Reading Assignment. Distributed Lag and Autoregressive Models. Chapter 17. Kennedy: Chapters 10 and 13. AREC-ECON 535 Lec G 1
|
|
- Adelia Washington
- 5 years ago
- Views:
Transcription
1 Reading Assignment Distributed Lag and Autoregressive Models Chapter 17. Kennedy: Chapters 10 and 13. AREC-ECON 535 Lec G 1
2 Distributed Lag and Autoregressive Models Distributed lag model: y t = α + β 0 x t + β 1 x t β k x t-k + e t. Autoregressive model: y t = α + x t β + φ y t-1 + e t. Examine where the shock enters a resting model and measuring the impact on y t through time. These models work very well. Forecast well and explain well. But are hard to publish because our theory is fairly void of dynamics these empirical models don t necessarily come from economic models consistent with theory or similar empirical models can come from rather different economic models. There is also the following problem. There are fundamental conceptual differences, but similar empirical representations. This is a problem... Last, both sides of the model can be nonstationary that is not explained by a deterministic trend variable and then we can have a spurious regression. AREC-ECON 535 Lec G 2
3 Interpretation of distributed lag model Impact Multipliers: Measure change in y given a change in x after: 0 period β 0 1 period β 1... k periods β k k+1 periods 0 Total Impact: β 0 + β β k Interpretation of autoregressive model Cumulative Impact: Measure cumulative change in y given a change in x after: 0 period β 1 period β (φ) 2 periods β (φ + φ 2 )... k periods β (φ + φ φ k ) Total Impact: β i φ i = β / (1 φ). (And we could start the impacts, not in the 0 th period, but in the 1 st period.) AREC-ECON 535 Lec G 3
4 Reasons for Distributed Lag and Other Dynamic Models 1. Psychological reasons: consumers form habits and producers must observe incentives repeatedly. 2. Technological reasons: technology may be slow to adopt or implement. 3. Institutional reasons: institutions may limit economic choices and speed of adjustment. Notice: these reasons are all ad hoc. Explicit dynamic models of economic behavior are needed. And are being developed. And are not empirically trivial... AREC-ECON 535 Lec G 4
5 Start with a Distributed Lag Model y t = α + β 0 x t + β 1 x t β k x t-k + e t. How to choose k? Make use of institutional knowledge relevant to that market... But we must be careful about data mining, degrees of freedom, and collinearity. The following two lines are very, very, very important... Assume some structure on β i s. Specifically, β k smaller than β 0 (or β 1 ), and the β i s transition back through time is smooth. AREC-ECON 535 Lec G 5
6 Infinite Lag: Geometric Lag y t = α + β 0 x t + β 1 x t-1 + β 2 x t e t y t = α + β s x t-s + e t s=0 assume β s = β 0 ω s where 0 < ω < 1 and s = 0, 1, 2,... y t = α + β 0 (x t + ω x t-1 + ω 2 x t ) + e t y t = α + β 0 ω s x t-s + e t s=0 (Count the parameters before and after ) AREC-ECON 535 Lec G 6
7 Transform to make estimable. y t = α + β 0 (x t + ω x t-1 + ω 2 x t ) + e t (lag both sides) y t-1 = α + β 0 (x t-1 + ω x t ) + e t-1 (Multiply by ω) ωy t-1 = ωα + β 0 (ωx t-1 + ω 2 x t ) + ωe t-1 Subtract 3 rd equation from first and cancelling a lot of terms results in y t = α (1 ω) + β 0 x t + ω y t-1 + (e t ωe t-1 ) which is okay in an undergraduate course to represent as y t = α (1 ω) + β 0 x t + ω y t-1 + u t but not in a graduate course. Careful: error term contains a moving average component, lagged dependent variable is stochastic, and DW-d is invalid. AREC-ECON 535 Lec G 7
8 Adaptive Expectations Economic Model: a rationalization for geometric lag. y t = α + β x t + e t Action by economic agent (y t ) depends on an unobservable expectations variable (x t ) and assume x t x t-1 = φ (x t x t-1 ) where 0 < φ 1 or x t = φ x t + (1 φ ) x t-1 so that the expectations are a combination of actual conditions and previous expectations revising past expectations based on the current condition. Big Picture: We are starting with a structural model or an economic model and deriving a reduced form or an estimable econometric model. Then we will attempt to recover the structural parameters from the reduced form parameters. AREC-ECON 535 Lec G 8
