Structure and Equity Liquidity. Working Paper No. RHS-06-067.
Lipson, Marc l., Mortal, Sandra, 2009. Liquidity and capital structure. Journal of Finance Mark. 12, 611-644.
Love. Inessa, 2003. Financial development and financing constraints:International evidence from the structural investment model. Review of Financial Studies, 16, 765 - 791.
Naes, Randi, Skeltorp, Johannes A., Odegaard, Bernt Arne, 2011. Stock market liquidity and the business cycle. Journal of Finance 66 (1), 139-176. Odean,
Terrance, 1999. Do investors trade too much? American Economic Review. 89, 1279 - 1298.
Newey. W. K.; West. K. D., 1987. A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix.
Econometrica 55 (3): 703 - 708.
Maug, Ernst, 1998. Large shareholders as monitors: is there a tradeoff between liquidity and control? Journal of Finance 53 (1), 65-98.
Miller, Edward M., 1977. Risk, uncertainty and divergence of opinion. Journal of Finance 32 (4), 1151-1168.
Modigliani. Franco & Miller. Merton H., 1958. “The Cost of Capital, Corporation Finance and the Theory of Investment,” American Economic Review, Vol. 48,
261- 97.
Muñoz. Francisco, 2012. Liquidity and firm investment: Evidence for Latin America. Journal of Empirical Finance, 20 (2013), 18-29.
Odean, Terrance, 1999. Do investors trade too much? American Economic Review. 89, 1279-1298.
Polk, Christopher, Sapienza, Paola, 2009. The stock market and corporate investment: a test of catering theory. Review of Finance Studies 22 (1), 188-217. Porter, Michael, 1992. Capital disadvantage: America's failing capital investment
system. Harvard Business Review 70 (5), 65-82.
Ratti. Ronald, Lee. Sunglyong, & Seol. Yuon, 2008. Bank concentration and financial constraints on firm level investment in Europe. Journal of Banking and
Finance, 32, 2684-2694.
Sadka. Ronnie, Scherbina. Anna, 2007. Analyst disagreement, mispricing, and liquidity. Journal of Finance 62 (5), 2367 - 2404.
S. Bogdan, S. Bareša, S. Ivanović, 2012. Measuring liquidity on stock market: Impact on liquidity ratio. Tourism and Hospitality Management, Vol. 18, No. 2, pp. 183-193, 201.
Stein. Jeremy, 1988. Takeover threats and managerial myopia. Journal of Political Economy 96 (1), 61-80.
Stein. Jeremy, 1989. Efficient capital market, inefficient firms: a model of myopic corporate behavior. Quarterly Journal of Economics 104 (4), 655-669.
Stein. Jeremy, 1996. Rational capital budgeting in an irrational world. Journal of Business, 69, 429-455.
Stock, James, Yogo, Motohiro, 2005. Testing for Weak Instruments in Linear IV Regression. Andrews, Donald W.K. (Ed.), Identification and Inference for Econometric Models. Cambridge University Press, New York, pp. 80-108. ch. 5. Ranaldo, A., 2000. Intraday Trading Activity on Financial Markets: The Swiss
Evidence. PhD diss., University of Fribourg, Fribourg.
R. Levine, S. Zervos, 1998. Capital control liberalization and stock market development. World Deverlopment, 26: 1169-1183.
Thakor, Anjan, Withed, Toni, 2011. Shareholder-manager disagreement and corporate investment. Rev. Finance 15, 277-300.
PHỤ LỤC
KẾT QUẢ HỒI QUY DGMM
Estimation Command:
=========================
GMM(CX=FD,COV=PERWHITE,GMM=PERWHITE) INVENTORIES LIQUIDITY LEVERAGE CASH_FLOW TOBINQ @ @DYN(INVENTORIES,-2) D(TOBINQ,1) D(TOBINQ,2)
Estimation Equation:
=========================
@DADJ(INVENTORIES) = C(1)*@DADJ(LIQUIDITY) + C(2)*@DADJ(LEVERAGE) + C(3)*@DADJ(CASH_FLOW) + C(4)*@DADJ(TOBINQ)
Dependent Variable: INVENTORIES
Method: Panel Generalized Method of Moments Transformation: First Differences
Date: 09/13/17 Time: 13:55 Sample (adjusted): 2010 2016 Periods included: 7
Cross-sections included: 320
Total panel (unbalanced) observations: 2014 White period instrument weighting matrix
White period standard errors & covariance (d.f. corrected) Instrument specification: @DYN(INVENTORIES,-2) D(TOBINQ,1) D(TOBINQ,2)
Constant added to instrument list
Variable Coefficient Std. Error t-Statistic Prob.
