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- Title: answer3
- Type: Notes
- School: Penn State
- Course: ECON 480
- Term: Fall
Coursehero >> Pennsylvania >> Penn State >> ECON 480
Path: Penn State >> ECON >> 497a Fall, 2008
Path: Penn State >> ECON >> 497a Fall, 2008
Path: Penn State >> ECON >> 497a Fall, 2008
Path: Penn State >> ECON >> 497a Fall, 2008
Path: Penn State >> ECON >> 497a Fall, 2008
Path: Penn State >> ECON >> 497a Fall, 2008
Path: Penn State >> ECON >> 497a Fall, 2008
Path: Penn State >> ECON >> 497a Fall, 2008
Path: Penn State >> ECON >> 497a Fall, 2008
Path: Penn State >> ECON >> 497a Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
Path: Penn State >> ECON >> 501 Fall, 2008
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1 Homework3 1. Exercise 6.2 (page 127) (a) y x = 4(x+ x)2 +9 (4x2 9) x = 8x + 4 x (b) dy=dx = f 0 (x) = 8x (c) f 0 (3) = 8 3 = 24 and f 0 (4) = 32 3. (a) y= x = 5; This is a constant function. (b) No di erence. y= x = 5, as x approaches zero. 2 2.(a) Exercise 7.1 (page 152) d dx ( x 4 ) = 4x 5 (b) d 4 3 dx (5w ) = 20w 3.(d) 34 f (x) = 4 x 3 1 f 0 (x) = x 3 p 1 So f 0 (1) = 1 and f 0 (2) = 2 3 ; (or f 0 (2) = 3 2). 3 2. Exercise 7.2 (page 160) dC C = AC Q = Q3 4Q2 + 174Q. Thus M C = dQ = 3Q2 9Q + 174: Yes, since the total-cost function shows zero xed cost, the situation depicted is the long run. 3. 1 (a) d 2 2) = 81x2 + 18x 6 dx f (x) = 18x(3x + 1) + 3(9x (c) d 2 2 dx f (x) = 2x(4x + 6) + 4x = 12x + 12x (e) d 3(1 + x)(x + 2) + (2 3x)(x + 2) + (2 3x)(1 + x) = dx f (x) = 10.(a) d M C = dQ T C = 6Q + 7 9x2 14x MC 30 20 10 -5 -4 -3 -2 -1 -10 -20 1 2 3 4 Q 5 AC = TC Q = 3Q + 7 + 12 Q AC 50 40 30 20 10 0 0 1 2 3 4 Q 5 4 2. Exercise 7.3 (page 165) dw=dx = dw dy dy dx = 2ay(2bx + c) = 2a(bx2 + cx)(2bx + c) 2 4. Both methods yield the same answer: dy=dx = 32(16x + 3) 3 6.(a) dy When x > 0; dx = 6x5 < 0, for all admissible values of x. Thus the function 1 is strictly decreasing, and dx=dy is equal to - 6 x 5 , the of reciprocal dy=dx. 5 Exercise 7.4 (page 169) 2) + (x2 2) 3y) = 3x2 4x 3y 2.(b) fx = 2x(x fy = 3(x (c) fx = fy = 5 (a) U1 = 2(x1 + 2)(x2 + 3)3 ; U2 = 3(x1 + 2)2 (x2 + 3)2 . (b) U1 (3; 3) = 2 (3 + 2) (3 + 3)3 = 2160 2(x+y) (2x 3y) 5y = (x+y)2 (x+y)2 3(x+y) (2x 3y) 5x = (x+y)2 (x+y)2 6 1.(a) Exercise 7.6 (page 177) jJj = 6x1 36x3 + 12x1 x2 + 48x1 1 The function is dependent. 1 6x2 + 2x2 + 8 1 =0 7 Exercise 9.4 (page 241) 1.(b) f 0 (x) = 3x2 + 12x f "(x) = 6x + 12 The critical values are x = 0 and x = 4. f (0) = 9 is a minimum, because f "(0) = 12 > 0, but f ( 4) = 41 is a maximum, because f "( 4) = 12 < 0. 3. (a) Yes. (b) From the demand function, we rst get the AR function P = 100 - Q. Then we have R = P Q = (100 Q)Q = 100Q Q2 . (c) = R C = 1 Q3 + 6Q2 11Q 50 3 3 (d) Setting d =dQ = Q2 + 12Q 11 = 0 yields two critical values 1 and 11. Only Q* = 11 gives a maximum pro t. ( "(1) > 0; "(11) < 0) (e) Maximum pro t = 111 1 3 6. (a) Q = f (L); R = P0 Q = P0 f (L); C = W0 L + F ; = R C = P0 f (L) W0 L F . (b) d =dL = P0 f 0 (L) W0 = 0, or P0 f 0 (L) = W0 . The value of marginal product must be equated to the wage rate. (c) d2 =dL2 = P0 f "(L). If f "(L) < 0; (means diminishing M P PL ), then we can be sure that pro t is maximized by L*. 4
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COINT.PDF
Path: Penn State >> ECON >> 497a Fall, 2008
Description: COINTEGRATION ANALYSIS Herman J. Bierens1 Pennsylvania State University January 28, 2006 1. 1.1 Introduction What is cointegration? The basic idea behind cointegration is that if all the components of a vector time series process zt have a unit root,...