9 Substitute expectation equation into model y t = α + β [φ x t + (1 φ ) x t-1 ] + e t. Lag model one period, multiply lagged model by (1 φ), and subtract result from model y t = φα + φβ x t + (1 φ ) y t-1 + (e t (1 φ)e t-1 ). y t = β 0 + β 1 x t + β 2 y t-1 + (e t β 2 e t-1 ) (or y t = β 0 + β 1 x t + β 2 y t-1 + u t but only for novices...) β 2 = (1 φ) so that φ = 1 β 2 β 1 = φβ so that β = β 1 / φ So we say φ and β are identified. This means that we can go from parameters estimated in the reduced form econometric model back to parameters in the structural economic model. AREC-ECON 535 Lec G 9
10 Partial Adjustment Economic Model: Another rationalization. y t = α + β x t + e t Action by economic agent (y t ) depends variable (x t ) but is only partial of what was intended y t - y t-1 = δ (y t y t-1 ) where 0 < δ 1 or y t = δ y t + (1 δ ) y t-1. Substitute model into adjustment equation y t = δ [α + β x t + e t ] + (1 δ)y t-1 y t = δ α + δ β x t + (1 δ )y t-1 + δ e t. y t = β 0 + β 1 x t + β 2 y t-1 + u t β 2 = (1 δ) so that δ = 1 β 2 β 1 = δ β so that β = β 1 / δ and β 0 = δ α so that α = β 0 / δ. AREC-ECON 535 Lec G 10
11 Finite Lag: Polynomial Distributed Lag This is a sharp contrast with the infinite lag approach. y t = α + β 0 x t + β 1 x t β k x t-k + e t k y t = α + β i x t-i + e t i = 0 where β i = ω 0 + ω 1 i + ω 2 i ω m i m and m < k. Polynomial enforces a relationship between the β i s. Estimable model: assume an order of polynomial, substitute polynomial into model, solve for ω s. (Count the parameters before and after ) AREC-ECON 535 Lec G 11
12 Example: 3rd order polynomial and lag length of 5 5 y t = α + β i x t-i + e t i = 0 (Number of slope parameters?) β i = (ω 0 + ω 1 i + ω 2 i 2 + ω 3 i 3 ) (Number of parameters with restriction?) 5 y t = α + (ω 0 + ω 1 i + ω 2 i 2 + ω 3 i 3 ) x t-i + e t i = y t = α + ω 0 x t-i + ω 1 i x t-i + ω 2 i 2 x t-i + ω 3 i 3 x t-i + e t i = 0 i = 0 i = 0 i = 0 y t = α + ω 0 z 0t + ω 1 z 1t + ω 2 z 2t + ω 3 z 3t + e t (Software often reports this model.) where z 0t = x t + x t-1 + x t-2 + x t-3 + x t-4 + x t-5 z 1t = x t x t x t x t x t-5 z 2t = x t x t x t x t x t-5 z 3t = x t x t x t x t x t-5 AREC-ECON 535 Lec G 12
13 Estimate ω s using Z s and recover β s through restrictions β i = ω 0 + ω 1 i + ω 2 i 2 + ω 3 i 3 β 0 = ω 0 β 1 = ω 0 + ω 1 + ω 2 + ω 3 β 2 = ω 0 + ω ω ω 3 8 β 3 = ω 0 + ω ω ω 3 27 β 4 = ω 0 + ω ω ω 3 64 β k =... V(β)s are recovered by using the formula for the variance of a random variable which is a linear combination of random variables. V( k i ω i ) = (SAS, EViews, and most packages report these also.) Computer software packages will perform polynomial distributed lagged regressions. AREC-ECON 535 Lec G 13
14 Endpoint restrictions: β i = ω 0 + ω 1 i + ω 2 i 2 + ω 3 i 3 Back: β k+1 = ω 0 + ω 1 (k+1) + ω 2 (k+1) 2 + ω 3 (k+1) 3 = 0 Front: β -1 = ω 0 - ω 1 + ω 2 - ω 3 = 0 Graphically, β t Restrictions imply hypotheses which can be tested. Back has a lot of intuition but the front does not however, the mathematics works. AREC-ECON 535 Lec G 14
15 Other Tests: Choosing order of m and lag length k? Polynomial order m: H 0 : ω m = 0 t-test. Lag order k: H 0 : β k = 0 t-test. Be careful of data mining. Order m should be small but k could be large. Procedure: Choose a large lag length and polynomial order. (You must understand the market or action you are modeling. Study what you are modeling.) Test down starting with lag length and then test polynomial order. Stop where last lag and polynomial element are insignificant. Add endpoint restriction(s). Want polynomial and endpoints to be binding but not too binding not contradict the data. Do not want the kth variable or the mth polynomial element to be significant. Do not want to reject endpoint restrictions. AREC-ECON 535 Lec G 15
16 Alternative Mechanical Procedure: Use an Information Criteria to determine lag length, Schwarz: SC = ln( σ 2 ) + k ln( T ) σ 2 maximum likelihood estimate of error variance k lag length T sample size. Choose k to minimize SC. Increasing k makes the model fit better but also makes the penalty go up. There are other Information Criteria. Many econometric packages will perform polynomial distributed lags and will test the order of the polynomial and endpoint restrictions. ex) EViews ls: y c pdl(x, Lag, Order, Restrictions) SAS has PROC PDLREG Practically, Need good reason for polynomial greater than 3rd order. Use back endpoint restrictions. AREC-ECON 535 Lec G 16