LIQUIDITY -17.56667 9.523290 -1.844601 0.0652
LEVERAGE 1.111643 0.667897 1.664393 0.0962
CASH_FLOW 3.592944 0.878649 4.089167 0.0000
TOBINQ 0.435801 0.109458 3.981434 0.0001
Effects Specification Cross-section fixed (first differences)
Mean dependent var -0.027099 S.D. dependent var 0.650911
S.E. of regression 0.737927 Sum squared resid 1094.519
J-statistic 44.87643 Instrument rank 37
Prob(J-statistic) 0.081350
Arellano-Bond Serial Correlation Test Equation: EQ01_INVENTORIES Date: 09/13/17 Time: 13:55 Sample: 2007 2016
Included observations: 2014
Test order m-Statistic rho SE(rho) Prob.
AR(1) -8.155847 -419.502213 51.435763 0.0000
Estimation Command:
=========================
GMM(CX=FD,COV=PERWHITE,GMM=PERWHITE,ITER=ONEB,KEEPWGTS) PPE LIQUIDITY LEVERAGE CASH_FLOW TOBINQ @ @DYN(D(PPE),-2) D(TOBINQ,1) D(TOBINQ,2)
Estimation Equation:
=========================
@DADJ(PPE) = C(1)*@DADJ(LIQUIDITY) + C(2)*@DADJ(LEVERAGE) + C(3)*@DADJ(CASH_FLOW) + C(4)*@DADJ(TOBINQ)
Substituted Coefficients:
=========================
@DADJ(PPE) = -51.7826035839*@DADJ(LIQUIDITY) - 1.87805827631*@DADJ(LEVERAGE) + 2.46121348909*@DADJ(CASH_FLOW) + 0.433885880805*@DADJ(TOBINQ)
Dependent Variable: PPE
Method: Panel Generalized Method of Moments Transformation: First Differences
Date: 09/13/17 Time: 13:57 Sample (adjusted): 2010 2016 Periods included: 7
Cross-sections included: 321
Total panel (unbalanced) observations: 1944 White period instrument weighting matrix
White period standard errors & covariance (d.f. corrected)
Instrument specification: @DYN(D(PPE),-2) D(TOBINQ,1) D(TOBINQ,2) Constant added to instrument list
Variable Coefficient Std. Error t-Statistic Prob.
LIQUIDITY -51.78260 13.69719 -3.780529 0.0002
LEVERAGE -1.878058 1.381257 -1.359673 0.1741
CASH_FLOW 2.461213 1.314726 1.872035 0.0614
TOBINQ 0.433886 0.137944 3.145373 0.0017
Effects Specification Cross-section fixed (first differences)
Mean dependent var 0.028684 S.D. dependent var 0.631573
S.E. of regression 0.770008 Sum squared resid 1150.249
J-statistic 37.33062 Instrument rank 30
Prob(J-statistic) 0.069777
Arellano-Bond Serial Correlation Test Equation: EQ01_PPE
Date: 09/13/17 Time: 13:57 Sample: 2007 2016
Included observations: 1944
Test order m-Statistic rho SE(rho) Prob.
AR(1) -8.247466 -428.919315 52.006193 0.0000
Estimation Command:
=========================
GMM(CX=FD,COV=PERWHITE,GMM=PERWHITE,ITER=ONEB) TOTAL_ASSET LIQUIDITY LEVERAGE CASH_FLOW TOBINQ @ @DYN(TOTAL_ASSET,-2) D(TOBINQ,1) D(TOBINQ,2)
Estimation Equation:
=========================
@DADJ(TOTAL_ASSET) = C(1)*@DADJ(LIQUIDITY) + C(2)*@DADJ(LEVERAGE) + C(3)*@DADJ(CASH_FLOW) + C(4)*@DADJ(TOBINQ)
Substituted Coefficients:
=========================
@DADJ(TOTAL_ASSET) = 14.0855225167*@DADJ(LIQUIDITY) - 0.0250270093129*@DADJ(LEVERAGE) + 3.21983568662*@DADJ(CASH_FLOW) + 0.215747894027*@DADJ(TOBINQ)
Dependent Variable: TOTAL_ASSET
Method: Panel Generalized Method of Moments Transformation: First Differences
Date: 09/13/17 Time: 13:58 Sample (adjusted): 2010 2016 Periods included: 7
Cross-sections included: 321
Total panel (unbalanced) observations: 2051 White period instrument weighting matrix
White period standard errors & covariance (d.f. corrected)
Instrument specification: @DYN(TOTAL_ASSET,-2) D(TOBINQ,1) D(TOBINQ,2)
Constant added to instrument list
Variable Coefficient Std. Error t-Statistic Prob.
