Materi

41OPTIMISASI DENGAN KENDALA KETIDAKSAMAAN [.pdf] [142.5 KB]
Materi kuliah Econometrics: OPTIMISASI DENGAN KENDALA KETIDAKSAMAAN; Dosen : Prof. Nachrowi D. Nachrowi, Ph.D; Fakultas Ekonomi Universitas Indonesia
Optimisasi dalam Ilmu Ekonomi Prof. Nachrowi Djalal Nachrowi, PhD OPTIMISASI DENGAN KENDALA KETIDAKSAMAAN Aplikasi: (i) Max U U (x1 , x2, ..., x n ); S.t.: P1 x1 + P2 x2+...+ Pn x n B; x1, x2,...,x n 0 (ii) Min C = PK K + PL L; S.t.: K L Q 0 ( Q 0 > 0); K , L 0 Secara umum, maksimisasi keuntungan dan minimisasi biaya, ditulis dalam format berikut: Max = f ( x1, x2, ..., xn ); ada n produk dan m kendala S.t. : g1 ( x1, x 2......... xn ) r 1 g2 ( x1, x 2......... xn ) r 2 gm ( x1, x 2.........xn
42OPTIMISASI TANPA KENDALA [.pdf] [250.5 KB]
Materi kuliah Econometrics: OPTIMISASI TANPA KENDALA; Dosen : Prof. Nachrowi D. Nachrowi, Ph.D; Fakultas Ekonomi Universitas Indonesia
Optimisasi dalam Ilmu Ekonomi Prof. NachrowiDjalal Nachrowi, PhD OPTIMISASI TANPA KENDALA Variabel ganda 1. Fungsi Dua Variabel -Bagaimana memaksimumkan keuntungan dari prerusahaan dengan 2 produk dengan fungsi keuntungan sbb.: (Q1 ,Q2 ) = R (Q1 ,Q2) ? C (Q1 ,Q2) - Secara umum, bagaimana mencari maksimum dari fungsi 2 variabel: Z = f ( x , y ) FONC: SOSC: fx = fy = 0 ; max Syarat: (i) fx x < 0 (ii) fy y < 0 (iii) fx x . fy y - (fx y)2 > 0 fx turunan partial f terhadap x fy turunan partial f terhadap y
43ECONOMETRICS METHODOLOGY [.pdf] [13.0 KB]
Materi kuliah Econometrics: ECONOMETRICS METHODOLOGY; Dosen : Prof. Nachrowi D. Nachrowi, Ph.D; Fakultas Ekonomi Universitas Indonesia
ECONOMETRICS METHODOLOGY (Revisited: in relation to all methods given) 1. 2. 3. 4. 5. 6. Developing a hypothesis Offer a model to test the hypothesis Estimate parameter model Model verification Make some prediction Use the model for policy analysis Issues to be address: single equation vs. multiple equations qualitative vs. quantitative variables reliability of the estimators etc
44How to Deal with Missing Observations [.pdf] [58.8 KB]
Materi kuliah Econometrics: How to Deal with Missing Observations, Dosen : Prof. Nachrowi D. Nachrowi, Ph.D; Fakultas Ekonomi Universitas Indonesia
How to Deal with Missing Observations In doing econometric analyses, sometimes we are facing problems of missing observations. How to deal with this problem? There is no best method to solve this; however, there are several suggested approaches to overcome this missing observations. Suppose we have the following model: Yi = 1 + 2 Xi + i If we have N observations of Y and X variables, we can use OLS method to estimate the model. What happened if we have M additional observations of Y variable but no ad
45SIMULTANEOUS EQUATION MODEL [.pdf] [99.6 KB]
Materi kuliah Econometrics: SIMULTANEOUS EQUATION MODEL, Dosen : Prof. Nachrowi D. Nachrowi, Ph.D Fakultas Ekonomi Universitas Indonesia
SIMULTANEOUS EQUATION MODEL ONE Equation Model (revisited) Characteristics: One dependent variable (Y): as a regressand One ore more independent variables (X): as regressors One way causality relationship: from X to Y no feedback Illustration: Income can explain consumption patterns. However, consumption does not influence income. Income and education level can explain insurance consumption. But, insurance consumption does not influence education level. Models of TWO or More Equations There are cases
46Panel Data Model [.pdf] [108.7 KB]
