TÁMOP /2/A/KMR
|
|
|
- Abner Haynes
- 5 years ago
- Views:
Transcription
1 ECONOMIC STATISTICS
2 ECONOMIC STATISTICS Sonsored by a Gran TÁMOP /2/A/KMR Course Maerial Develoed by Dearmen of Economics, Faculy of Social Sciences, Eövös Loránd Universiy Budaes (ELTE) Dearmen of Economics, Eövös Loránd Universiy Budaes Insiue of Economics, Hungarian Academy of Sciences Balassi Kiadó, Budaes
3
4 ELTE Faculy of Social Sciences, Dearmen of Economics ECONOMIC STATISTICS Auhor: Anikó Bíró Suervised by Anikó Bíró June 200
5 ECONOMIC STATISTICS Week AR models Anikó Bíró
6 AR() model U o now: AR() model Sloe saionariy AR() model: auoregression of order ρ=0 uni roo -2<ρ<0 - saionary e e
7 AR() model modified form e e
8 Uni roo has a uni roo canno be included in he regression! Exemion: coinegraion Differenced value (Δ) has o be used! Δ sacionary difference saionary : has sochasic rend
9 Deerminisic rend Examle: e, saionary rend saionary Grah: similar o sochasic rend no enough o make a decision on uni roo
10 Examle AR(4) model AR(4) model wih deerminisic rend: Generae differenced variables Differenced variables: 3 lags Coefficien of - = 0? e
11 Seasonaliy Paern recurring a regular inervals Examle: consumion, agriculural roducion, exor Treamen: variables indicaing seasonaliy Quarerly: 3 dummies! Monhly: dummies! Or: seasonal adjusmen KSH: seasonally adjused ime series
12 Secificaion choice... e Maximal lag lengh ( max ) Esimae AR( max ) model wih or wihou deerminisic rend (according o he deenden variable, based on assumion!) Tes Γ max- =0 (-es) if saisfied: decrease lag lengh by one
13 Uni roo es Tesing ρ=0: usual -es canno be used! Dickey Fuller-es: use -saisic, bu criical values are correced Problem: weak es can find uni roo even if i is no resen Examle: rend saionary ime series, srucural break
14 Dickey Fuller-es Quesion: include rend? Null hyohesis: uni roo Large -value: has uni roo, no saionary
15 Uni roo es examle Monly exor daa Seasonally adjused Trend Null Hyohesis: EXPORT_SA has a uni roo Exogenous: Consan, Linear Trend Lag Lengh: 3 (Auomaic based on SIC, MAXLAG=3) -Saisic Prob. Augmened Dickey Fuller-es sa. -2,86 0,530 Tes criical values: % level -4,080 5% level -3,4389 0% level -3,438
16 Summary AR() model, modified form Uni roo in AR() models Trend saionariy Seasonaliy Dickey Fuller-es
17 AR models Seminar
18 AR() model AR() model: auoregression of order ρ=0 uni roo -2<ρ<0 - saionary e e
19 Uni roo has a uni roo canno be included in he regression! Exemion: coinegraion Differenced value (Δ) has o be used! Δ saionary difference saionary : has sochasic rend
20 Examle monhly exor MNB daa (m EUR) Esimaion of AR(4) model wih deerminisic rend: Generae differenced variables Differenced variables: 3 lags Coefficien of - = 0? e
21 Seasonaliy Paern recurring a regular inervals Treamen: variables indicaing seasonaliy Quarerly: 3 dummies Monhly: dummies
22 Seasonaliy examle Monhly exor daa 2 seasonal dummies: mulicollineariy EViews error message EViews: Procs/Seasonal adjusmen
23 Secificaion choice... e Maximal lag lengh ( max ) Esimae AR( max ) mode wih or wihou deerminisic rend Tes Γ max- =0 (-es) if saisfied: decrease lag lengh by one Tes he significance of rend afer lag lengh selecion Examle: AR() model for firs differenced log exor ime series (use seasonally adjused daa!)
