Materi

1pembelajaran [.php] [100.0 KB]
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2Peran Hormon Dalam Proses Tumbuh Kembang [.pdf] [111.3 KB]
Materi perkuliahan Dr. drg. Sri Redjeki M.S. Departement Of Physiology FKG UI
PHYSIOLOGIC BASIS OF GROWTH & DEVELOPMENT Sri Redjeki Prasetyo Dept. of Physiology 1 What? ? why? ? how? ENVIRONMENT (external) Heat ? cold temp. Fully adaptable individual Gravitational force adolescent child Visible ? invisible lights baby mechanical ? chemical ? electrical energies fetus fetus 2 What? Growth dimension ? Soft tissue bones Factors? Internal development function external 3 Internal factors - External factors ?Genes ?Hormones: Controlling growth & development Regulating
3Persamaan Diferensial Eksak [.pdf] [86.5 KB]
Mata kuliah : Matematika Ekonomi Lanjutan ; Dosen : Prof. Nachrowi D. Nachrowi, Ph.D ; Fakultas : Ekonomi (S1 FEUI); Universitas Indonesia
Persamaan Diferensial dalam Ilmu Ekonomi Prof. Nachrowi Djalal Nachrowi, PhD Persamaan Diferensial Eksak ? Bila ada fungsi dua variabel F(y,t), diferensial totalnya: dF(y, t) = ( F/y ) dy + ( F/t ) dt ? Pada saat dF(y, t) = 0, (F/y) dy + ( F/t ) dt = 0, Bentuk persamaan diferensial tersebut disebut Persamaan Diferensial Eksak, karena ruas kiri merupakan diferensial dari F(y, t) secara eksak. ? Misalkan saja F( y, t ) = y2 t + k; k: kontstan Diferensial totalnya, dF = 2y t dy + y2 dt, maka persamaan dife
4Granger Causality Test [.pdf] [79.5 KB]
Mata kuliah : Econometrics 2; Dosen : Prof. Nachrowi D. Nachrowi, Ph.D ; Fakultas : Ekonomi (Program Magister Double Degree PPIE FEUI); Universitas Indonesia
Granger Causality Test In doing economic analysis, sometimes, we would like to know whether changes in a variable will have an impact on changes other variables To find out this phenomena more accurately, we need to learn more about Granger Causality Test. In principle, the concept is as follows: If X causes Y, then, changes of X happened first then followed by changes of Y. More specifically, if X causes Y, there are two conditions to be satisfied: 1. X can help in predicting Y. Regression of X on Y
5Vector AutoRegression (VAR) [.pdf] [124.2 KB]
Mata kuliah : Econometrics 2; Dosen : Prof. Nachrowi D. Nachrowi, Ph.D ; Fakultas : Ekonomi (Program Magister Double Degree PPIE FEUI); Universitas Indonesia
Vector AutoRegression (VAR) Background When we talk about simultaneous equations, some variables are treated as endogenous variables and the rests are treated as exogenous variables. Before estimating simultaneous equation models, we need to check first whether the equations are identified or not. This identification process assumes that lag variables (predetermined variables) only present in some equations. These assumptions are very subjective and criticized by some experts like CHRISTOPHER SIMS. Sims
6Model Dinamika Harga Pasar [.pdf] [91.0 KB]
Mata kuliah : Matematika Ekonomi Lanjutan ; Dosen : Prof. Nachrowi D. Nachrowi, Ph.D ; Fakultas : Ekonomi (S1 FEUI); Universitas Indonesia
Persamaan Diferensial dalam Ilmu Ekonomi Prof. Nachrowi Djalal Nachrowi, PhD Pengenalan: Ilustrasi Sederhana Model Dinamika Harga Pasar Misalkan saja, fungsi permintaan dan penawaran dalam suatu pasar diformulasikan sebagai berikut: Qd = - P Qs = - + P ; , > 0 ; , > 0 Menurut hasil dari kuliah terdahulu, harga keseimbangannya: P= ( +)/( +) ? Kalau harga awal, P(0), sudah sama dengan harga keseimbangan, P, berarti pasar sudah dalam keadaan seimbang dan tidak perlu dianalisis lagi tentang dinamika harga
7Cointegration Analysis and ECM [.pdf] [92.1 KB]
Mata kuliah : Econometrics 2; Dosen : Prof. Nachrowi D. Nachrowi, Ph.D ; Fakultas : Ekonomi (Program Magister Double Degree PPIE FEUI); Universitas Indonesia
Cointegration Analysis and ECM Before discussing co-integration concept, we need to talk about other relating concepts that are very important in understanding cointegration. Spurious Regression Phenomena. Suppose there are 2 random walk models yt = yt-1 + ut; ut ~ N (0,1); ut white noise xt = xt-1 + vt; vt ~ N (0,1); vt white noise From earlier discussion, yt and xt are not stationer. More over, yt and xt are not correlated (based on the way the variables are generated). However, when we regress xt on
8PRESENT VALUE of CASH FLOW. [.pdf] [66.4 KB]
Mata kuliah : Econometrics 2; Dosen : Prof. Nachrowi D. Nachrowi, Ph.D ; Fakultas : Ekonomi (Program Magister Double Degree PPIE FEUI); Universitas Indonesia
Bahan Kukiah Matematika Ekonomi Lanjutan Prof. Nachrowi Djalal Nachrowi, PhD. (C ) PRESENT VALUE of CASH FLOW. V = future value available t years from now . A = Present value . i = discount rate . A = V ( 1 + i ) -t A = V e - r t ; ; discrete case continous case . Bila ada 3 revenue dimasa datang , total revenue , 3 = Rt ( 1 + i ) - t t=1 3 ; discrete = R (t) e 0 - r t dt . ; continous. Contoh : ( i ) Berapa Present Value dari flow selama y = 2 tahun dengan constan flow D = $ 3000 da
9Analisis Dinamik: Aplikasinya dalam Ilmu Ekonomi [.pdf] [115.8 KB]
Mata kuliah : Econometrics 2; Dosen : Prof. Nachrowi D. Nachrowi, Ph.D ; Fakultas : Ekonomi (Program Magister Double Degree PPIE FEUI); Universitas Indonesia
Bahan Kuliah Matematika Ekonomi Lanjutan Prof. Nachrowi Djalal Nachrowi, PhD. 1 Analisis Dinamik: Aplikasinya dalam Ilmu Ekonomi ? Model Statik Mencari nilai dari suatu variabel yang memenuhi syarat ? syarat keseimbangan. - Variabel yang memaksimumkan keuntungan - Variabel yang meminimumkan biaya produksi FONC Keseimbangan / Equilibrium ? Model Dinamik Melihat gerakan variabel berdasarkan pada pola perubahannya . H : Populasi yang berubah dengan kecepatan / rate H(t) Ingin dilihat : - bagaimana gerak
10Time series LinierModels [.pdf] [133.7 KB]
Mata kuliah : Econometrics 2; Dosen : Prof. Nachrowi D. Nachrowi, Ph.D ; Fakultas : Ekonomi (Program Magister Double Degree PPIE FEUI); Universitas Indonesia
Time series Linier Models We have learned simple extrapolation techniques for deterministic and stochastic time series models. In addition, we also have learned stationery and non stationery time series data. ? We will develop models that can explain the movement of time series data by relating the data with : (i). Previous data (autoregressive) and / or (ii). Current and past random deviation (moving average) ? The models focus on linear models for practical reason, simple and easy. These models can be
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