17 Autoregressive Models Geometric lag: y t = α (1 ω) + β 0 x t + ω y t-1 + (e t ω e t-1 ) Adaptive expectations: y t = φα + φβ x t + (1 φ ) y t-1 + (e t (1 φ ) e t-1 ) Partial adjustment: y t = δ α + δ β x t + (1 δ ) y t-1 + δ e t Autoregressive model: y t = α + x t β + φ y t-1 + u t A lot of dynamic models look alike in empirical implementation. Especially, if the error term has serial correlation. So, it s tough to recover the structural economic model from the econometric time series model. Thus, your alternative models are not different enough for statistical methods to say which is correct... AREC-ECON 535 Lec G 17
18 Problems with using OLS with autoregressive model: by definition we may make the error term: u t = e t λ e t-1 Serial correlation: E(u t u t-1 ) = -λ σ 2 Errors correlated with independent variable: Cov(y t-1, u t ) = -λ σ 2 biased and inconsistent estimates. Serial correlation in a model with a lagged dependent variable is very very very bad. AREC-ECON 535 Lec G 18
19 Tests for Serial Correlation 1. Durbin h statistic: h T ( utu t1) ˆ where ˆ and... y 2 t1 1 T ( Var( ˆ)) ( u ) t... h ~ N(0,1) under H 0 : ρ = 0. Notice the test is invalid if Var(φ) > BG test. AREC-ECON 535 Lec G 19
20 What to do if you have serial correlation in autoregressive model? First, theory better say that is the model. Second, use an instrumental variable. Need a variable correlated with y t-1 but not with u t. y t = f(y t-1, z 1t, z 2t,... ) + u t y t = g(x t, x t-1,... ) + v t More on instrumental variables with simultaneous equations... Third, maximum likelihood. AREC-ECON 535 Lec G 20
21 Spatial Models Spatial Autocorrelation y = X β + ε and ε = λwε + v or y i = x i β + ε i ε i = λ Σ j w ji ε ji + v i Spatial Autoregression y = X β + ρwy + ε y i = x i β + ρw ji y ji + ε i where W is a weighting matrix that determines neighbors and possibly distance. Suppose you have cross sectional data (e.g., U.S. counties or states). Then could you pick up one and place it somewhere else in the country and get the same result? Doubtful. That s spatial dependence. AREC-ECON 535 Lec G 21
22 What s W look like with temporal data? Temporal neighbors are easy. W What s W look like with temporal data? Spatial neighbors less so. You are going to need software. W AREC-ECON 535 Lec G 22
Reading Assignment. Serial Correlation and Heteroskedasticity. Chapters 12 and 11. Kennedy: Chapter 8. AREC-ECON 535 Lec F1 1
Reading Assignment Serial Correlation and Heteroskedasticity Chapters 1 and 11. Kennedy: Chapter 8. AREC-ECON 535 Lec F1 1 Serial Correlation or Autocorrelation y t = β 0 + β 1 x 1t + β x t +... + β k
More informationHeteroskedasticity. y i = β 0 + β 1 x 1i + β 2 x 2i β k x ki + e i. where E(e i. ) σ 2, non-constant variance.
Heteroskedasticity y i = β + β x i + β x i +... + β k x ki + e i where E(e i ) σ, non-constant variance. Common problem with samples over individuals. ê i e ˆi x k x k AREC-ECON 535 Lec F Suppose y i =
More informationLecture 6: Dynamic Models
Lecture 6: Dynamic Models R.G. Pierse 1 Introduction Up until now we have maintained the assumption that X values are fixed in repeated sampling (A4) In this lecture we look at dynamic models, where the
More informationUnivariate ARIMA Models
Univariate ARIMA Models ARIMA Model Building Steps: Identification: Using graphs, statistics, ACFs and PACFs, transformations, etc. to achieve stationary and tentatively identify patterns and model components.
More informationTopic 4 Unit Roots. Gerald P. Dwyer. February Clemson University
Topic 4 Unit Roots Gerald P. Dwyer Clemson University February 2016 Outline 1 Unit Roots Introduction Trend and Difference Stationary Autocorrelations of Series That Have Deterministic or Stochastic Trends
More informationEC408 Topics in Applied Econometrics. B Fingleton, Dept of Economics, Strathclyde University
EC408 Topics in Applied Econometrics B Fingleton, Dept of Economics, Strathclyde University Applied Econometrics What is spurious regression? How do we check for stochastic trends? Cointegration and Error
More information7 Introduction to Time Series Time Series vs. Cross-Sectional Data Detrending Time Series... 15
Econ 495 - Econometric Review 1 Contents 7 Introduction to Time Series 3 7.1 Time Series vs. Cross-Sectional Data............ 3 7.2 Detrending Time Series................... 15 7.3 Types of Stochastic