24 Dickey Fuller-es Tes uni roo View/Uni roo es Oion: auomaic lag lengh selecion Quesion: include rend? Null hyohesis: uni roo Large -value: has uni roo, no saionary
25 Uni roo es Monhly exor daa (MNB) Seasonally adjused Trend? Inerre ouu Is he differenced variable saionary? Quarerly ublic deb daa (MNB) Trend?
Licenciatura de ADE y Licenciatura conjunta Derecho y ADE. Hoja de ejercicios 2 PARTE A
Licenciaura de ADE y Licenciaura conjuna Derecho y ADE Hoja de ejercicios PARTE A 1. Consider he following models Δy = 0.8 + ε (1 + 0.8L) Δ 1 y = ε where ε and ε are independen whie noise processes. In
Chickens vs. Eggs: Replicating Thurman and Fisher (1988) by Arianto A. Patunru Department of Economics, University of Indonesia 2004
Chicens vs. Eggs: Relicaing Thurman and Fisher (988) by Ariano A. Paunru Dearmen of Economics, Universiy of Indonesia 2004. Inroducion This exercise lays ou he rocedure for esing Granger Causaliy as discussed
Vectorautoregressive Model and Cointegration Analysis. Time Series Analysis Dr. Sevtap Kestel 1
Vecorauoregressive Model and Coinegraion Analysis Par V Time Series Analysis Dr. Sevap Kesel 1 Vecorauoregression Vecor auoregression (VAR) is an economeric model used o capure he evoluion and he inerdependencies
STAD57 Time Series Analysis. Lecture 17
STAD57 Time Series Analysis Lecure 17 1 Exponenially Weighed Moving Average Model Consider ARIMA(0,1,1), or IMA(1,1), model 1 s order differences follow MA(1) X X W W Y X X W W 1 1 1 1 Very common model
STAD57 Time Series Analysis. Lecture 17
STAD57 Time Series Analysis Lecure 17 1 Exponenially Weighed Moving Average Model Consider ARIMA(0,1,1), or IMA(1,1), model 1 s order differences follow MA(1) X X W W Y X X W W 1 1 1 1 Very common model
Cointegration and Implications for Forecasting
Coinegraion and Implicaions for Forecasing Two examples (A) Y Y 1 1 1 2 (B) Y 0.3 0.9 1 1 2 Example B: Coinegraion Y and coinegraed wih coinegraing vecor [1, 0.9] because Y 0.9 0.3 is a saionary process
Lecture 5. Time series: ECM. Bernardina Algieri Department Economics, Statistics and Finance
Lecure 5 Time series: ECM Bernardina Algieri Deparmen Economics, Saisics and Finance Conens Time Series Modelling Coinegraion Error Correcion Model Two Seps, Engle-Granger procedure Error Correcion Model
Box-Jenkins Modelling of Nigerian Stock Prices Data
Greener Journal of Science Engineering and Technological Research ISSN: 76-7835 Vol. (), pp. 03-038, Sepember 0. Research Aricle Box-Jenkins Modelling of Nigerian Sock Prices Daa Ee Harrison Euk*, Barholomew
- The whole joint distribution is independent of the date at which it is measured and depends only on the lag.
Saionary Processes Sricly saionary - The whole join disribuion is indeenden of he dae a which i is measured and deends only on he lag. - E y ) is a finie consan. ( - V y ) is a finie consan. ( ( y, y s
Methodology. -ratios are biased and that the appropriate critical values have to be increased by an amount. that depends on the sample size.