hwcv-047.pdfPath: Penn State >> ECON >> 497a Fall, 2008
Description: -2- INTRODUCnON Artificial neural networks are a class of models developed by cognitive scientists interested in understanding how computation is performed by the brain. These networks are capable of learning through a process of trial and error th...
HECKMAN.PDFPath: Penn State >> ECON >> 497a Fall, 2008
Description: Maximum likelihood estimation of Heckmans sample selection model Herman J. Bierens October 2007 1 1.1 Heckmans sample selection model Introduction Heckmans sample selection model1 is based on two latent dependent variables models: Y1 = 0 X + U1 ,...
PROBIT_LOGIT.PDFPath: Penn State >> ECON >> 497a Fall, 2008
Description: Comparison of Probit and Logit Analysis The following gure compares the standard normal density f(x) with the density g(x) of the rescaled Logit distribution G(x) = i.e., 1 , 1 + exp (x/) 1 G(x) (1 G(x) , where is chosen such that G(1.96) = 0.975...
RECIDIVISM.PDFPath: Penn State >> ECON >> 497a Fall, 2008
Description: A Competing Risk Analysis of Recidivism Jos R. Carvalho Federal University of Ceara, Brazil Herman J. Bierens Pennsylvania State University, USA & Tilburg University, The Netherlands November 3, 2002 Abstract In this paper we build and estimate an ...
KERNEL.PDFPath: Penn State >> ECON >> 497a Fall, 2008
Description: ...
QUANTILE.PDFPath: Penn State >> ECON >> 497a Fall, 2008
Description: Integrated Conditional Moment Testing of Quantile Regression Models Herman J. Bierens1 Department of Economics, Pennsylvania State University, 608 Kern Graduate Building, University Park, PA16802, and Tilburg University, The Netherlands Donna K. Gin...
EASYREG.PDFPath: Penn State >> ECON >> 497a Fall, 2008
Description: Introduction to EasyReg International Herman J. Bierens Pennsylvania State University April 15, 2007 1. Introduction EasyReg (Easy Regression) International is a free econometrics software package, which can be downloaded from URL http:/econ.la.ps...
COINTJOE.PDFPath: Penn State >> ECON >> 497a Fall, 2008
Description: Nonparametric Cointegration Analysis Herman J. Bierens1 Pennsylvania State University, U.S.A., and Tilburg University, the Netherlands In this paper we propose consistent cointegration tests, and estimators of a basis of the space of cointegrating ve...
MULTCOL.PDFPath: Penn State >> ECON >> 497a Fall, 2008
Description: MULTICOLLINEARITY Herman J. Bierens Pennsylvania State University Revised: April 5, 2007 1. Introduction Consider the linear regression model yj \' 1x1,j % . % kxk,j % uj , j \' 1,.,n , (1) where yj is the dependent variable, the xi,j\'s are the inde...
