|
|
|
- Θεοδοσία Πρίσκιλλα Βενιζέλος
- 6 χρόνια πριν
- Προβολές:
Transcript
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22 22
23 .5 Real consumption.5 Real residential investment Real house prices.5 Real fixed investment Inflation rate.3 Short term nominal interest rate Hours worked goods sector.3 Hours worked housing sector Figure Data Note: Variables in the top panel (with the exception of real house prices) are divided bycivilian noninstitutional population age 6 and over, log-transformed. The first observation (965:) is normalized to zero. Variables in the bottom panel are de-meaned. Hours worked in the two sectors are defined as total hours divided by population. 23
24 Real consumption.4 Real fixed investment Real residential investment.8 Real house prices Figure 2 Data and estimated trends Note: Red dashed line corresponds to the median of the posterior distribution of the trend. Red dotted lines corresponds to 5 and 95 percentiles, blue solid line the data. 24
25 .4 Nominal interest rate.2 Real consumption Inflation Real residential investment Real house price.2 Real fixed investment Hours worked goods sector.2 Hours worked housing sector Real consumption: lenders.2 Real consumption: borrowers Housing stock: lenders 8 Housing stock: borrowers Figure 3 Impulse responses to a positive housing preference shock 25
26 . Nominal interest rate Real consumption.5. Inflation Real residential investment.5.5. Real house price Real fixed investment Hours worked goods sector.5 Hours worked housing sector Real consumption: lenders.8 Real consumption: borrowers Housing stock: lenders.5 Housing stock: borrowers Figure 4 Impulse responses to a positive technology shock in the goods sector 26
27 .6 Nominal interest rate. Real consumption Inflation. 6 Real residential investment Real house price.2 Real fixed investment Hours worked goods sector.2 2 Hours worked housing sector Real consumption: lenders.25 Real consumption: borrowers Housing stock: lenders Housing stock: borrowers Figure 5 Impulse responses to a positive technology shock in the housing sector 27
28 .5 Nominal interest rate.5 Real consumption.5. Inflation.5.5 Real residential investment..5 Real house price.5 Real fixed investment.5.5 Hours worked goods sector 2.5 Hours worked housing sector Real consumption: lenders.5 Real consumption: borrowers Housing stock: lenders 2 Housing stock: borrowers Figure 6 Impulse responses to a negative monetary policy shock 28
29 .2 Nominal interest rate Real consumption Inflation.5 Real residential investment Real house price Real fixed investment.5 Hours worked goods sector 2.5 Hours worked housing sector Real consumption: lenders Real consumption: borrowers Housing stock: lenders 2 Housing stock: borrowers Figure 7 Impulse responses to a negative cost-push shock 29
30 .2 Nominal interest rate.4 Real consumption..2.6 Inflation. Real residential investment Real house price. 3 Real fixed investment Hours worked goods sector.2 Hours worked housing sector Real consumption: lenders.5 Real consumption: borrowers Housing stock: lenders Housing stock: borrowers Figure 8 Impulse responses to a positive investment specific technology 3
31 .2 Nominal interest rate Real consumption..5.2 Inflation.5.5 Real residential investment.5.2 Real house price.5 Real fixed investment.5.5 Hours worked goods sector.5 Hours worked housing sector Real consumption: lenders.5 Real consumption: borrowers Housing stock: lenders 5 Housing stock: borrowers Figure 9 Impulse responses to a positive preference shock 3
32 .2 Nominal interest rate Real consumption Inflation Real residential investment 2.5 Real house price 3 Real fixed investment.5 Hours worked goods sector 2 Hours worked housing sector Real consumption: lenders.5 Real consumption: borrowers Housing stock: lenders Housing stock: borrowers Figure Impulse responses to a negative labor supply shock 32
33 .4 Real consumption.2 Real house prices.2. Real business investment.2.4 Real residential investment.2. Short term nominal interest rate Inflation rate Hours worked goods sector..25 Hours worked housing sector Figure Counterfactual experiment: What if there had been no housing demand shocks from 22: onwards Note: Red dotted lines correspond to the path of the variables in the counterfactual experiment. Blue solid line corresponds to the data. In the simulation the mean of the posterior distribution has been used. 33
34 . Real consumption.2 Real house prices..5 Real business investment.2.5 Real residential investment Short term nominal interest rate.4 Inflation rate Hours worked goods sector.2.5 Hours worked housing sector Figure 2 Counterfactual experiment: What if there had been no housing demand shocks from 965: onwards Note: Red dotted lines correspond to the path of the variables in the counterfactual experiment. In the simulation the mean of the posterior distribution has been used. 34
35 . Real consumption.2 Real house prices..5 Real business investment.2.5 Real residential investment Short term nominal interest rate.4 Inflation rate Hours worked goods sector.2.5 Hours worked housing sector Figure 3 Counterfactual experiment: What if there had been no technology shocks in the housing sector from 965: onwards Note: Red dotted lines correspond to the path of the variables in the counterfactual experiment. In the simulation the mean of the posterior distribution has been used. 35
36 . Real consumption.2 Real house prices..5 Real business investment.2.5 Real residential investment Short term nominal interest rate.4 Inflation rate Hours worked goods sector.2.5 Hours worked housing sector Figure 4 Counterfactual experiment: What if there had been no monetary policy shocks from 965: onwards Note: Red dotted lines correspond to the path of the variables in the counterfactual experiment. In the simulation the mean of the posterior distribution has been used. 36