More informationExercise Sheet 6: Solutions
Exercise Sheet 6: Solutions R.G. Pierse 1. (a) Regression yields: Dependent Variable: LC Date: 10/29/02 Time: 18:37 Sample(adjusted): 1950 1985 Included observations: 36 after adjusting endpoints C 0.244716
More informationApplied Econometrics. Applied Econometrics. Applied Econometrics. Applied Econometrics. What is Autocorrelation. Applied Econometrics
Autocorrelation 1. What is 2. What causes 3. First and higher orders 4. Consequences of 5. Detecting 6. Resolving Learning Objectives 1. Understand meaning of in the CLRM 2. What causes 3. Distinguish
More informationEcon 300/QAC 201: Quantitative Methods in Economics/Applied Data Analysis. 17th Class 7/1/10
Econ 300/QAC 201: Quantitative Methods in Economics/Applied Data Analysis 17th Class 7/1/10 The only function of economic forecasting is to make astrology look respectable. --John Kenneth Galbraith show
More informationECON3327: Financial Econometrics, Spring 2016
ECON3327: Financial Econometrics, Spring 2016 Wooldridge, Introductory Econometrics (5th ed, 2012) Chapter 11: OLS with time series data Stationary and weakly dependent time series The notion of a stationary
More information1 Regression with Time Series Variables
1 Regression with Time Series Variables With time series regression, Y might not only depend on X, but also lags of Y and lags of X Autoregressive Distributed lag (or ADL(p; q)) model has these features:
More information7 Introduction to Time Series
Econ 495 - Econometric Review 1 7 Introduction to Time Series 7.1 Time Series vs. Cross-Sectional Data Time series data has a temporal ordering, unlike cross-section data, we will need to changes some
More informationEconometrics Summary Algebraic and Statistical Preliminaries
Econometrics Summary Algebraic and Statistical Preliminaries Elasticity: The point elasticity of Y with respect to L is given by α = ( Y/ L)/(Y/L). The arc elasticity is given by ( Y/ L)/(Y/L), when L
More informationModel Mis-specification
Model Mis-specification Carlo Favero Favero () Model Mis-specification 1 / 28 Model Mis-specification Each specification can be interpreted of the result of a reduction process, what happens if the reduction
More informationLATVIAN GDP: TIME SERIES FORECASTING USING VECTOR AUTO REGRESSION
LATVIAN GDP: TIME SERIES FORECASTING USING VECTOR AUTO REGRESSION BEZRUCKO Aleksandrs, (LV) Abstract: The target goal of this work is to develop a methodology of forecasting Latvian GDP using ARMA (AutoRegressive-Moving-Average)
More information7. Integrated Processes
7. Integrated Processes Up to now: Analysis of stationary processes (stationary ARMA(p, q) processes) Problem: Many economic time series exhibit non-stationary patterns over time 226 Example: We consider
More informationTAKEHOME FINAL EXAM e iω e 2iω e iω e 2iω
ECO 513 Spring 2015 TAKEHOME FINAL EXAM (1) Suppose the univariate stochastic process y is ARMA(2,2) of the following form: y t = 1.6974y t 1.9604y t 2 + ε t 1.6628ε t 1 +.9216ε t 2, (1) where ε is i.i.d.
More informationOutline. Overview of Issues. Spatial Regression. Luc Anselin
Spatial Regression Luc Anselin University of Illinois, Urbana-Champaign http://www.spacestat.com Outline Overview of Issues Spatial Regression Specifications Space-Time Models Spatial Latent Variable Models
More informationCHAPTER 6: SPECIFICATION VARIABLES
Recall, we had the following six assumptions required for the Gauss-Markov Theorem: 1. The regression model is linear, correctly specified, and has an additive error term. 2. The error term has a zero
More information7. Integrated Processes
7. Integrated Processes Up to now: Analysis of stationary processes (stationary ARMA(p, q) processes) Problem: Many economic time series exhibit non-stationary patterns over time 226 Example: We consider
More informationShort T Panels - Review
Short T Panels - Review We have looked at methods for estimating parameters on time-varying explanatory variables consistently in panels with many cross-section observation units but a small number of
More informationIris Wang.
Chapter 10: Multicollinearity Iris Wang iris.wang@kau.se Econometric problems Multicollinearity What does it mean? A high degree of correlation amongst the explanatory variables What are its consequences?