LIQUIDITY 14.08552 4.041674 3.485071 0.0005
LEVERAGE -0.025027 0.456384 -0.054838 0.9563
CASH_FLOW 3.219836 0.435204 7.398454 0.0000
TOBINQ 0.215748 0.051239 4.210612 0.0000
Effects Specification Cross-section fixed (first differences)
Mean dependent var -0.025042 S.D. dependent var 0.284272
S.E. of regression 0.379479 Sum squared resid 294.7766
J-statistic 29.75821 Instrument rank 37
Prob(J-statistic) 0.629316
Arellano-Bond Serial Correlation Test Equation: EQ01_TA
Date: 09/13/17 Time: 13:59 Sample: 2007 2016
Included observations: 2051
Test order m-Statistic rho SE(rho) Prob.
AR(1) -6.034414 -98.750516 16.364558 0.0000
Estimation Command:
=========================
GMM(CX=FD,COV=PERWHITE,GMM=PERWHITE) INVENTORIES LIQUIDITY LIQUIDITY*ISSUE LEVERAGE CASH_FLOW TOBINQ @ @DYN(INVENTORIES,-2) D(TOBINQ,1) D(TOBINQ,2) Estimation Equation:
=========================
@DADJ(INVENTORIES) = C(1)*@DADJ(LIQUIDITY) + C(2)*@DADJ(LIQUIDITY*ISSUE) + C(3)*@DADJ(LEVERAGE) + C(4)*@DADJ(CASH_FLOW) + C(5)*@DADJ(TOBINQ) Substituted Coefficients:
=========================
@DADJ(INVENTORIES) = -32.79237433*@DADJ(LIQUIDITY) + 33.855122016*@DADJ(LIQUIDITY*ISSUE) + 1.03087558449*@DADJ(LEVERAGE) + 3.79426839705*@DADJ(CASH_FLOW) +
0.358358581617*@DADJ(TOBINQ) Dependent Variable: INVENTORIES
Method: Panel Generalized Method of Moments Transformation: First Differences
Date: 09/13/17 Time: 13:59 Sample (adjusted): 2010 2016 Periods included: 7
Cross-sections included: 320
Total panel (unbalanced) observations: 2014 White period instrument weighting matrix
White period standard errors & covariance (d.f. corrected) Instrument specification: @DYN(INVENTORIES,-2) D(TOBINQ,1) D(TOBINQ,2)
Constant added to instrument list
Variable Coefficient Std. Error t-Statistic Prob.
LIQUIDITY -32.79237 13.14255 -2.495131 0.0127 LIQUIDITY*ISSUE 33.85512 21.36833 1.584359 0.1133 LEVERAGE 1.030876 0.695296 1.482642 0.1383 CASH_FLOW 3.794268 0.890465 4.260994 0.0000 TOBINQ 0.358359 0.121710 2.944366 0.0033 Effects Specification Cross-section fixed (first differences)
Mean dependent var -0.027099 S.D. dependent var 0.650911
S.E. of regression 0.756892 Sum squared resid 1150.926
J-statistic 40.40033 Instrument rank 37
Prob(J-statistic) 0.146432
Arellano-Bond Serial Correlation Test Equation: EQ02_INVENTORIES Date: 09/13/17 Time: 14:00 Sample: 2007 2016
Included observations: 2014
Test order m-Statistic rho SE(rho) Prob.
AR(1) -7.461046 -449.032305 60.183560 0.0000
Estimation Command:
=========================
GMM(CX=FD,COV=PERWHITE,GMM=PERWHITE,ITER=ONEB) PPE LIQUIDITY LIQUIDITY*ISSUE LEVERAGE CASH_FLOW TOBINQ @ @DYN(PPE,-2) D(TOBINQ,1) D(TOBINQ,2)
Estimation Equation:
=========================
@DADJ(PPE) = C(1)*@DADJ(LIQUIDITY) + C(2)*@DADJ(LIQUIDITY*ISSUE) + C(3)*@DADJ(LEVERAGE) + C(4)*@DADJ(CASH_FLOW) + C(5)*@DADJ(TOBINQ)
Substituted Coefficients:
=========================
@DADJ(PPE) = -26.9655589448*@DADJ(LIQUIDITY) - 21.588827528*@DADJ(LIQUIDITY*ISSUE) - 3.86853204939*@DADJ(LEVERAGE) + 2.89467780584*@DADJ(CASH_FLOW) +
0.558967389713*@DADJ(TOBINQ) Dependent Variable: PPE
Method: Panel Generalized Method of Moments Transformation: First Differences
Date: 09/13/17 Time: 14:01 Sample (adjusted): 2010 2016 Periods included: 7
Cross-sections included: 321
Total panel (unbalanced) observations: 1989 White period instrument weighting matrix
White period standard errors & covariance (d.f. corrected)
Instrument specification: @DYN(PPE,-2) D(TOBINQ,1) D(TOBINQ,2) Constant added to instrument list
Variable Coefficient Std. Error t-Statistic Prob.