37 .6 Real consumption.2 Real house prices.4.2. Real business investment.2.4 Real residential investment.2. Short term nominal interest rate Inflation rate Hours worked goods sector.5.25 Hours worked housing sector Figure 5 Counterfactual experiment: What if the Federal Reserve had responded to real house prices since 22: Note: Red dotted lines correspond to the path of the variables in the counterfactual experiment. In the simulation the mean of the posterior distribution has been used. 37
38 elasticity of C to q elasticity of IH to q elasticity of IK to q α Figure 6 Elasticity of aggregate demand components to real house prices following a housing preference shock: the effect of varying the share of constrained agents Note: The elasticities are computed as ratios of the four quarters averages of the responses. Results based on the output of the Metropolis algorithm. 38
39 Credit constraints and age distribution.35 Wage share constrained people (model) Fraction of population aged (data) Figure 7 Credit constraints and age distribution of population Note: Black dotted line corresponds to the share of constrained person in the U.S. Blue solid thick line corresponds to the mean recursive estimates of ( α), while blue solid thin lines denote the 9 percent probabilty intervals. The model has been estimated recursively over a moving window of 2 years. 39
40 Table. Summary statistics for the posterior distribution of the parameters posterior prior parameter mean st. dev. mean st. dev. Type ɛ Beta ɛ Beta η Gamma η Gamma ν Normal ν Normal φ k,c Gamma φ k,h Gamma α Beta m Beta r R Beta r Π Normal r Y Normal θ Beta γ Beta g a,c Normal g a,h Normal g a,k Normal ρ j Beta ρ a,c Beta ρ a,h Beta ρ a,k Beta ρ z Beta ρ τ Beta σ a,c Gamma σ a,h Gamma σ a,k Gamma σ j Gamma σ u Gamma σ R Gamma σ z Gamma σ τ Gamma N ote: Results based on 5, draws from the posterior distribution obtained using the Metropolis algorithm. 4
41 Table 2. Business cycle properties of the model Data Standard deviation (perc.) C IH IK Q NC NH π c R Y Correlations C, Y IK,Y IH,Y Q, Y Q, C Q, IH Q, IK First order correlation C IH IK Q NC NH π c R Y Lead-lag correlations IH (t ),Y (t) IH (t),y (t) IH (t +),Y (t) N ote: Results based on 5, draws from the posterior distribution obtained using the Metropolis algorithm. 4
42 Table 3. Decomposition of the asymptotic variance of the forecast error j a c a h ɛ R u a k z τ C t [.,.4] [.8,4.6] [.,.] [.9,3.] [9.8,2.9] [.4,35.2] [3.5,8.] [9.,43.3] π t [.,.] [2.9,5.3] [.,.2] [2.,8.] [45.2,59.] [.,3.2] [3.,5.] [2.2,36.] IH t [2.8,.8] [3.7,.6] [59.6,75.5] [.,.2] [.,.6] [.,.2] [.,.6] [2.2,23.3] Q t [55.4,78.2] [2.,6.9] [2.2,7.] [.9,2.] [3.2,7.3] [2.8,.8] [.,2.2] [5.9,5.2] R t [.2,2.] [4.3,7.7] [.,.] [9.8,5.8] [5.2,26.3] [6.,2.2] [5.,25.7] [26.9,45.3] IK t [.,.3] [2.8,2.4] [.,.] [.9,2.2] [5.,2.] [57.7,82.] [.,2.] [6.2,8.5] N c,t [.2,.8] [3.5,7.2] [.,.2] [3.2,6.8] [25.9,39.4] [.,.9] [3.,6.] [4.8,6.7] N h,t [8.2,23.4] [.5,.5] [.6,2.7] [.,.9] [.3,5.4] [.,.] [.,2.5] [55.,73.3] N ote: The table reports posterior medians and 9-percent probability intervals (in brackets). Statistics are computed using 5, draws obtained from the Metropolis algorithm. 42
.5 Real consumption.5 Real residential investment.5.5.5 965 975 985 995 25.5 965 975 985 995 25.5 Real house prices.5 Real fixed investment.5.5.5 965 975 985 995 25.5 965 975 985 995 25.3 Inflation rate.3
1 1 1 2 1 2 2 1 43 123 5 122 3 1 312 1 1 122 1 1 1 1 6 1 7 1 6 1 7 1 3 4 2 312 43 4 3 3 1 1 4 1 1 52 122 54 124 8 1 3 1 1 1 1 1 152 1 1 1 1 1 1 152 1 5 1 152 152 1 1 3 9 1 159 9 13 4 5 1 122 1 4 122 5
Managing Economic Fluctuations. Managing Macroeconomic Fluctuations 1
Managing Economic Fluctuations -Keynesian macro: - -term nominal interest rates. - - P. - - P. Managing Macroeconomic Fluctuations 1 Review: New Keynesian Model -run macroeconomics: - π = γ (Y Y P ) +
Supplementary Appendix
Supplementary Appendix Measuring crisis risk using conditional copulas: An empirical analysis of the 2008 shipping crisis Sebastian Opitz, Henry Seidel and Alexander Szimayer Model specification Table
Cite as: Pol Antras, course materials for International Economics I, Spring MIT OpenCourseWare (http://ocw.mit.edu/), Massachusetts
/ / σ/σ σ/σ θ θ θ θ y 1 0.75 0.5 0.25 0 0 0.5 1 1.5 2 θ θ θ x θ θ Φ θ Φ θ Φ π θ /Φ γφ /θ σ θ π θ Φ θ θ Φ θ θ θ θ σ θ / Φ θ θ / Φ / θ / θ Normalized import share: (Xni / Xn) / (XII / XI) 1 0.1 0.01 0.001
Monetary Policy Design in the Basic New Keynesian Model
Monetary Policy Design in the Basic New Keynesian Model Jordi Galí CREI, UPF and Barcelona GSE June 216 Jordi Galí (CREI, UPF and Barcelona GSE) Monetary Policy Design June 216 1 / 12 The Basic New Keynesian
Bayesian statistics. DS GA 1002 Probability and Statistics for Data Science.
Bayesian statistics DS GA 1002 Probability and Statistics for Data Science http://www.cims.nyu.edu/~cfgranda/pages/dsga1002_fall17 Carlos Fernandez-Granda Frequentist vs Bayesian statistics In frequentist
Other Test Constructions: Likelihood Ratio & Bayes Tests
Other Test Constructions: Likelihood Ratio & Bayes Tests Side-Note: So far we have seen a few approaches for creating tests such as Neyman-Pearson Lemma ( most powerful tests of H 0 : θ = θ 0 vs H 1 :
Description of the PX-HC algorithm
A Description of the PX-HC algorithm Let N = C c= N c and write C Nc K c= i= k= as, the Gibbs sampling algorithm at iteration m for continuous outcomes: Step A: For =,, J, draw θ m in the following steps:
Biostatistics for Health Sciences Review Sheet
Biostatistics for Health Sciences Review Sheet http://mathvault.ca June 1, 2017 Contents 1 Descriptive Statistics 2 1.1 Variables.............................................. 2 1.1.1 Qualitative........................................
Figure A.2: MPC and MPCP Age Profiles (estimating ρ, ρ = 2, φ = 0.03)..