More information05 Regression with time lags: Autoregressive Distributed Lag Models. Andrius Buteikis,
05 Regression with time lags: Autoregressive Distributed Lag Models Andrius Buteikis, andrius.buteikis@mif.vu.lt http://web.vu.lt/mif/a.buteikis/ Introduction The goal of a researcher working with time
More informationEconometrics. Week 4. Fall Institute of Economic Studies Faculty of Social Sciences Charles University in Prague
Econometrics Week 4 Institute of Economic Studies Faculty of Social Sciences Charles University in Prague Fall 2012 1 / 23 Recommended Reading For the today Serial correlation and heteroskedasticity in
More informationNon-Stationary Time Series and Unit Root Testing
Econometrics II Non-Stationary Time Series and Unit Root Testing Morten Nyboe Tabor Course Outline: Non-Stationary Time Series and Unit Root Testing 1 Stationarity and Deviation from Stationarity Trend-Stationarity
More informationAn estimate of the long-run covariance matrix, Ω, is necessary to calculate asymptotic
Chapter 6 ESTIMATION OF THE LONG-RUN COVARIANCE MATRIX An estimate of the long-run covariance matrix, Ω, is necessary to calculate asymptotic standard errors for the OLS and linear IV estimators presented
More informationLectures 5 & 6: Hypothesis Testing
Lectures 5 & 6: Hypothesis Testing in which you learn to apply the concept of statistical significance to OLS estimates, learn the concept of t values, how to use them in regression work and come across
More informationTesting Restrictions and Comparing Models
Econ. 513, Time Series Econometrics Fall 00 Chris Sims Testing Restrictions and Comparing Models 1. THE PROBLEM We consider here the problem of comparing two parametric models for the data X, defined by
More informationHeteroscedasticity and Autocorrelation
Heteroscedasticity and Autocorrelation Carlo Favero Favero () Heteroscedasticity and Autocorrelation 1 / 17 Heteroscedasticity, Autocorrelation, and the GLS estimator Let us reconsider the single equation
More informationFöreläsning /31
1/31 Föreläsning 10 090420 Chapter 13 Econometric Modeling: Model Speci cation and Diagnostic testing 2/31 Types of speci cation errors Consider the following models: Y i = β 1 + β 2 X i + β 3 X 2 i +
More informationSection 2 NABE ASTEF 65
Section 2 NABE ASTEF 65 Econometric (Structural) Models 66 67 The Multiple Regression Model 68 69 Assumptions 70 Components of Model Endogenous variables -- Dependent variables, values of which are determined
More informationTime Series Methods. Sanjaya Desilva
Time Series Methods Sanjaya Desilva 1 Dynamic Models In estimating time series models, sometimes we need to explicitly model the temporal relationships between variables, i.e. does X affect Y in the same
More informationCHAPTER 21: TIME SERIES ECONOMETRICS: SOME BASIC CONCEPTS
CHAPTER 21: TIME SERIES ECONOMETRICS: SOME BASIC CONCEPTS 21.1 A stochastic process is said to be weakly stationary if its mean and variance are constant over time and if the value of the covariance between
More informationLecture 2: Univariate Time Series
Lecture 2: Univariate Time Series Analysis: Conditional and Unconditional Densities, Stationarity, ARMA Processes Prof. Massimo Guidolin 20192 Financial Econometrics Spring/Winter 2017 Overview Motivation:
More informationAn overview of applied econometrics
An overview of applied econometrics Jo Thori Lind September 4, 2011 1 Introduction This note is intended as a brief overview of what is necessary to read and understand journal articles with empirical
More informationAuto correlation 2. Note: In general we can have AR(p) errors which implies p lagged terms in the error structure, i.e.,
1 Motivation Auto correlation 2 Autocorrelation occurs when what happens today has an impact on what happens tomorrow, and perhaps further into the future This is a phenomena mainly found in time-series
More informationTime Series. April, 2001 TIME SERIES ISSUES
Time Series Nathaniel Beck Department of Political Science University of California, San Diego La Jolla, CA 92093 beck@ucsd.edu http://weber.ucsd.edu/ nbeck April, 2001 TIME SERIES ISSUES Consider a model
More informationLecture 5: Unit Roots, Cointegration and Error Correction Models The Spurious Regression Problem
Lecture 5: Unit Roots, Cointegration and Error Correction Models The Spurious Regression Problem Prof. Massimo Guidolin 20192 Financial Econometrics Winter/Spring 2018 Overview Stochastic vs. deterministic
More informationAn Introduction to Parameter Estimation
Introduction Introduction to Econometrics An Introduction to Parameter Estimation This document combines several important econometric foundations and corresponds to other documents such as the Introduction
More informationChristopher Dougherty London School of Economics and Political Science
Introduction to Econometrics FIFTH EDITION Christopher Dougherty London School of Economics and Political Science OXFORD UNIVERSITY PRESS Contents INTRODU CTION 1 Why study econometrics? 1 Aim of this
More informationECON The Simple Regression Model
ECON 351 - The Simple Regression Model Maggie Jones 1 / 41 The Simple Regression Model Our starting point will be the simple regression model where we look at the relationship between two variables In
More informationProf. Dr. Roland Füss Lecture Series in Applied Econometrics Summer Term Introduction to Time Series Analysis
Introduction to Time Series Analysis 1 Contents: I. Basics of Time Series Analysis... 4 I.1 Stationarity... 5 I.2 Autocorrelation Function... 9 I.3 Partial Autocorrelation Function (PACF)... 14 I.4 Transformation
More informationCourse information EC2020 Elements of econometrics
Course information 2015 16 EC2020 Elements of econometrics Econometrics is the application of statistical methods to the quantification and critical assessment of hypothetical economic relationships using
More information9. AUTOCORRELATION. [1] Definition of Autocorrelation (AUTO) 1) Model: y t = x t β + ε t. We say that AUTO exists if cov(ε t,ε s ) 0, t s.
9. AUTOCORRELATION [1] Definition of Autocorrelation (AUTO) 1) Model: y t = x t β + ε t. We say that AUTO exists if cov(ε t,ε s ) 0, t s. ) Assumptions: All of SIC except SIC.3 (the random sample assumption).