Mehodology. Uni Roo Tess A ime series is inegraed when i has a mean revering propery and a finie variance. I is only emporarily ou of equilibrium and is called saionary in I(0). However a ime series ha
How to Deal with Structural Breaks in Practical Cointegration Analysis
How o Deal wih Srucural Breaks in Pracical Coinegraion Analysis Roselyne Joyeux * School of Economic and Financial Sudies Macquarie Universiy December 00 ABSTRACT In his noe we consider he reamen of srucural
Nonstationarity-Integrated Models. Time Series Analysis Dr. Sevtap Kestel 1
Nonsaionariy-Inegraed Models Time Series Analysis Dr. Sevap Kesel 1 Diagnosic Checking Residual Analysis: Whie noise. P-P or Q-Q plos of he residuals follow a normal disribuion, he series is called a Gaussian
Stability. Coefficients may change over time. Evolution of the economy Policy changes
Sabiliy Coefficiens may change over ime Evoluion of he economy Policy changes Time Varying Parameers y = α + x β + Coefficiens depend on he ime period If he coefficiens vary randomly and are unpredicable,
Chapter 16. Regression with Time Series Data
Chaper 16 Regression wih Time Series Daa The analysis of ime series daa is of vial ineres o many groups, such as macroeconomiss sudying he behavior of naional and inernaional economies, finance economiss
Department of Economics East Carolina University Greenville, NC Phone: Fax:
March 3, 999 Time Series Evidence on Wheher Adjusmen o Long-Run Equilibrium is Asymmeric Philip Rohman Eas Carolina Universiy Absrac The Enders and Granger (998) uni-roo es agains saionary alernaives wih
Wisconsin Unemployment Rate Forecast Revisited
Wisconsin Unemploymen Rae Forecas Revisied Forecas in Lecure Wisconsin unemploymen November 06 was 4.% Forecass Poin Forecas 50% Inerval 80% Inerval Forecas Forecas December 06 4.0% (4.0%, 4.0%) (3.95%,
Arima Fit to Nigerian Unemployment Data
2012, TexRoad Publicaion ISSN 2090-4304 Journal of Basic and Applied Scienific Research www.exroad.com Arima Fi o Nigerian Unemploymen Daa Ee Harrison ETUK 1, Barholomew UCHENDU 2, Uyodhu VICTOR-EDEMA
Distribution of Least Squares
Disribuion of Leas Squares In classic regression, if he errors are iid normal, and independen of he regressors, hen he leas squares esimaes have an exac normal disribuion, no jus asympoic his is no rue
STAD57 Time Series Analysis. Lecture 5
STAD57 Time Series Analysis Lecure 5 1 Exploraory Daa Analysis Check if given TS is saionary: µ is consan σ 2 is consan γ(s,) is funcion of h= s If no, ry o make i saionary using some of he mehods below:
Introduction D P. r = constant discount rate, g = Gordon Model (1962): constant dividend growth rate.
Inroducion Gordon Model (1962): D P = r g r = consan discoun rae, g = consan dividend growh rae. If raional expecaions of fuure discoun raes and dividend growh vary over ime, so should he D/P raio. Since
Time series Decomposition method
Time series Decomposiion mehod A ime series is described using a mulifacor model such as = f (rend, cyclical, seasonal, error) = f (T, C, S, e) Long- Iner-mediaed Seasonal Irregular erm erm effec, effec,
Quarterly ice cream sales are high each summer, and the series tends to repeat itself each year, so that the seasonal period is 4.