Supplemental Material (not for publication) Persistent vs. Permanent Income Shocks in the Buffer-Stock Model Jeppe Druedahl Thomas H. Jørgensen May, A Additional Figures and Tables Figure A.: Wealth and
Solution Series 9. i=1 x i and i=1 x i.
Lecturer: Prof. Dr. Mete SONER Coordinator: Yilin WANG Solution Series 9 Q1. Let α, β >, the p.d.f. of a beta distribution with parameters α and β is { Γ(α+β) Γ(α)Γ(β) f(x α, β) xα 1 (1 x) β 1 for < x
HOMEWORK#1. t E(x) = 1 λ = (b) Find the median lifetime of a randomly selected light bulb. Answer:
HOMEWORK# 52258 李亞晟 Eercise 2. The lifetime of light bulbs follows an eponential distribution with a hazard rate of. failures per hour of use (a) Find the mean lifetime of a randomly selected light bulb.
HW 3 Solutions 1. a) I use the auto.arima R function to search over models using AIC and decide on an ARMA(3,1)
HW 3 Solutions a) I use the autoarima R function to search over models using AIC and decide on an ARMA3,) b) I compare the ARMA3,) to ARMA,0) ARMA3,) does better in all three criteria c) The plot of the
Μια δεκαετία στασιµότητας των µισθών
14 / 4 Μια δεκαετία στασιµότητας των µισθών Των Fernando Rios-Avila και Julie L. Hotchkiss Οι µισθοί των εργαζοµένων είναι το πιο σηµαντικό συστατικό στοιχείο του εισοδήµατος των νοικοκυριών. Αντιπροσωπεύουν
Geographic Barriers to Commodity Price Integration: Evidence from US Cities and Swedish Towns,
Crawford School of Public Policy CAMA Centre for Applied Macroeconomic Analysis Geographic Barriers to Commodity Price Integration: Evidence from US Cities and Swedish Towns, 1732-1860 CAMA Working Paper
ΔΙΠΛΩΜΑΤΙΚΗ ΕΡΓΑΣΙΑ: Mετακύλιση τιμών βασικών προϊόντων και τροφίμων στην περίπτωση του Νομού Αιτωλοακαρνανίας
ΤΜΗΜΑ ΔΙΟΙΚΗΣΗΣ ΕΠΙΧΕΙΡΗΣΕΩΝ ΑΓΡΟΤΙΚΩΝ ΠΡΟΙΟΝΤΩΝ ΚΑΙ ΤΡΟΦΙΜΩΝ MBA ΔΙΟΙΚΗΣΗ ΕΠΙΧΕΙΡΗΣΕΩΝ ΤΡΟΦΙΜΩΝ ΔΙΠΛΩΜΑΤΙΚΗ ΕΡΓΑΣΙΑ: Mετακύλιση τιμών βασικών προϊόντων και τροφίμων στην περίπτωση του Νομού Αιτωλοακαρνανίας
IMES DISCUSSION PAPER SERIES
IMES DISCUSSION PAPER SERIES Will a Growth Miracle Reduce Debt in Japan? Selahattin mrohorolu and Nao Sudo Discussion Paper No. 2011-E-1 INSTITUTE FOR MONETARY AND ECONOMIC STUDIES BANK OF JAPAN 2-1-1
Sticky Leverage. Joao Gomes, Urban Jermann & Lukas Schmid. Wharton School, Duke & UCLA
Sticky Leverage Joao Gomes, Urban Jermann & Lukas Schmid Wharton School, Duke & UCLA We consider nominal debt that is long-term and defaultable We consider nominal debt that is long-term and defaultable
UMI Number: All rights reserved
UMI Number: 3408360 All rights reserved INFORMATION TO ALL USERS The quality of this reproduction is dependent upon the quality of the copy submitted. In the unlikely event that the author did not send
Statistics 104: Quantitative Methods for Economics Formula and Theorem Review
Harvard College Statistics 104: Quantitative Methods for Economics Formula and Theorem Review Tommy MacWilliam, 13 tmacwilliam@college.harvard.edu March 10, 2011 Contents 1 Introduction to Data 5 1.1 Sample
k A = [k, k]( )[a 1, a 2 ] = [ka 1,ka 2 ] 4For the division of two intervals of confidence in R +
[a 1, a 2 ] = [ka 1,ka 2 ] 4For the division of two intervals of confidence in R +](/thumbs/73/69566903.jpg "k A = [k, k]( )[a 1, a 2 ] = [ka 1,ka 2 ] 4For the division of two intervals of confidence in R +")
Chapter 3. Fuzzy Arithmetic 3- Fuzzy arithmetic: ~Addition(+) and subtraction (-): Let A = [a and B = [b, b in R If x [a and y [b, b than x+y [a +b +b Symbolically,we write A(+)B = [a (+)[b, b = [a +b
NBER WORKING PAPER SERIES SHOCKS AND CRASHES. Martin Lettau Sydney C. Ludvigson. Working Paper
NBER WORKING PAPER SERIES SHOCKS AND CRASHES Martin Lettau Sydney C. Ludvigson Working Paper 16996 http://www.nber.org/papers/w16996 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge,
Άσκηση 10, σελ. 119. Για τη μεταβλητή x (άτυπος όγκος) έχουμε: x censored_x 1 F 3 F 3 F 4 F 10 F 13 F 13 F 16 F 16 F 24 F 26 F 27 F 28 F
Άσκηση 0, σελ. 9 από το βιβλίο «Μοντέλα Αξιοπιστίας και Επιβίωσης» της Χ. Καρώνη (i) Αρχικά, εισάγουμε τα δεδομένα στο minitab δημιουργώντας δύο μεταβλητές: τη x για τον άτυπο όγκο και την y για τον τυπικό