FORECAST.PDFPath: Penn State >> ECON >> 497a Fall, 2008
Description: FORECASTING Herman J. Bierens Pennsylvania State University November 2008 1. Recursive best linear forecasting Let Y t be a covariance stationary time series process, with E[Y t] \' 0 . The best linear h- step ahead forecast of Yt%h , h \' 1,2,3,., ...
E511THW4.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: ECON 511: Time series econometrics Theoretical homework assignment 4 Consider the bivariate VAR(1) process xt yt where A\' 0.5 0 , and ut vt - i.i.d. N2 0 0 , . \'A xt1 % ut vt , &0.2 0.7 (a) What is the condition for stationarity of the proc...
TEXTBOOK2REVIEW.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: Econometric Theory, 14, 1998, 369374+ Printed in the United States of America+ TOPICS IN ADVANCED ECONOMETRICS: ESTIMATION , TESTING, AND SPECIFICATION OF CROSS-SECTION AND TIME SERIES MODELS Herman J. Bierens Cambridge University Press, 1994 O A H ...
FORECASTING.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: FORECASTING Herman J. Bierens Pennsylvania State University April 18, 2008 1. Conditional expectations as best forecasting schemes Consider a pair of random variables, X and Y, for which you know the joint distribution. Suppose that Y is not yet ob...
LINREG3.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: MULTIVARIATE LINEAR REGRESSION Herman J. Bierens Pennsylvania State University November 20, 2008 1. Missing variables Suppose you assume that the relationship between a dependent variable Yj and an explanatory variable Xj for observations j = 1,.,...
SURVIVAL2.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: EasyReg Module SURVIVAL2 1 Introduction EasyReg module SURVIVAL2 estimates a proportional hazard model for a duration T , conditional on a vector of covariates, without unobserved heterogeneity. The general form of the conditional survival function i...
SNPSURVIVAL2.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: The Right-Censored Bivariate Semi-Nonparametric Mixed Proportional Hazard Model and its Implementation in EasyReg Herman J. Bierens March 8, 2007 Abstract This note contains the setup of the model in Bierens and Carvalho (2007), and its implementatio...
NORMTEST.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: TESTS OF NORMALITY OF REGRESSION ERRORS Herman J. Bierens Pennsylvania State University In this note I will derive the Jarque-Bera and Salmon-Kiefer tests of the normality of the regression errors. I will occasionally refer to my lecture notes on Mo...
METHMOM.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: METHOD OF MOMENTS Herman J. Bierens Pennsylvania State University September 19, 2005 1. 1.1. Linear method of moments The model Consider a system of k linear equations, yi,t \' xi,t i % ui,t, t \' 1,.,n, i \' 1,.,k, i 0 i , T p (1) where the xi.,t ...
FRACTIONS.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: Modeling fractions Herman J. Bierens December 12, 2007 1 Modeling a single fraction Let Y be a dependent variable which is bounded between zero and one, Y (0, 1), for example if Y is a fraction. A possible way to model the distribution of Y condi...
ARMA.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: ARMA MODELS Herman J. Bierens Pennsylvania State University September 9, 2005 1. Introduction Given a covariance stationary process Y t with vanishing memory1 and expectation \' E[Y t] , the linear projection of Y t on its entire past takes the fo...
COINTAPP.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: Separate Appendix to: NONPARAMETRIC COINTEGRATION ANALYSIS by Herman J.Bierens Pennsylvania State University, and Tilburg University, The Netherlands Following Phillips (1987), we use throughout this appendix the symbol \"Y\" to indicate weak converg...
NLLS.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: 1 The CES production function The homogenous Constant Elasticity of Substitution (CES) production function takes the form Q = K + (1 )L n o1/ exp(U), 1, 0 1, > 0, (1) where K is capital, L is labor, Q is output, and U is an error term sa...
ORTHONORMAL_POL.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: Orthonormal Polynomials, Related Orthonormal Functions and the Hilbert Spaces they Span Herman J. Bierens December 5, 2008 1 Orthogonal Polynomials Let w(x) be a non-negative Borel measurable real-valued function on R satisfying Z |x|k w(x)dx (0,...
CETPAR.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: The Econometric Consequences of the Ceteris Paribus Condition in Economic Theory Herman J. Bierens Pennsylvania State University, USA & Tilburg University, the Netherlands Norman R. Swanson Pennsylvania State University, USA September 1998 Abstract ...