Seasonal models Many business and economic ime series conain a seasonal componen ha repeas iself afer a regular period of ime. The smalles ime period for his repeiion is called he seasonal period, and
International Parity Relations between Poland and Germany: A Cointegrated VAR Approach
Research Seminar a he Deparmen of Economics, Warsaw Universiy Warsaw, 15 January 2008 Inernaional Pariy Relaions beween Poland and Germany: A Coinegraed VAR Approach Agnieszka Sążka Naional Bank of Poland
Nonstationary Time Series Data and Cointegration
ECON 4551 Economerics II Memorial Universiy of Newfoundland Nonsaionary Time Series Daa and Coinegraion Adaped from Vera Tabakova s noes 12.1 Saionary and Nonsaionary Variables 12.2 Spurious Regressions
Application of Statistical Methods of Time-Series for Estimating and Forecasting the Wheat Series in Yemen (Production and Import)
American Journal of Alied Mahemaics 16; 4(3): 14-131 h://www.scienceublishinggrou.com/j/ajam doi: 1.11648/j.ajam.1643.1 ISSN: 33-43 (Prin); ISSN: 33-6X (Online) Mehodology Aricle Alicaion of Saisical Mehods
Ready for euro? Empirical study of the actual monetary policy independence in Poland VECM modelling
Macroeconomerics Handou 2 Ready for euro? Empirical sudy of he acual moneary policy independence in Poland VECM modelling 1. Inroducion This classes are based on: Łukasz Goczek & Dagmara Mycielska, 2013.
A multivariate labour market model in the Czech Republic 1. Jana Hanclová Faculty of Economics, VŠB-Technical University Ostrava
A mulivariae labour marke model in he Czech Republic Jana Hanclová Faculy of Economics, VŠB-Technical Universiy Osrava Absrac: The paper deals wih an exisence of an equilibrium unemploymen-vacancy rae
ESTIMATION OF DYNAMIC PANEL DATA MODELS WHEN REGRESSION COEFFICIENTS AND INDIVIDUAL EFFECTS ARE TIME-VARYING
Inernaional Journal of Social Science and Economic Research Volume:02 Issue:0 ESTIMATION OF DYNAMIC PANEL DATA MODELS WHEN REGRESSION COEFFICIENTS AND INDIVIDUAL EFFECTS ARE TIME-VARYING Chung-ki Min Professor
The Properties of Procedures Dealing with Uncertainty about Intercept and Deterministic Trend in Unit Root Testing
CESIS Elecronic Working Paper Series Paper No. 214 The Properies of Procedures Dealing wih Uncerainy abou Inercep and Deerminisic Trend in Uni Roo Tesing R. Sco Hacker* and Abdulnasser Haemi-J** *Jönköping
Section 4 NABE ASTEF 232
Secion 4 NABE ASTEF 3 APPLIED ECONOMETRICS: TIME-SERIES ANALYSIS 33 Inroducion and Review The Naure of Economic Modeling Judgemen calls unavoidable Economerics an ar Componens of Applied Economerics Specificaion
Modeling House Price Volatility States in the UK by Switching ARCH Models
Modeling House Price Volailiy Saes in he UK by Swiching ARCH Models I-Chun Tsai* Dearmen of Finance Souhern Taiwan Universiy of Technology, Taiwan Ming-Chi Chen Dearmen of Finance Naional Sun Ya-sen Universiy,
Choice of Spectral Density Estimator in Ng-Perron Test: A Comparative Analysis
Inernaional Economeric Review (IER) Choice of Specral Densiy Esimaor in Ng-Perron Tes: A Comparaive Analysis Muhammad Irfan Malik and Aiq-ur-Rehman Inernaional Islamic Universiy Islamabad and Inernaional
Why is Chinese Provincial Output Diverging? Joakim Westerlund, University of Gothenburg David Edgerton, Lund University Sonja Opper, Lund University
Why is Chinese Provincial Oupu Diverging? Joakim Weserlund, Universiy of Gohenburg David Edgeron, Lund Universiy Sonja Opper, Lund Universiy Purpose of his paper. We re-examine he resul of Pedroni and
Stationary Time Series
3-Jul-3 Time Series Analysis Assoc. Prof. Dr. Sevap Kesel July 03 Saionary Time Series Sricly saionary process: If he oin dis. of is he same as he oin dis. of ( X,... X n) ( X h,... X nh) Weakly Saionary
A Specification Test for Linear Dynamic Stochastic General Equilibrium Models
Journal of Saisical and Economeric Mehods, vol.1, no.2, 2012, 65-70 ISSN: 2241-0384 (prin), 2241-0376 (online) Scienpress Ld, 2012 A Specificaion Tes for Linear Dynamic Sochasic General Equilibrium Models
Regression with Time Series Data
Regression wih Time Series Daa y = β 0 + β 1 x 1 +...+ β k x k + u Serial Correlaion and Heeroskedasiciy Time Series - Serial Correlaion and Heeroskedasiciy 1 Serially Correlaed Errors: Consequences Wih
Frequency independent automatic input variable selection for neural networks for forecasting
Universiä Hamburg Insiu für Wirschafsinformaik Prof. Dr. D.B. Preßmar Frequency independen auomaic inpu variable selecion for neural neworks for forecasing Nikolaos Kourenzes Sven F. Crone LUMS Deparmen
Econ Autocorrelation. Sanjaya DeSilva
Econ 39 - Auocorrelaion Sanjaya DeSilva Ocober 3, 008 1 Definiion Auocorrelaion (or serial correlaion) occurs when he error erm of one observaion is correlaed wih he error erm of any oher observaion. This
ARE SHOCKS IN THE TOURISM OF V4 COUNTRIES PERMANENT?