APPENDICES APPENDIX A. STATISTICAL TABLES AND CHARTS 651 APPENDIX B. BIBLIOGRAPHY 677 APPENDIX C. ANSWERS TO SELECTED EXERCISES 679
APPENDICES APPENDIX A. STATISTICAL TABLES AND CHARTS 1 Table I Summary of Common Probability Distributions 2 Table II Cumulative Standard Normal Distribution Table III Percentage Points, 2 of the Chi-Squared
SECTION II: PROBABILITY MODELS
SECTION II: PROBABILITY MODELS 1 SECTION II: Aggregate Data. Fraction of births with low birth weight per province. Model A: OLS, using observations 1 260 Heteroskedasticity-robust standard errors, variant
d 1 d 1
É É d 1 d 1 n n ;,x E ; x E X0 X X)0 0Ld 0Ld 0Ld 0Ld 0Ld 0Ld 1 0Ld 0Ld 0 0 0 1 ' 0 0d 0 0d 0 0d 0 0 0 0 0 0 0d 0 0 0 0d 0 0 0 0 0d 0 0 0Ld 0 0Ld 0d 0 0 0 0 0 0 0 0 0 0 \g 0,Q 0 i 0)( 0)( 0 0 0 0d 0 0
FORMULAS FOR STATISTICS 1
FORMULAS FOR STATISTICS 1 X = 1 n Sample statistics X i or x = 1 n x i (sample mean) S 2 = 1 n 1 s 2 = 1 n 1 (X i X) 2 = 1 n 1 (x i x) 2 = 1 n 1 Xi 2 n n 1 X 2 x 2 i n n 1 x 2 or (sample variance) E(X)
Π1 ΕΝΣΤΠΟ ΠΡΟΣΑΕΩΝ ΕΟΔΩΝ ΠΡΟΫΠΟΛΟΓΙΜΟΤ ΟΙΚONOMIKOY ΕΣΟΤ 2017
Π1 ΕΝΣΤΠΟ ΠΡΟΣΑΕΩΝ ΕΟΔΩΝ ΠΡΟΫΠΟΛΟΓΙΜΟΤ ΟΙΚONOMIKOY ΕΣΟΤ 2017 ΤΠΟΤΡΓΕΙΟ ΓΕΝΙΚΗ Δ/ΝΗ ΔΙΕΤΘΤΝΗ ΣΜΗΜΑ Ταχ. Δ/νση : Πληροφορίες : Τηλέφωνο : Fax : e-mail : Κ.Α. ΕΟΔΟΤ Περιγραφή εσόδου: ΤΝΣΟΜΗ ΠΕΡΙΓΡΑΥΗ ΣΟΤ
Exercises to Statistics of Material Fatigue No. 5
Prof. Dr. Christine Müller Dipl.-Math. Christoph Kustosz Eercises to Statistics of Material Fatigue No. 5 E. 9 (5 a Show, that a Fisher information matri for a two dimensional parameter θ (θ,θ 2 R 2, can
Queensland University of Technology Transport Data Analysis and Modeling Methodologies
Queensland University of Technology Transport Data Analysis and Modeling Methodologies Lab Session #7 Example 5.2 (with 3SLS Extensions) Seemingly Unrelated Regression Estimation and 3SLS A survey of 206
HISTOGRAMS AND PERCENTILES What is the 25 th percentile of a histogram? What is the 50 th percentile for the cigarette histogram?
HISTOGRAMS AND PERCENTILES What is the 25 th percentile of a histogram? The point on the horizontal axis such that of the area under the histogram lies to the left of that point (and to the right) What
Notes on the Open Economy
Notes on the Open Econom Ben J. Heijdra Universit of Groningen April 24 Introduction In this note we stud the two-countr model of Table.4 in more detail. restated here for convenience. The model is Table.4.
Summary of the model specified
Program: HLM 7 Hierarchical Linear and Nonlinear Modeling Authors: Stephen Raudenbush, Tony Bryk, & Richard Congdon Publisher: Scientific Software International, Inc. (c) 2010 techsupport@ssicentral.com
ΑΝΑΛΥΣΗ ΚΟΣΤΟΥΣ ΟΦΕΛΟΥΣ ΣΤΟΝ ΚΛΑΔΟ ΤΗΣ ΕΝΕΡΓΕΙΑΣ
Εργασία στα πλαίσια του μαθήματος ΟΙΚΟΝΟΜΙΚΑ ΤΗΣ ΕΝΕΡΓΕΙΑΣ ΑΝΑΛΥΣΗ ΚΟΣΤΟΥΣ ΟΦΕΛΟΥΣ ΣΤΟΝ ΚΛΑΔΟ ΤΗΣ ΕΝΕΡΓΕΙΑΣ Α.Μ. 12012058 Επιβλέπων: Κωνσταντίνος Κουνετάς ΔΕΚΕΜΒΡΙΟΣ 2014 Περίληψη Η ανάλυση κόστους οφέλους
5.4 The Poisson Distribution.
The worst thing you can do about a situation is nothing. Sr. O Shea Jackson 5.4 The Poisson Distribution. Description of the Poisson Distribution Discrete probability distribution. The random variable
WORKING P A P E R. Learning your Child s Price. Evidence from Data on Projected Dowry in Rural India A. V. CHARI AND ANNEMIE MAERTENS WR-899
WORKING P A P E R Learning your Child s Price Evidence from Data on Projected Dowry in Rural India A. V. CHARI AND ANNEMIE MAERTENS WR-899 November 2011 This product is part of the RAND Labor and Population
Housing, Economics and People in the U.S. and Wisconsin
Housing, Economics and People in the U.S. and Wisconsin Morris A. Davis, Ph. D. Academic Director, James A. Graaskamp Center for Real Estate Associate Professor, Department of Real Estate and Urban Land
: Monte Carlo EM 313, Louis (1982) EM, EM Newton-Raphson, /. EM, 2 Monte Carlo EM Newton-Raphson, Monte Carlo EM, Monte Carlo EM, /. 3, Monte Carlo EM
2008 6 Chinese Journal of Applied Probability and Statistics Vol.24 No.3 Jun. 2008 Monte Carlo EM 1,2 ( 1,, 200241; 2,, 310018) EM, E,,. Monte Carlo EM, EM E Monte Carlo,. EM, Monte Carlo EM,,,,. Newton-Raphson.