ERRATA2.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: Introduction to the Mathematical and Statistical Foundations of Econometrics Remaining corrections and improvements in the 2004 and 2007 editions1 December 12, 2008 Page 8, section 1.2.3: Some of my students had difficulties understanding the deri...
RECIDIVISM.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: A Competing Risk Analysis of Recidivism Jos R. Carvalho Federal University of Ceara, Brazil Herman J. Bierens Pennsylvania State University, USA & Tilburg University, The Netherlands November 3, 2002 Abstract In this paper we build and estimate an ...
SMINKREG.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: Sample Moments Integrating Normal Kernel (SMINK) density and regression estimators 1 SMINK density estimation Let X1; :; Xn be a random sample from a k-variate absolutely continuous distribution with density f (x); expectation ; and non-singular vari...
CIAMODEL.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: ! \" % \" \" \" 0 \" \" # # ./ $ %1 % % + , # \" . < # \" \" ( 5. 7# 89 / \" 1 \" / , 8 # 1 3 2 : \';. 6 9 \". 9\" 4* \" 3# * 8 4 \"> 8. >\" - : . %9*= ,- 2\" \'+) 4 B 9 - * 4 -4 ? 28 )\'@ )+; @!< 3 8 )\'@ )+< @( \'...
FBC.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: Forecasting Quarterly Brazilian GDP Growth Rate With Linear and NonLinear Diusion Index Models Roberto Tatiwa Ferreiraa, Herman Bierensb, Ivan Castelarc a,c Universidade b Pennsylvania Federal do Cear (CAEN/UFC), Brazil a State University, U. S. A. ...
TVCOINT.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: Time Varying Cointegration Herman J. Bierens and Luis F. Martins May 3, 2008 Abstract In this paper we propose a time varying cointegration vector error correction model in which the cointegrating relationship varies smoothly over time. The Johansen...
ICM_IID_SLIDES.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: Integrated Conditional Moment Tests for Parametric Conditional Distributions Herman J. Bierens and Li Wang Pennsylvania State University Department of Economics University Park, PA 16802 This paper extends the Integrated Conditional Moment (ICM) test...
TOBIT.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: The Tobit model Herman J. Bierens September 17, 2004 1 The model The Tobit1 model assumes that the observed dependent variables Yj for observations j = 1; :; n satisfy Yj = max Yj; 0 ; (1) where the Yjs are latent variables generated by the cla...
CALCULUS.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: REVIEW OF CALCULUS Herman J. Bierens Pennsylvania State University (January 28, 2004) 1. Summation Let x1 , x2 , . , xn be a sequence of numbers. The sum of these numbers is usually denoted by x1 % x2 %.% xn \' j xj , or x1 % x2 %.% xn \' \'j\'1xj . n ...
SURVIVAL.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: The Interval-Censored Proportional Hazard Model and its Implementation in EasyReg Herman J. Bierens May 10, 2005 1 The proportional hazard model This EasyReg module (SURVIVAL) estimates a proportional hazard model for a duration T , conditional on...
00b.pdfPath: Penn State >> ECON >> 501 Fall, 2008
Description: ...
KERNEL.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: ...
CONDTREAT.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: Conditional Treatment and Its Eect on Recidivism* Jos R. Carvalho* e Herman J. Bierens* Abstract The objective of this paper is to evaluate the eect of the 1985 Employment Services for Ex-Oenders (ESEO) program on recidivism. Initially, the sample ha...
CONDTREAT2.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: Job Search, Conditional Treatment and Recidivism: The Employment Services for Ex-Oenders Program Reconsidered Herman J. Bierens Department of Economics, Pennsylvania State University Jos R. Carvalho CAEN, Universidade Federal do Cear, Brazil. June 12...
COTRENDA.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: Separate Appendix to: Nonparametric Nonlinear Co-Trending Analysis, With an Application to Interest and Inflation in the U.S. Herman J. Bierens Pennsylvania State University, Department of Economics, University Park, PA 16802 & Tilburg University, t...