ARE SHOCKS IN THE TOURISM OF V4 COUNTRIES PERMANENT? Šefan Lyócsa Eva Liavcová Pera Vašaničová Absrac We sudy he persisence properies of seasonally adjused number of nighs spen (NNS) in four Cenral and
(10) (a) Derive and plot the spectrum of y. Discuss how the seasonality in the process is evident in spectrum.
January 01 Final Exam Quesions: Mark W. Wason (Poins/Minues are given in Parenheses) (15) 1. Suppose ha y follows he saionary AR(1) process y = y 1 +, where = 0.5 and ~ iid(0,1). Le x = (y + y 1 )/. (11)
A One Line Derivation of DCC: Application of a Vector Random Coefficient Moving Average Process*
A One Line Derivaion of DCC: Alicaion of a Vecor Random Coefficien Moving Average Process* Chrisian M. Hafner Insiu de saisique, biosaisique e sciences acuarielles Universié caholique de Louvain Michael
Mean Reversion of Balance of Payments GEvidence from Sequential Trend Break Unit Root Tests. Abstract
Mean Reversion of Balance of Paymens GEvidence from Sequenial Trend Brea Uni Roo Tess Mei-Yin Lin Deparmen of Economics, Shih Hsin Universiy Jue-Shyan Wang Deparmen of Public Finance, Naional Chengchi
A Hybrid Model for Improving. Malaysian Gold Forecast Accuracy
In. Journal of Mah. Analysis, Vol. 8, 2014, no. 28, 1377-1387 HIKARI Ld, www.m-hikari.com hp://dx.doi.org/10.12988/ijma.2014.45139 A Hybrid Model for Improving Malaysian Gold Forecas Accuracy Maizah Hura
A Point Optimal Test for the Null of Near Integration. A. Aznar and M. I. Ayuda 1. University of Zaragoza
A Poin Opimal es for he Null of Near Inegraion A. Aznar and M. I. Ayuda Universiy of Zaragoza he objecive of his paper is o derive a poin opimal es for he null hypohesis of near inegraion (PONI-es). We
Properties of Autocorrelated Processes Economics 30331
Properies of Auocorrelaed Processes Economics 3033 Bill Evans Fall 05 Suppose we have ime series daa series labeled as where =,,3, T (he final period) Some examples are he dail closing price of he S&500,
LONG MEMORY AT THE LONG-RUN AND THE SEASONAL MONTHLY FREQUENCIES IN THE US MONEY STOCK. Guglielmo Maria Caporale. Brunel University, London
LONG MEMORY AT THE LONG-RUN AND THE SEASONAL MONTHLY FREQUENCIES IN THE US MONEY STOCK Guglielmo Maria Caporale Brunel Universiy, London Luis A. Gil-Alana Universiy of Navarra Absrac In his paper we show
Components Model. Remember that we said that it was useful to think about the components representation
Componens Model Remember ha we said ha i was useful o hink abou he componens represenaion = T S C Suppose ha C is an AR(p) process Wha model does his impl for? TrendCcle Model For simplici, we sar wih
Modeling and Forecasting Volatility Autoregressive Conditional Heteroskedasticity Models. Economic Forecasting Anthony Tay Slide 1
Modeling and Forecasing Volailiy Auoregressive Condiional Heeroskedasiciy Models Anhony Tay Slide 1 smpl @all line(m) sii dl_sii S TII D L _ S TII 4,000. 3,000.1.0,000 -.1 1,000 -. 0 86 88 90 9 94 96 98