Ηλεκτρονικοί Υπολογιστές IV
ΠΑΝΕΠΙΣΤΗΜΙΟ ΙΩΑΝΝΙΝΩΝ ΑΝΟΙΚΤΑ ΑΚΑΔΗΜΑΪΚΑ ΜΑΘΗΜΑΤΑ Ηλεκτρονικοί Υπολογιστές IV Δυναμική του χρέους και του ελλείμματος Διδάσκων: Επίκουρος Καθηγητής Αθανάσιος Σταυρακούδης Άδειες Χρήσης Το παρόν εκπαιδευτικό
ΚΥΠΡΙΑΚΗ ΕΤΑΙΡΕΙΑ ΠΛΗΡΟΦΟΡΙΚΗΣ CYPRUS COMPUTER SOCIETY ΠΑΓΚΥΠΡΙΟΣ ΜΑΘΗΤΙΚΟΣ ΔΙΑΓΩΝΙΣΜΟΣ ΠΛΗΡΟΦΟΡΙΚΗΣ 19/5/2007
Οδηγίες: Να απαντηθούν όλες οι ερωτήσεις. Αν κάπου κάνετε κάποιες υποθέσεις να αναφερθούν στη σχετική ερώτηση. Όλα τα αρχεία που αναφέρονται στα προβλήματα βρίσκονται στον ίδιο φάκελο με το εκτελέσιμο
Κόστος Κεφαλαίου. Estimating Inputs: Discount Rates
Αρτίκης Γ. Παναγιώτης Κόστος Κεφαλαίου Estimating Inputs: Discount Rates Critical ingredient in discounted cashflow valuation. Errors in estimating the discount rate or mismatching cashflows and discount
6.3 Forecasting ARMA processes
122 CHAPTER 6. ARMA MODELS 6.3 Forecasting ARMA processes The purpose of forecasting is to predict future values of a TS based on the data collected to the present. In this section we will discuss a linear
5.6 evaluating, checking, comparing Chris Parrish July 3, 2016
5.6 evaluating, checking, comparing Chris Parrish July 3, 2016 Contents residuals 1 evaluating, checking, comparing 1 data..................................................... 1 model....................................................
Durbin-Levinson recursive method
Durbin-Levinson recursive method A recursive method for computing ϕ n is useful because it avoids inverting large matrices; when new data are acquired, one can update predictions, instead of starting again
IR Futures Effective Asset Class ก Efficient Frontier
Interest Futures* ก * ก ก ก. ก ก 11 ก ก ก ก ก ( ) ก ก * Interest Futures ก ก ก ก ก ก ก ก ก ก (Synthetic Portfolio) ก * ก ก ก 2 กก IR Futures ก ก (Asset Class) IR Futures Supposedly Most Efficient and Effective
Aquinas College. Edexcel Mathematical formulae and statistics tables DO NOT WRITE ON THIS BOOKLET
Aquinas College Edexcel Mathematical formulae and statistics tables DO NOT WRITE ON THIS BOOKLET Pearson Edexcel Level 3 Advanced Subsidiary and Advanced GCE in Mathematics and Further Mathematics Mathematical
CHAPTER 25 SOLVING EQUATIONS BY ITERATIVE METHODS
CHAPTER 5 SOLVING EQUATIONS BY ITERATIVE METHODS EXERCISE 104 Page 8 1. Find the positive root of the equation x + 3x 5 = 0, correct to 3 significant figures, using the method of bisection. Let f(x) =
ST5224: Advanced Statistical Theory II
ST5224: Advanced Statistical Theory II 2014/2015: Semester II Tutorial 7 1. Let X be a sample from a population P and consider testing hypotheses H 0 : P = P 0 versus H 1 : P = P 1, where P j is a known
255 (log-normal distribution) 83, 106, 239 (malus) 26 - (Belgian BMS, Markovian presentation) 32 (median premium calculation principle) 186 À / Á (goo
(absolute loss function)186 - (posterior structure function)163 - (a priori rating variables)25 (Bayes scale) 178 (bancassurance)233 - (beta distribution)203, 204 (high deductible)218 (bonus)26 ( ) (total
Αγορά Ακινήτων και η ελληνική Κρίση
Αγορά Ακινήτων και η ελληνική Κρίση Γκίκας Α. Χαρδούβελης Οικονομικός Σύμβουλος, Eurobank Group Καθηγητής, Τμήμα Χρηματοοικονομικής και Τραπεζικής Διοικητικής, Πανεπιστήμιο Πειραιά PRODEXPO 213 14ο Συνέδριο
Mean bond enthalpy Standard enthalpy of formation Bond N H N N N N H O O O
Q1. (a) Explain the meaning of the terms mean bond enthalpy and standard enthalpy of formation. Mean bond enthalpy... Standard enthalpy of formation... (5) (b) Some mean bond enthalpies are given below.
ΔΗΜΙΟΥΡΓΙΑ ΔΥΝΑΜΙΚΟΥ ΜΟΝΤΕΛΟΥ ΑΝΟΔΙΚΗΣ ΠΡΟΣΕΓΓΙΣΗΣ ΓΙΑ ΤΗΝ ΕΚΤΙΜΗΣΗ ΤΗΣ ΖΗΤΗΣΗΣ ΕΝΕΡΓΕΙΑΣ ΣΤΟΝ ΟΙΚΙΣΤΙΚΟ ΤΟΜΕΑ ΤΗΣ ΚΥΠΡΟΥ
Σχολή Γεωτεχνικών Επιστημών & Διαχείρισης Περιβάλλοντος Μεταπτυχιακή διατριβή ΔΗΜΙΟΥΡΓΙΑ ΔΥΝΑΜΙΚΟΥ ΜΟΝΤΕΛΟΥ ΑΝΟΔΙΚΗΣ ΠΡΟΣΕΓΓΙΣΗΣ ΓΙΑ ΤΗΝ ΕΚΤΙΜΗΣΗ ΤΗΣ ΖΗΤΗΣΗΣ ΕΝΕΡΓΕΙΑΣ ΣΤΟΝ ΟΙΚΙΣΤΙΚΟ ΤΟΜΕΑ ΤΗΣ ΚΥΠΡΟΥ Γαλάτεια
Homework 3 Solutions
Homework 3 Solutions Igor Yanovsky (Math 151A TA) Problem 1: Compute the absolute error and relative error in approximations of p by p. (Use calculator!) a) p π, p 22/7; b) p π, p 3.141. Solution: For
department listing department name αχχουντσ ϕανε βαλικτ δδσϕηασδδη σδηφγ ασκϕηλκ τεχηνιχαλ αλαν ϕουν διξ τεχηνιχαλ ϕοην µαριανι
She selects the option. Jenny starts with the al listing. This has employees listed within She drills down through the employee. The inferred ER sttricture relates this to the redcords in the databasee
Bayesian Data Analysis, Midterm I
Bayesian Data Analysis, Midterm I Bugra Gedik bgedik@cc.gatech.edu October 3, 4 Q1) I have used Gibs sampler to solve this problem. 5, iterations with burn-in value of 1, is used. The resulting histograms
Μηχανική Μάθηση Hypothesis Testing
ΕΛΛΗΝΙΚΗ ΔΗΜΟΚΡΑΤΙΑ ΠΑΝΕΠΙΣΤΗΜΙΟ ΚΡΗΤΗΣ Μηχανική Μάθηση Hypothesis Testing Γιώργος Μπορμπουδάκης Τμήμα Επιστήμης Υπολογιστών Procedure 1. Form the null (H 0 ) and alternative (H 1 ) hypothesis 2. Consider
5.1 logistic regresssion Chris Parrish July 3, 2016
5.1 logistic regresssion Chris Parrish July 3, 2016 Contents logistic regression model 1 1992 vote 1 data..................................................... 1 model....................................................