Materi kuliah Econometrics: Panel Data Model, Dosen : Prof. Nachrowi D. Nachrowi, Ph.D Fakultas Ekonomi Universitas Indonesia
Panel Data Model Panel data can be defined as data that are collected as a cross section but then they are observed periodically. For example, the economic growths of each province in Indonesia from 1971-2009; or the profit of companies listed in ISX observed from 1991-2009 Panel data can be very useful for researchers who are interested in analyzing something that can not be done using either time series data or cross section data only. For example, we would like to develop a model that can explain the
47MULTINOMIAL LOGITMODEL [.pdf] [64.6 KB]
Materi kuliah Econometrics: MULTINOMIAL LOGIT MODEL, Dosen : Prof. Nachrowi D. Nachrowi, Ph.D Fakultas Ekonomi Universitas Indonesia
MULTINOMIAL LOGIT MODEL Case: Investment decisions (i). Time Deposit (ii). Stocks (iii). Bonds (iv). Certificate of BI Case: Choice of transportation modes (i.) train, (ii) bus, or public transportation non train (iii) car (iv) motorcycle Logistic Model with 4 categories have 3 logit functions: (i). Logit Function for Y = 1 relative to logit function for Y = 0 (ii). Logit Function for Y = 2 relative to logit function for Y = 0 (iii). Logit Function for Y = 3 relative to logit function for Y = 0 Category
48Syllabus of Econometrics (Revision Edition) [.pdf] [25.2 KB]
Syllabus of Econometrics (Revision Edition), Dosen : Prof. Nachrowi D. Nachrowi, Ph.D Fakultas Ekonomi Universitas Indonesia
University of Indonesia Graduate Program in Economics 2009 / 2010 Syllabus of Econometrics (Revision Edition) Instructor: Prof. Nachrowi Djalal Nachrowi, PhD Teaching Assistant: Mohamad Soleh Nurzaman, SE, MIDEc I. Course Description Econometrics is a method or techniques that can be used in analyzing economics phenomena quantitatively based on statistical approach. Specifically, this course focuses on analyzing and using econometric models in formulating economics application in general. However, our d
49Econometrics : LPM vs. LOGIT [.pdf] [62.6 KB]
Materi kuliah Econometrics: LPM vs LOGIT, Dosen : Prof. Nachrowi D. Nachrowi, Ph.D Fakultas Ekonomi Universitas Indonesia
LPM vs. LOGIT 1.LPM: Linear Probability Model (a review) pi = E (Yi = 1 Xi) = 1 + 2 Xi X : represents a family income Y = 1 ; if a family has a house 0 ; if a family has no house Observations: (i). pi : the value can be out of range (beyond 0 and 1) because the value of 1 + 2 Xi can be anything (ii). This method can be used only if the value of 1 + 2 Xi between 0 and 1 2.Logit (logistic distribution function): a method that guarantee that the value of p between 0 and 1 Define: or p i = E (Yi = 1 X i )
50THE PRESENCE OF CATEGORICAL VAR IN ECONOMETRIC MODELS [.pdf] [53.8 KB]
Materi kuliah Econometrics: Lecture 10, Dosen : Prof. Nachrowi D. Nachrowi, Ph.D Fakultas Ekonomi Universitas Indonesia
THE PRESENCE OF CATEGORICAL VAR IN ECONOMETRIC MODELS 1. Categorical Variables as regressors Illustration: Male-female; Urban-rural; Yes -No; Foreign-Domestic Level of Educations: SD, SLTP, SLTA, D3, S1, S2, S3 Problems? No problem; as long as: Categorical variables are properly defined Be careful in interpreting the model 2. Categorical Variable as a regressand Ilustration: Invest on stock market or not? Own house or not Choice of Investments: Stock, Gold, Properties, Saving Deposits Choice of Transpor
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