More informationLikely causes: The Problem. E u t 0. E u s u p 0
Autocorrelation This implies that taking the time series regression Y t X t u t but in this case there is some relation between the error terms across observations. E u t 0 E u t E u s u p 0 Thus the error
More informationUnivariate linear models
Univariate linear models The specification process of an univariate ARIMA model is based on the theoretical properties of the different processes and it is also important the observation and interpretation
More informationEconometric Forecasting Overview
Econometric Forecasting Overview April 30, 2014 Econometric Forecasting Econometric models attempt to quantify the relationship between the parameter of interest (dependent variable) and a number of factors
More informationProblem Set #6: OLS. Economics 835: Econometrics. Fall 2012
Problem Set #6: OLS Economics 835: Econometrics Fall 202 A preliminary result Suppose we have a random sample of size n on the scalar random variables (x, y) with finite means, variances, and covariance.
More informationEcon 424 Time Series Concepts
Econ 424 Time Series Concepts Eric Zivot January 20 2015 Time Series Processes Stochastic (Random) Process { 1 2 +1 } = { } = sequence of random variables indexed by time Observed time series of length
More informationEconometrics of Panel Data
Econometrics of Panel Data Jakub Mućk Meeting # 6 Jakub Mućk Econometrics of Panel Data Meeting # 6 1 / 36 Outline 1 The First-Difference (FD) estimator 2 Dynamic panel data models 3 The Anderson and Hsiao
More informationEconometría 2: Análisis de series de Tiempo
Econometría 2: Análisis de series de Tiempo Karoll GOMEZ kgomezp@unal.edu.co http://karollgomez.wordpress.com Segundo semestre 2016 IX. Vector Time Series Models VARMA Models A. 1. Motivation: The vector
More informationDynamic Regression Models
Università di Pavia 2007 Dynamic Regression Models Eduardo Rossi University of Pavia Data Generating Process & Models Setup y t denote an (n 1) vector of economic variables generated at time t. The collection
More informationLECTURE 11. Introduction to Econometrics. Autocorrelation
LECTURE 11 Introduction to Econometrics Autocorrelation November 29, 2016 1 / 24 ON PREVIOUS LECTURES We discussed the specification of a regression equation Specification consists of choosing: 1. correct
More informationNon-Stationary Time Series and Unit Root Testing
Econometrics II Non-Stationary Time Series and Unit Root Testing Morten Nyboe Tabor Course Outline: Non-Stationary Time Series and Unit Root Testing 1 Stationarity and Deviation from Stationarity Trend-Stationarity
More informationAUTOCORRELATION. Phung Thanh Binh
AUTOCORRELATION Phung Thanh Binh OUTLINE Time series Gauss-Markov conditions The nature of autocorrelation Causes of autocorrelation Consequences of autocorrelation Detecting autocorrelation Remedial measures
More informationTesting and Model Selection
Testing and Model Selection This is another digression on general statistics: see PE App C.8.4. The EViews output for least squares, probit and logit includes some statistics relevant to testing hypotheses
More informationDepartment of Economics, UCSB UC Santa Barbara
Department of Economics, UCSB UC Santa Barbara Title: Past trend versus future expectation: test of exchange rate volatility Author: Sengupta, Jati K., University of California, Santa Barbara Sfeir, Raymond,
More informationMA Advanced Econometrics: Applying Least Squares to Time Series
MA Advanced Econometrics: Applying Least Squares to Time Series Karl Whelan School of Economics, UCD February 15, 2011 Karl Whelan (UCD) Time Series February 15, 2011 1 / 24 Part I Time Series: Standard
More information13. Time Series Analysis: Asymptotics Weakly Dependent and Random Walk Process. Strict Exogeneity
Outline: Further Issues in Using OLS with Time Series Data 13. Time Series Analysis: Asymptotics Weakly Dependent and Random Walk Process I. Stationary and Weakly Dependent Time Series III. Highly Persistent
More informationHomoskedasticity. Var (u X) = σ 2. (23)
Homoskedasticity How big is the difference between the OLS estimator and the true parameter? To answer this question, we make an additional assumption called homoskedasticity: Var (u X) = σ 2. (23) This
More informationEmpirical Market Microstructure Analysis (EMMA)
Empirical Market Microstructure Analysis (EMMA) Lecture 3: Statistical Building Blocks and Econometric Basics Prof. Dr. Michael Stein michael.stein@vwl.uni-freiburg.de Albert-Ludwigs-University of Freiburg
More informationPanel Data. March 2, () Applied Economoetrics: Topic 6 March 2, / 43
Panel Data March 2, 212 () Applied Economoetrics: Topic March 2, 212 1 / 43 Overview Many economic applications involve panel data. Panel data has both cross-sectional and time series aspects. Regression
More informationEconometrics Honor s Exam Review Session. Spring 2012 Eunice Han
Econometrics Honor s Exam Review Session Spring 2012 Eunice Han Topics 1. OLS The Assumptions Omitted Variable Bias Conditional Mean Independence Hypothesis Testing and Confidence Intervals Homoskedasticity