ΧΩΡΙΚΑ ΟΙΚΟΝΟΜΕΤΡΙΚΑ ΥΠΟΔΕΙΓΜΑΤΑ ΣΤΗΝ ΕΚΤΙΜΗΣΗ ΤΩΝ ΤΙΜΩΝ ΤΩΝ ΑΚΙΝΗΤΩΝ SPATIAL ECONOMETRIC MODELS FOR VALUATION OF THE PROPERTY PRICES
1 ο Συνέδριο Χωρικής Ανάλυσης: Πρακτικά, Αθήνα, 013, Σ. Καλογήρου (Επ.) ISBN: 978-960-86818-6-6 ΧΩΡΙΚΑ ΟΙΚΟΝΟΜΕΤΡΙΚΑ ΥΠΟΔΕΙΓΜΑΤΑ ΣΤΗΝ ΕΚΤΙΜΗΣΗ ΤΩΝ ΤΙΜΩΝ ΤΩΝ ΑΚΙΝΗΤΩΝ Μαριάνθη Στάμου 1*, Άγγελος Μιμής και
Probability and Random Processes (Part II)
Probability and Random Processes (Part II) 1. If the variance σ x of d(n) = x(n) x(n 1) is one-tenth the variance σ x of a stationary zero-mean discrete-time signal x(n), then the normalized autocorrelation
The Pohozaev identity for the fractional Laplacian
The Pohozaev identity for the fractional Laplacian Xavier Ros-Oton Departament Matemàtica Aplicada I, Universitat Politècnica de Catalunya (joint work with Joaquim Serra) Xavier Ros-Oton (UPC) The Pohozaev
Supplementary Materials for Evolutionary Multiobjective Optimization Based Multimodal Optimization: Fitness Landscape Approximation and Peak Detection
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, VOL. XX, NO. X, XXXX XXXX Supplementary Materials for Evolutionary Multiobjective Optimization Based Multimodal Optimization: Fitness Landscape Approximation
The Simply Typed Lambda Calculus
Type Inference Instead of writing type annotations, can we use an algorithm to infer what the type annotations should be? That depends on the type system. For simple type systems the answer is yes, and
Testing for Indeterminacy: An Application to U.S. Monetary Policy. Technical Appendix
Testing for Indeterminacy: An Application to U.S. Monetary Policy Technical Appendix Thomas A. Lubik Department of Economics Johns Hopkins University Frank Schorfheide Department of Economics University
Figure 3 Three observations (Vp, Vs and density isosurfaces) intersecting in the PLF space. Solutions exist at the two indicated points.
φ φ φ φ Figure 1 Resampling of a rock-physics model from velocity-porosity to lithology-porosity space. C i are model results for various clay contents. φ ρ ρ δ Figure 2 Bulk modulus constraint cube in
Repeated measures Επαναληπτικές μετρήσεις
ΠΡΟΒΛΗΜΑ Στο αρχείο δεδομένων diavitis.sav καταγράφεται η ποσότητα γλυκόζης στο αίμα 10 ασθενών στην αρχή της χορήγησης μιας θεραπείας, μετά από ένα μήνα και μετά από δύο μήνες. Μελετήστε την επίδραση
Introduction to Risk Parity and Budgeting
Introduction to Risk Parity and Budgeting Chapter 1 Modern Portfolio Theory c Thierry Roncalli & CRC Press Evry University & Lyxor Asset Management, France Instructors may find the description of the book
Κόστος Κεφαλαίου. Estimating Inputs: Discount Rates
Αρτίκης Γ. Παναγιώτης Κόστος Κεφαλαίου Estimating Inputs: Discount Rates Critical ingredient in discounted cashflow valuation. Errors in estimating the discount rate or mismatching cashflows and discount
An Inventory of Continuous Distributions
Appendi A An Inventory of Continuous Distributions A.1 Introduction The incomplete gamma function is given by Also, define Γ(α; ) = 1 with = G(α; ) = Z 0 Z 0 Z t α 1 e t dt, α > 0, >0 t α 1 e t dt, α >
/ Economic Line / Σελίδα Page
Η εταιρία Ν. Ζουμπούλης VIOBRASS δραστηριοποιείται από το 1972 στον σχεδιασμό και κατασκευή ειδών κιγκαλερίας. Παράγει κουρτνόβεργες, πόμολα επίπλων, χερούλια θυρών και εξωθύρων, κρεμάστρες και καλόγερους.
Selecting Critical Properties of Terpenes and Terpenoids through Group-Contribution Methods and Equations of State
Supporting Information Selecting Critical Properties of Terpenes and Terpenoids through Group-Contribution Methods and Equations of State Mónia A. R. Martins, 1,2 Pedro J. Carvalho, 1 André M. Palma, 1
( ) , ) , ; kg 1) 80 % kg. Vol. 28,No. 1 Jan.,2006 RESOURCES SCIENCE : (2006) ,2 ,,,, ; ;
28 1 2006 1 RESOURCES SCIENCE Vol. 28 No. 1 Jan. 2006 :1007-7588(2006) 01-0002 - 07 20 1 1 2 (11 100101 ; 21 101149) : 1978 1978 2001 ; 2010 ; ; ; : ; ; 24718kg 1) 1990 26211kg 260kg 1995 2001 238kg( 1)
Περιγραφική στατιστική μεθοδολογία.
Περιγραφική στατιστική μεθοδολογία. Κυργίδης Αθανάσιος MD, DDS, BΟpt, PhD MSc Medical Research, Μετεκπαίδευση ΕΠΙ ΕΚΑΒ Γναθοπροσωπικός Χειρουργός Ass. Editor, Hippokratia 2 κεφάλαια: Περιγραφική Αναλυτική
C32,B22, Q1,E52 :JEL.