IMPHM.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: Semi-Nonparametric Identication of the Right Censored Mixed Proportional Hazard Model Herman J. Bierens Department of Economics Pennsylvania State University November 3, 2008 Abstract Elbers and Ridder (1982) and Heckman and Singer (1984) have shown ...
BREITUNG.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: Journal of Econometrics 108 (2002) 343 363 www.elsevier.com/locate/econbase Nonparametric tests for unit roots and cointegration Jorg Breitung Institute of Statistics and Econometrics, Humboldt University Berlin, Spandauer Strasse 1, D-10178 Berl...
PHILLIPS_JOHANSEN.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: Weak Convergence to the Matrix Stochastic R1 Integral 0 BdB 0 in the Gaussian Case, with Application to Likelihood-Based Cointegration Analysis Herman J. Bierens Pennsylvania State University March 3, 2007 Abstract Phillips (1988) has set forth condi...
LINREG.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: THE CLASSICAL LINEAR REGRESSION MODEL Herman J. Bierens Pennsylvania State University September 1, 2002 1. Introduction The classical linear regression model takes the form y j \' 21 x 1,j % . % 2kx k,j % uj , j \' 1,.,n , unobservable error terms, n i...
MEDIAN.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: Integrated Conditional Moment Testing of Median Regression Models Herman J. Bierens1 Pennsylvania State University, U.S.A. Tilburg University, The Netherlands Donna K. Ginther Washington University, St. Louis, U.S.A. Current version: March 23, 200...
URNLTREND.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: Testing the unit root with drift hypothesis against nonlinear trend stationarity, with an application to the U.S. price level and interest rate 1 Herman J.Bierens 2 Pennsylvania State University, USA, and Tilburg University, the Netherlands (Novemb...
TVCOINT_APPENDIX.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: Separate Appendix to: Time Varying Cointegration Herman J. Bierens and Luis F. Martins May 2, 2008 Abstract In this separate appendix to Bierens and Martins (2008), Time Varying Cointegration, the proof of Theorem 2 is given and the results for the d...
RIDDERWOUTERSEN.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: AO, 5WBNw+WAv 6 AO, ,66WW,v N# 6 AO, Wj,# +AWw O~+# #,w + Djji| -aaji BAa Cj6jA bN|jitjA_ N?iht|) Lu 5L|ih? @*uLh?@ @?_ N?iht|) Lu `it|ih? ?|@hL Li4Mih Sc 2ff lj+NiatH Gxudwlrq/ Vhpl0sdudphwulf Hflhqf| Erxqg/ Pl{hg Sursruwlrqdo Kd}dug1 W?|hL_U|L?...
SDSGEMAPP.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: Separate Appendix to: Econometric Analysis of Linearized Singular Dynamic Stochastic General Equilibrium Models Herman J. Bierens Pennsylvania State University and Tilburg University Derivation of (18): 1X ln [pt1 (, , 1 , Q, 1 | )] n t=1 n = (t1...
SDSGEM.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: Econometric Analysis of Linearized Singular Dynamic Stochastic General Equilibrium Models Herman J. Bierens Pennsylvania State University Abstract In this paper I propose an alternative to calibration of linearized singular dynamic stochastic genera...
COINTJOE.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: Nonparametric Cointegration Analysis Herman J. Bierens1 Pennsylvania State University, U.S.A., and Tilburg University, the Netherlands In this paper we propose consistent cointegration tests, and estimators of a basis of the space of cointegrating ve...
MIGRATION.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: Testing the regional restructuring hypothesis in western Germany Herman J. Bierens Department of Economics, Pennsylvania State University, 608 Kern Graduate Building, University Park, PA 16802, USA; e-mail: hbierens@psu.edu Thomas Kontuly Department ...
ICMMEAN.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: Review of the Integrated Conditional Moment Test and Its Implementation in EasyReg International Herman J. Bierens Pennsylvania State University April 21, 2006 1 The ICM test The ICM test is based on the following theorem: THEOREM 1: Let u be a ra...
ICM_TS.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: Integrated Conditional Moment Tests for Parametric Conditional Distributions of Stationary Time Series Processes Herman J. Bierens and Li Wang Department of Economics and CAPCP Pennsylvania State University University Park, PA 16802 April 29, 2008 A...