More informationLecture 4a: ARMA Model
Lecture 4a: ARMA Model 1 2 Big Picture Most often our goal is to find a statistical model to describe real time series (estimation), and then predict the future (forecasting) One particularly popular model
More informationEmpirical Economic Research, Part II
Based on the text book by Ramanathan: Introductory Econometrics Robert M. Kunst robert.kunst@univie.ac.at University of Vienna and Institute for Advanced Studies Vienna December 7, 2011 Outline Introduction
More informationAutoregressive Moving Average (ARMA) Models and their Practical Applications
Autoregressive Moving Average (ARMA) Models and their Practical Applications Massimo Guidolin February 2018 1 Essential Concepts in Time Series Analysis 1.1 Time Series and Their Properties Time series:
More informationEcon 510 B. Brown Spring 2014 Final Exam Answers
Econ 510 B. Brown Spring 2014 Final Exam Answers Answer five of the following questions. You must answer question 7. The question are weighted equally. You have 2.5 hours. You may use a calculator. Brevity
More informationMULTIPLE REGRESSION AND ISSUES IN REGRESSION ANALYSIS
MULTIPLE REGRESSION AND ISSUES IN REGRESSION ANALYSIS Page 1 MSR = Mean Regression Sum of Squares MSE = Mean Squared Error RSS = Regression Sum of Squares SSE = Sum of Squared Errors/Residuals α = Level
More informationSpatial Econometrics
Spatial Econometrics Lecture 5: Single-source model of spatial regression. Combining GIS and regional analysis (5) Spatial Econometrics 1 / 47 Outline 1 Linear model vs SAR/SLM (Spatial Lag) Linear model
More informationFreeing up the Classical Assumptions. () Introductory Econometrics: Topic 5 1 / 94
Freeing up the Classical Assumptions () Introductory Econometrics: Topic 5 1 / 94 The Multiple Regression Model: Freeing Up the Classical Assumptions Some or all of classical assumptions needed for derivations
More informationThe regression model with one stochastic regressor (part II)
The regression model with one stochastic regressor (part II) 3150/4150 Lecture 7 Ragnar Nymoen 6 Feb 2012 We will finish Lecture topic 4: The regression model with stochastic regressor We will first look
More informationG. S. Maddala Kajal Lahiri. WILEY A John Wiley and Sons, Ltd., Publication
G. S. Maddala Kajal Lahiri WILEY A John Wiley and Sons, Ltd., Publication TEMT Foreword Preface to the Fourth Edition xvii xix Part I Introduction and the Linear Regression Model 1 CHAPTER 1 What is Econometrics?
More informationQuestions and Answers on Unit Roots, Cointegration, VARs and VECMs
Questions and Answers on Unit Roots, Cointegration, VARs and VECMs L. Magee Winter, 2012 1. Let ɛ t, t = 1,..., T be a series of independent draws from a N[0,1] distribution. Let w t, t = 1,..., T, be
More informationNon-Stationary Time Series and Unit Root Testing
Econometrics II Non-Stationary Time Series and Unit Root Testing Morten Nyboe Tabor Course Outline: Non-Stationary Time Series and Unit Root Testing 1 Stationarity and Deviation from Stationarity Trend-Stationarity
More informationF9 F10: Autocorrelation
F9 F10: Autocorrelation Feng Li Department of Statistics, Stockholm University Introduction In the classic regression model we assume cov(u i, u j x i, x k ) = E(u i, u j ) = 0 What if we break the assumption?
More informationFinancial Econometrics
Financial Econometrics Estimation and Inference Gerald P. Dwyer Trinity College, Dublin January 2013 Who am I? Visiting Professor and BB&T Scholar at Clemson University Federal Reserve Bank of Atlanta
More informationApplied Econometrics (QEM)
Applied Econometrics (QEM) based on Prinicples of Econometrics Jakub Mućk Department of Quantitative Economics Jakub Mućk Applied Econometrics (QEM) Meeting #3 1 / 42 Outline 1 2 3 t-test P-value Linear
More informationE 4160 Autumn term Lecture 9: Deterministic trends vs integrated series; Spurious regression; Dickey-Fuller distribution and test
E 4160 Autumn term 2016. Lecture 9: Deterministic trends vs integrated series; Spurious regression; Dickey-Fuller distribution and test Ragnar Nymoen Department of Economics, University of Oslo 24 October
More informationRegression with time series
Regression with time series Class Notes Manuel Arellano February 22, 2018 1 Classical regression model with time series Model and assumptions The basic assumption is E y t x 1,, x T = E y t x t = x tβ
More informationOutline. Nature of the Problem. Nature of the Problem. Basic Econometrics in Transportation. Autocorrelation
1/30 Outline Basic Econometrics in Transportation Autocorrelation Amir Samimi What is the nature of autocorrelation? What are the theoretical and practical consequences of autocorrelation? Since the assumption
More informationTHE LONG-RUN DETERMINANTS OF MONEY DEMAND IN SLOVAKIA MARTIN LUKÁČIK - ADRIANA LUKÁČIKOVÁ - KAROL SZOMOLÁNYI
92 Multiple Criteria Decision Making XIII THE LONG-RUN DETERMINANTS OF MONEY DEMAND IN SLOVAKIA MARTIN LUKÁČIK - ADRIANA LUKÁČIKOVÁ - KAROL SZOMOLÁNYI Abstract: The paper verifies the long-run determinants
More information10. Time series regression and forecasting