- / / / / ( ) / / : / / :.... C32,B22, Q1,E52 :JEL. : E-mail: amdadras@yahoo.com E-mail: zibaei@shirazu.ac.ir.. / 96.( ).( )...( ).( ). ).(. ).(. 1. Schuh 2. Chambers 3. Just 4. Batten 5. Belongia 6. Bessler
ΣΕΝΑΡΙΟ ΑΝΑΦΟΡΑΣ ΣΕΝΑΡΙΟ ΤΩΝ ΜΗΧΑΝΙΚΩΝ ΣΕΝΑΡΙΟ ΣΥΝΕΡΓΑΣΙΑΣ
ΑΝΑΠΤΥΞΗ ΚΑΙ ΕΦΑΡΜΟΓΗ ΠΟΛΙΤΙΚΗΣ ΟΛΟΚΛΗΡΩΜΕΝΗΣ ΔΙΑΧΕΙΡΙΣΗΣ ΥΔΑΤΙΚΩΝ ΠΟΡΩΝ ΣΕ ΜΙΑ ΥΔΡΟΛΟΓΙΚΗ ΛΕΚΑΝΗ ΜΕ ΤΗΝ ΕΦΑΡΜΟΓΗ ΜΙΑΣ ΔΗΜΟΣΙΑΣ ΚΟΙΝΩΝΙΚΗΣ ΣΥΜΦΩΝΙΑΣ ΣΤΗ ΒΑΣΗ ΤΩΝ ΑΡΧΩΝ ΤΗΣ AGENDA 21 ΚΑΙ ΤΩΝ ΚΑΤΕΥΘΥΝΣΕΩΝ
Statistics & Research methods. Athanasios Papaioannou University of Thessaly Dept. of PE & Sport Science
Statistics & Research methods Athanasios Papaioannou University of Thessaly Dept. of PE & Sport Science 30 25 1,65 20 1,66 15 10 5 1,67 1,68 Κανονική 0 Height 1,69 Καμπύλη Κανονική Διακύμανση & Ζ-scores
ΠΟΙΟΤΗΤΑ ΑΤΜΟΣΦΑΙΡΙΚΟΥ ΠΕΡΙΒΑΛΛΟΝΤΟΣ
ΠΟΙΟΤΗΤΑ ΑΤΜΟΣΦΑΙΡΙΚΟΥ ΠΕΡΙΒΑΛΛΟΝΤΟΣ Κωνσταντινή Σαµαρά - Κωνσταντίνου Εργαστήριο Ελέγχου Ρύπανσης Περιβάλλοντος, Τµήµα Χηµείας, ΑΠΘ ΠΕΡΙΛΗΨΗ Παρότι υπήρξε σηµαντική βελτίωση της ποιότητας του αέρα στις
VBA Microsoft Excel. J. Comput. Chem. Jpn., Vol. 5, No. 1, pp (2006)
J. Comput. Chem. Jpn., Vol. 5, No. 1, pp. 29 38 (2006) Microsoft Excel, 184-8588 2-24-16 e-mail: yosimura@cc.tuat.ac.jp (Received: July 28, 2005; Accepted for publication: October 24, 2005; Published on
Simon et al. Supplemental Data Page 1
Simon et al. Supplemental Data Page 1 Supplemental Data Acute hemodynamic effects of inhaled sodium nitrite in pulmonary hypertension associated with heart failure with preserved ejection fraction Short
Web-based supplementary materials for Bayesian Quantile Regression for Ordinal Longitudinal Data
Web-based supplementary materials for Bayesian Quantile Regression for Ordinal Longitudinal Data Rahim Alhamzawi, Haithem Taha Mohammad Ali Department of Statistics, College of Administration and Economics,
Section 7.6 Double and Half Angle Formulas
09 Section 7. Double and Half Angle Fmulas To derive the double-angles fmulas, we will use the sum of two angles fmulas that we developed in the last section. We will let α θ and β θ: cos(θ) cos(θ + θ)
«Συμπεριφορά μαθητών δευτεροβάθμιας εκπαίδευσης ως προς την κατανάλωση τροφίμων στο σχολείο»
ΑΡΙΣΤΟΤΕΛΕΙΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΘΕΣΣΑΛΟΝΙΚΗΣ ΓΕΩΠΟΝΙΚΗ ΣΧΟΛΗ ΜΕΤΑΠΤΥΧΙΑΚΟ ΠΡΟΓΡΑΜΜΑ ΣΠΟΥΔΩΝ ΕΙΔΙΚΕΥΣΗ «ΑΓΡΟΤΙΚΗ ΟΙΚΟΝΟΜΙΑ» «Συμπεριφορά μαθητών δευτεροβάθμιας εκπαίδευσης ως προς την κατανάλωση τροφίμων στο
HOMEWORK 4 = G. In order to plot the stress versus the stretch we define a normalized stretch:
HOMEWORK 4 Problem a For the fast loading case, we want to derive the relationship between P zz and λ z. We know that the nominal stress is expressed as: P zz = ψ λ z where λ z = λ λ z. Therefore, applying
Approximation of distance between locations on earth given by latitude and longitude
Approximation of distance between locations on earth given by latitude and longitude Jan Behrens 2012-12-31 In this paper we shall provide a method to approximate distances between two points on earth
PENGARUHKEPEMIMPINANINSTRUKSIONAL KEPALASEKOLAHDAN MOTIVASI BERPRESTASI GURU TERHADAP KINERJA MENGAJAR GURU SD NEGERI DI KOTA SUKABUMI
155 Lampiran 6 Yayan Sumaryana, 2014 PENGARUHKEPEMIMPINANINSTRUKSIONAL KEPALASEKOLAHDAN MOTIVASI BERPRESTASI GURU TERHADAP KINERJA MENGAJAR GURU SD NEGERI DI KOTA SUKABUMI Universitas Pendidikan Indonesia
ΤΕΧΝΟΛΟΓΙΚΟ ΕΚΠΑΙ ΕΥΤΙΚΟ Ι ΡΥΜΑ ΚΡΗΤΗΣ ΣΧΟΛΗ ΙΟΙΚΗΣΗΣ ΚΑΙ ΟΙΚΟΝΟΜΙΑΣ ΤΜΗΜΑ ΙΟΙΚΗΣΗΣ ΕΠΙΧΕΙΡΗΣΕΩΝ ΠΤΥΧΙΑΚΗ ΕΡΓΑΣΙΑ
ΤΕΧΝΟΛΟΓΙΚΟ ΕΚΠΑΙ ΕΥΤΙΚΟ Ι ΡΥΜΑ ΚΡΗΤΗΣ ΣΧΟΛΗ ΙΟΙΚΗΣΗΣ ΚΑΙ ΟΙΚΟΝΟΜΙΑΣ ΤΜΗΜΑ ΙΟΙΚΗΣΗΣ ΕΠΙΧΕΙΡΗΣΕΩΝ ΠΤΥΧΙΑΚΗ ΕΡΓΑΣΙΑ Το franchising ( δικαιόχρηση ) ως µέθοδος ανάπτυξης των επιχειρήσεων λιανικού εµπορίου