ML_LOGIT.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: The Logit Model: Estimation, Testing and Interpretation Herman J. Bierens October 25, 2008 1 1.1 Introduction to maximum likelihood estimation The likelihood function Consider a random sample Y1 , ., Yn from the Bernoulli distribution: Pr[Yj = 1] ...
SURVIVAL1.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: The Right-Censored Proportional Hazard Model and its Implementation in EasyReg Herman J. Bierens May 10, 2005 1 1.1 The right-censored mixed proportional hazard model The proportional hazard model Let T be a duration, and let X be a vector of cova...
BHK.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: An Econometric Model of Credit Spreads with Rebalancing, ARCH and Jump Eects1 Herman Bierens2 Economics Department, Penn State University Jing-zhi Huang3 Smeal College, Penn State University and Stern School, NYU Weipeng Kong4 Smeal College of Busine...
94a.pdfPath: Penn State >> ECON >> 501 Fall, 2008
Description: ...
SMINKDEN.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: The Sample Moments Integrating Normal Kernel (SMINK) density estimator Let X1 , ., Xn be a random sample from a k-variate absolutely continuous distribution with density f(x), expectation , and non-singular variance matrix . Let x(i) be the i-th comp...
POPFORC.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: Urban Studies (1985) 22, 83-90 1985 Urban Studies Notes and Comments Population Forecasting at the City Level: An Econometric Approach Herman J. Bierens and Roy Hoever 1. Introduction tFirst received, April 1983; in final form, December 1983] The...
AUCTIONS_HETERO.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: Semi-Nonparametric Estimation of First-Price Auctions Models with Auction-Specic Heterogeneity via an Integrated Simulated Conditional Moments Method Herman J. Bierensa and Hosin Songb a Department of Economics and CAPCP Pennsylvania State Universit...
SNPSURVIVAL.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: The Interval-Censored Semi-Nonparametric Mixed Proportional Hazard Model and its Implementation in EasyReg Herman J. Bierens May 10, 2005 1 The mixed proportional hazard model This EasyReg module (SNPSURVIVAL) estimates a semi-nonparametric (SNP) ...
ERRATA.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: Introduction to the Mathematical and Statistical Foundations of Econometrics Errata to the first edition April 5, 2007 Page 10, line 2 from bottom: Page 10, line 1 from bottom: Page 20, line 5 from bottom: Page 37, line 2 of Section 2.1: Page 37, lin...
hwcv-047.pdfPath: Penn State >> ECON >> 501 Fall, 2008
Description: -2- INTRODUCnON Artificial neural networks are a class of models developed by cognitive scientists interested in understanding how computation is performed by the brain. These networks are capable of learning through a process of trial and error th...
PROBIT_LOGIT.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: Comparison of Probit and Logit Analysis The following gure compares the standard normal density f(x) with the density g(x) of the rescaled Logit distribution G(x) = i.e., 1 , 1 + exp (x/) 1 G(x) (1 G(x) , where is chosen such that G(1.96) = 0.975...
HAHN1994.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: ...
E511THW5.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: Breitungs nonparametric unit root tests 1. 1.1 The Breitung tests The unit root hypothesis versus zero-mean stationarity Consider the null hypothesis that the time series Y t , t = 1, .,n, is a unit root process: H0: Y t \' Yt&1 % Ut , (1) which yo...
E511THW1.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: ECON 511: Time series econometrics Theoretical Homework 1 1. Let Xt be a zero-mean covariance stationary process for which the linear projection of Xt Xt \' Xtm] \' 0 for m \' 1,2,3,. 4 4 on {Xt1,...
E511THW2.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: ECON 511: Time series econometrics Theoretical homework 2 A time series Xt, t < 4, is always assumed to be defined on a common probability 1. space {,P}. Suppose that Xt is generated by tossing a fair coin at each time t: Xt = 1 if the outcom...
E501_CH6_EX_1.PDFPath: Penn State >> ECON >> 501 Fall, 2008
Description: Let {Xj } be a sequence of random variables j=1 satisfying E[Xj ] = 0, E[|Xi.Xj |] 2|ij|, E[Xj2] = 1 Prove that n 1X Xj = 0. plim n j=1 n 1 ...