Unit Root Time Series. Univariate random walk
Uni Roo ime Series Univariae random walk Consider he regression y y where ~ iid N 0, he leas squares esimae of is: ˆ yy y y yy Now wha if = If y y hen le y 0 =0 so ha y j j If ~ iid N 0, hen y ~ N 0, he
The Validity of the Tourism-Led Growth Hypothesis for Thailand
MPRA Munich Personal RePEc Archive The Validiy of he Tourism-Led Growh Hypohesis for Thailand Komain Jiranyakul Naional Insiue of Developmen Adminisraion Augus 206 Online a hps://mpra.ub.uni-muenchen.de/72806/
ECON 482 / WH Hong Time Series Data Analysis 1. The Nature of Time Series Data. Example of time series data (inflation and unemployment rates)
ECON 48 / WH Hong Time Series Daa Analysis. The Naure of Time Series Daa Example of ime series daa (inflaion and unemploymen raes) ECON 48 / WH Hong Time Series Daa Analysis The naure of ime series daa
Time Series Test of Nonlinear Convergence and Transitional Dynamics. Terence Tai-Leung Chong
Time Series Tes of Nonlinear Convergence and Transiional Dynamics Terence Tai-Leung Chong Deparmen of Economics, The Chinese Universiy of Hong Kong Melvin J. Hinich Signal and Informaion Sciences Laboraory
A complementary test for ADF test with an application to the exchange rates returns
MPRA Munich Personal RePEc Archive A complemenary es for ADF es wih an applicaion o he exchange raes reurns Venus Khim-Sen Liew and Sie-Hoe Lau and Siew-Eng Ling 005 Online a hp://mpra.ub.uni-muenchen.de/518/
BOX-JENKINS MODEL NOTATION. The Box-Jenkins ARMA(p,q) model is denoted by the equation. pwhile the moving average (MA) part of the model is θ1at
BOX-JENKINS MODEL NOAION he Box-Jenkins ARMA(,q) model is denoed b he equaion + + L+ + a θ a L θ a 0 q q. () he auoregressive (AR) ar of he model is + L+ while he moving average (MA) ar of he model is
Description of the MS-Regress R package (Rmetrics)
Descriion of he MS-Regress R ackage (Rmerics) Auhor: Marcelo Perlin PhD Suden / ICMA Reading Universiy Email: marceloerlin@gmail.com / m.erlin@icmacenre.ac.uk The urose of his documen is o show he general
OBJECTIVES OF TIME SERIES ANALYSIS
OBJECTIVES OF TIME SERIES ANALYSIS Undersanding he dynamic or imedependen srucure of he observaions of a single series (univariae analysis) Forecasing of fuure observaions Asceraining he leading, lagging
Estimation Uncertainty
Esimaion Uncerainy The sample mean is an esimae of β = E(y +h ) The esimaion error is = + = T h y T b ( ) = = + = + = = = T T h T h e T y T y T b β β β Esimaion Variance Under classical condiions, where
Lecture 15. Dummy variables, continued
Lecure 15. Dummy variables, coninued Seasonal effecs in ime series Consider relaion beween elecriciy consumpion Y and elecriciy price X. The daa are quarerly ime series. Firs model ln α 1 + α2 Y = ln X
Modeling Economic Time Series with Stochastic Linear Difference Equations