A life-table metamodel to support management of data deficient species, exemplified in sturgeons and shads. Electronic Supplementary Material
A life-table metamodel to support management of data deficient species, exemplified in sturgeons and shads Electronic Supplementary Material Ivan Jarić 1,2*, Jörn Gessner 1 and Mirjana Lenhardt 3 1 Leibniz-Institute
Homework 8 Model Solution Section
MATH 004 Homework Solution Homework 8 Model Solution Section 14.5 14.6. 14.5. Use the Chain Rule to find dz where z cosx + 4y), x 5t 4, y 1 t. dz dx + dy y sinx + 4y)0t + 4) sinx + 4y) 1t ) 0t + 4t ) sinx
27/2/2013
Μοντέλα και μοντελοποίηση Βασικές έννοιες και ορισμοί Αθανάσιος Σταυρακούδης http://stavrakoudis.econ.uoi.gr 27/2/2013 Διάστημα και μοντελοποίηση Clive W.J. Granger Cambridge University Press 1999 http://bit.ly/emecom
ON NEGATIVE MOMENTS OF CERTAIN DISCRETE DISTRIBUTIONS
Pa J Statist 2009 Vol 25(2), 135-140 ON NEGTIVE MOMENTS OF CERTIN DISCRETE DISTRIBUTIONS Masood nwar 1 and Munir hmad 2 1 Department of Maematics, COMSTS Institute of Information Technology, Islamabad,
CSAE WPS/2009-06 Figure 1: Cut Flower Exports from Kenya, 1995-2007 Table 1: Firms in Areas with and w/out Conflict Panel A - Export Records Variable Observations Mean in No-Conflict
" # $ % # &' %# ()( ( * +, $- * $- # (, + ' ' * ',' ' #. * + / /, * #. * *, /# 0, ' /# " /, 1 * * / +, /# " 2, *, * # 0 ' * * / / #
! " # $ % # &' %# ()( ( * +, $- * $- # (, + ' ' * ',' ' #. * + / /, * #. * *, /# 0, ' /# " /, 1 * * / +, /# " 2, *, * # 0 ' * * / / # ( * +,,#. )' ",', % ' - 3*' 4, 3' " - # / */ 5 5##/### # 0 # # # #
1. A fully continuous 20-payment years, 30-year term life insurance of 2000 is issued to (35). You are given n A 1
Chapter 7: Exercises 1. A fully continuous 20-payment years, 30-year term life insurance of 2000 is issued to (35). You are given n A 1 35+n:30 n a 35+n:20 n 0 0.068727 11.395336 10 0.097101 7.351745 25
Εργαστήριο στατιστικής Στατιστικό πακέτο S.P.S.S.
Σημειώσεις για το μάθημα Εργαστήριο στατιστικής Στατιστικό πακέτο S.P.S.S. Παπάνα Αγγελική E mail: papanagel@yahoo.gr, agpapana@gen.auth.gr Α.Τ.Ε.Ι. Θεσσαλονίκης ΠΑΡΑΡΤΗΜΑ ΚΑΤΕΡΙΝΗΣ Τμήμα Τυποποίησης και
ΕΛΛΗΝΙΚΗ ΔΗΜΟΚΡΑΤΙΑ ΠΑΝΕΠΙΣΤΗΜΙΟ ΚΡΗΤΗΣ. Ψηφιακή Οικονομία. Διάλεξη 10η: Basics of Game Theory part 2 Mαρίνα Μπιτσάκη Τμήμα Επιστήμης Υπολογιστών
ΕΛΛΗΝΙΚΗ ΔΗΜΟΚΡΑΤΙΑ ΠΑΝΕΠΙΣΤΗΜΙΟ ΚΡΗΤΗΣ Ψηφιακή Οικονομία Διάλεξη 0η: Basics of Game Theory part 2 Mαρίνα Μπιτσάκη Τμήμα Επιστήμης Υπολογιστών Best Response Curves Used to solve for equilibria in games
w o = R 1 p. (1) R = p =. = 1
Πανεπιστήµιο Κρήτης - Τµήµα Επιστήµης Υπολογιστών ΗΥ-570: Στατιστική Επεξεργασία Σήµατος 205 ιδάσκων : Α. Μουχτάρης Τριτη Σειρά Ασκήσεων Λύσεις Ασκηση 3. 5.2 (a) From the Wiener-Hopf equation we have:
4.6 Autoregressive Moving Average Model ARMA(1,1)
84 CHAPTER 4. STATIONARY TS MODELS 4.6 Autoregressive Moving Average Model ARMA(,) This section is an introduction to a wide class of models ARMA(p,q) which we will consider in more detail later in this
2 4 5
f Æ 2 4 5 6 e D S. 7 8 9 : H ; BhL Ä,giU W? i?t ),giu 2 b 6 6 6 6 6 6 6 6 6 f < 43 3 3 2 2 2 44 45 46 w wã4 w wã4 BhL Ä,giU W? i?t ),giu 2 e D S ^ 2 M^ e D S ^ M^ Figure 1 US GDP and Exports, 1980-1994
Matrices and Determinants
Matrices and Determinants SUBJECTIVE PROBLEMS: Q 1. For what value of k do the following system of equations possess a non-trivial (i.e., not all zero) solution over the set of rationals Q? x + ky + 3z
Supplementary figures
A Supplementary figures a) DMT.BG2 0.87 0.87 0.72 20 40 60 80 100 DMT.EG2 0.93 0.85 20 40 60 80 EMT.MG3 0.85 0 20 40 60 80 20 40 60 80 100 20 40 60 80 100 20 40 60 80 EMT.G6 DMT/EMT b) EG2 0.92 0.85 5