LIQUIDITY -26.96556 17.87515 -1.508550 0.0316 LIQUIDITY*ISSUE -21.58883 26.83151 -0.804607 0.4211 LEVERAGE -3.868532 1.603005 -2.413300 0.0159 CASH_FLOW 2.894678 1.353353 2.138894 0.0326 TOBINQ 0.558967 0.177227 3.153965 0.0016 Effects Specification Cross-section fixed (first differences)
Mean dependent var 0.034229 S.D. dependent var 0.643748
S.E. of regression 0.819520 Sum squared resid 1332.479
J-statistic 39.84610 Instrument rank 37
Prob(J-statistic) 0.160526
Arellano-Bond Serial Correlation Test Equation: EQ02_PPE
Date: 09/13/17 Time: 14:01 Sample: 2007 2016
Included observations: 1989
Test order m-Statistic rho SE(rho) Prob.
AR(1) -8.204970 -460.191410 56.086913 0.0000
Estimation Command:
=========================
GMM(CX=FD,COV=PERWHITE,GMM=PERWHITE,ITER=ONEB) TOTAL_ASSET LIQUIDITY LIQUIDITY*ISSUE LEVERAGE CASH_FLOW TOBINQ @ @DYN(TOTAL_ASSET,-2) D(TOBINQ,1) D(TOBINQ,2)
Estimation Equation:
=========================
@DADJ(TOTAL_ASSET) = C(1)*@DADJ(LIQUIDITY) + C(2)*@DADJ(LIQUIDITY*ISSUE) + C(3)*@DADJ(LEVERAGE) + C(4)*@DADJ(CASH_FLOW) + C(5)*@DADJ(TOBINQ) Substituted Coefficients:
=========================
@DADJ(TOTAL_ASSET) = 4.1729959996*@DADJ(LIQUIDITY) +
14.1793049131*@DADJ(LIQUIDITY*ISSUE) - 0.10945232775*@DADJ(LEVERAGE) + 3.07259316879*@DADJ(CASH_FLOW) + 0.203344981518*@DADJ(TOBINQ)
Dependent Variable: TOTAL_ASSET
Method: Panel Generalized Method of Moments Transformation: First Differences
Date: 09/13/17 Time: 14:02 Sample (adjusted): 2010 2016 Periods included: 7
Cross-sections included: 321
Total panel (unbalanced) observations: 2051 White period instrument weighting matrix
White period standard errors & covariance (d.f. corrected)
Instrument specification: @DYN(TOTAL_ASSET,-2) D(TOBINQ,1) D(TOBINQ,2) Constant added to instrument list
Variable Coefficient Std. Error t-Statistic Prob.
LIQUIDITY 4.172996 8.029230 0.519726 0.6033 LIQUIDITY*ISSUE 14.17930 11.14705 1.272023 0.2035 LEVERAGE -0.109452 0.461511 -0.237161 0.8126 CASH_FLOW 3.072593 0.472915 6.497134 0.0000 TOBINQ 0.203345 0.051898 3.918135 0.0001 Effects Specification Cross-section fixed (first differences)
Mean dependent var -0.025042 S.D. dependent var 0.284272
S.E. of regression 0.374386 Sum squared resid 286.7774
J-statistic 30.30522 Instrument rank 37
Prob(J-statistic) 0.552461
Arellano-Bond Serial Correlation Test Equation: EQ02_TA
Date: 09/13/17 Time: 14:02 Sample: 2007 2016
Included observations: 2051
Test order m-Statistic rho SE(rho) Prob.
AR(1) -5.496978 -99.400536 18.082761 0.0000
Estimation Command:
=========================
GMM(CX=FD,COV=PERWHITE,GMM=PERWHITE,ITER=ONEB) INVENTORIES LIQUIDITY LIQUIDITY*LARGE LEVERAGE CASH_FLOW TOBINQ @ @DYN(INVENTORIES,-2) D(TOBINQ,1) D(TOBINQ,2)
Estimation Equation:
=========================
@DADJ(INVENTORIES) = C(1)*@DADJ(LIQUIDITY) + C(2)*@DADJ(LIQUIDITY*LARGE) + C(3)*@DADJ(LEVERAGE) + C(4)*@DADJ(CASH_FLOW) + C(5)*@DADJ(TOBINQ) Substituted Coefficients:
=========================
@DADJ(INVENTORIES) = -19.3865283409*@DADJ(LIQUIDITY) +
1.51247319376*@DADJ(LIQUIDITY*LARGE) + 1.12253440252*@DADJ(LEVERAGE) + 3.59588623329*@DADJ(CASH_FLOW) + 0.448124080455*@DADJ(TOBINQ)
Dependent Variable: INVENTORIES
Method: Panel Generalized Method of Moments Transformation: First Differences