10. Time series regression and forecasting Key feature of this section: Analysis of data on a single entity observed at multiple points in time (time series data) Typical research questions: What is the
More informationEcon 623 Econometrics II Topic 2: Stationary Time Series
1 Introduction Econ 623 Econometrics II Topic 2: Stationary Time Series In the regression model we can model the error term as an autoregression AR(1) process. That is, we can use the past value of the
More informationProblem Set 2: Box-Jenkins methodology
Problem Set : Box-Jenkins methodology 1) For an AR1) process we have: γ0) = σ ε 1 φ σ ε γ0) = 1 φ Hence, For a MA1) process, p lim R = φ γ0) = 1 + θ )σ ε σ ε 1 = γ0) 1 + θ Therefore, p lim R = 1 1 1 +
More informationMultiple Regression Analysis
1 OUTLINE Basic Concept: Multiple Regression MULTICOLLINEARITY AUTOCORRELATION HETEROSCEDASTICITY REASEARCH IN FINANCE 2 BASIC CONCEPTS: Multiple Regression Y i = β 1 + β 2 X 1i + β 3 X 2i + β 4 X 3i +
More informationEconometrics II Heij et al. Chapter 7.1
Chapter 7.1 p. 1/2 Econometrics II Heij et al. Chapter 7.1 Linear Time Series Models for Stationary data Marius Ooms Tinbergen Institute Amsterdam Chapter 7.1 p. 2/2 Program Introduction Modelling philosophy
More informationA Non-Parametric Approach of Heteroskedasticity Robust Estimation of Vector-Autoregressive (VAR) Models
Journal of Finance and Investment Analysis, vol.1, no.1, 2012, 55-67 ISSN: 2241-0988 (print version), 2241-0996 (online) International Scientific Press, 2012 A Non-Parametric Approach of Heteroskedasticity
More informationEconometrics I KS. Module 2: Multivariate Linear Regression. Alexander Ahammer. This version: April 16, 2018
Econometrics I KS Module 2: Multivariate Linear Regression Alexander Ahammer Department of Economics Johannes Kepler University of Linz This version: April 16, 2018 Alexander Ahammer (JKU) Module 2: Multivariate
More informationFactor models. March 13, 2017
Factor models March 13, 2017 Factor Models Macro economists have a peculiar data situation: Many data series, but usually short samples How can we utilize all this information without running into degrees
More informationProblem set 1 - Solutions
EMPIRICAL FINANCE AND FINANCIAL ECONOMETRICS - MODULE (8448) Problem set 1 - Solutions Exercise 1 -Solutions 1. The correct answer is (a). In fact, the process generating daily prices is usually assumed
More informationNext, we discuss econometric methods that can be used to estimate panel data models.
1 Motivation Next, we discuss econometric methods that can be used to estimate panel data models. Panel data is a repeated observation of the same cross section Panel data is highly desirable when it is
More information11. Further Issues in Using OLS with TS Data
11. Further Issues in Using OLS with TS Data With TS, including lags of the dependent variable often allow us to fit much better the variation in y Exact distribution theory is rarely available in TS applications,
More informationEconometrics with Observational Data. Introduction and Identification Todd Wagner February 1, 2017
Econometrics with Observational Data Introduction and Identification Todd Wagner February 1, 2017 Goals for Course To enable researchers to conduct careful quantitative analyses with existing VA (and non-va)
More informationWORKSHOP. Introductory Econometrics with EViews. Asst. Prof. Dr. Kemal Bağzıbağlı Department of Economic
WORKSHOP on Introductory Econometrics with EViews Asst. Prof. Dr. Kemal Bağzıbağlı Department of Economic Res. Asst. Pejman Bahramian PhD Candidate, Department of Economic Res. Asst. Gizem Uzuner MSc Student,
More informationSpatial Econometrics. Wykªad 6: Multi-source spatial models. Andrzej Torój. Institute of Econometrics Department of Applied Econometrics
Spatial Econometrics Wykªad 6: Multi-source spatial models (6) Spatial Econometrics 1 / 21 Outline 1 Multi-source models 2 SARAR model 3 SDM (Durbin) model 4 SDEM model 5 Exercises (6) Spatial Econometrics
More informationChapter 3: Regression Methods for Trends
Chapter 3: Regression Methods for Trends Time series exhibiting trends over time have a mean function that is some simple function (not necessarily constant) of time. The example random walk graph from
More information2. Linear regression with multiple regressors
2. Linear regression with multiple regressors Aim of this section: Introduction of the multiple regression model OLS estimation in multiple regression Measures-of-fit in multiple regression Assumptions
More information11.1 Gujarati(2003): Chapter 12
11.1 Gujarati(2003): Chapter 12 Time Series Data 11.2 Time series process of economic variables e.g., GDP, M1, interest rate, echange rate, imports, eports, inflation rate, etc. Realization An observed
More informationECON 4160: Econometrics-Modelling and Systems Estimation Lecture 7: Single equation models
ECON 4160: Econometrics-Modelling and Systems Estimation Lecture 7: Single equation models Ragnar Nymoen Department of Economics University of Oslo 25 September 2018 The reference to this lecture is: Chapter
More information