A. Thiemer, SLDG.mcd, 6..6 FH-Kiel Universiy of Applied Sciences Prof. Dr. Andreas Thiemer e-mail: andreas.hiemer@fh-kiel.de Modeling Economic Time Series wih Sochasic Linear Difference Equaions Summary:
Exercise: Building an Error Correction Model of Private Consumption. Part II Testing for Cointegration 1
Bo Sjo 200--24 Exercise: Building an Error Correcion Model of Privae Consumpion. Par II Tesing for Coinegraion Learning objecives: This lab inroduces esing for he order of inegraion and coinegraion. The
Derived Short-Run and Long-Run Softwood Lumber Demand and Supply
Derived Shor-Run and Long-Run Sofwood Lumber Demand and Supply Nianfu Song and Sun Joseph Chang School of Renewable Naural Resources Louisiana Sae Universiy Ouline Shor-run run and long-run implied by
Financial Econometrics Jeffrey R. Russell Midterm Winter 2009 SOLUTIONS
Name SOLUTIONS Financial Economerics Jeffrey R. Russell Miderm Winer 009 SOLUTIONS You have 80 minues o complee he exam. Use can use a calculaor and noes. Try o fi all your work in he space provided. If
An Overview of Methods for Testing Short- and Long-Run Equilibrium with Time Series Data: Cointegration and Error Correction Mechanism
ISSN 2039-9340 (prin) Medierranean Journal of Social Sciences Published by MCSER-CEMAS-Sapienza Universiy of Rome An Overview of Mehods for Tesing Shor- and Long-Run Equilibrium wih Time Series Daa: Coinegraion
Forecast of Adult Literacy in Sudan
Journal for Sudies in Managemen and Planning Available a hp://inernaionaljournalofresearch.org/index.php/jsmap e-issn: 2395-463 Volume 1 Issue 2 March 215 Forecas of Adul Lieracy in Sudan Dr. Elfarazdag
LONG YEARS APICULTURE DATA MODEL OF TURKEY: AN ECONOMETRIC TIME SERIES ANALYSIS ABSTRACT
Dogan Shor e Communicaion al., J. Anim. Plan Sci. 4(4):014 The Journal of Animal & Plan Sciences, 4(5): 014, Page: 1573-1578 ISSN: 1018-7081 LONG YEARS APICULTURE DATA MODEL OF TURKEY: AN ECONOMETRIC TIME
A New Unit Root Test against Asymmetric ESTAR Nonlinearity with Smooth Breaks
Iran. Econ. Rev. Vol., No., 08. pp. 5-6 A New Uni Roo es agains Asymmeric ESAR Nonlineariy wih Smooh Breaks Omid Ranjbar*, sangyao Chang, Zahra (Mila) Elmi 3, Chien-Chiang Lee 4 Received: December 7, 06
Applied Econometrics and International Development Vol.9-1 (2009)
Applied Economerics and Inernaional Developmen Vol.9- (2009) THE BILATERAL RELATIONSHIP BETWEEN CONSUMPTION AND IN MEXICO AND THE US: A COMMENT GOMEZ-ZALDIVAR, Manuel * VENTOSA-SANTAULARIA, Daniel Absrac
Økonomisk Kandidateksamen 2005(II) Econometrics 2. Solution
Økonomisk Kandidaeksamen 2005(II) Economerics 2 Soluion his is he proposed soluion for he exam in Economerics 2. For compleeness he soluion gives formal answers o mos of he quesions alhough his is no always
INVESTIGATING THE WEAK FORM EFFICIENCY OF AN EMERGING MARKET USING PARAMETRIC TESTS: EVIDENCE FROM KARACHI STOCK MARKET OF PAKISTAN
Elecronic Journal of Applied Saisical Analysis EJASA, Elecron. J. App. Sa. Anal. Vol. 3, Issue 1 (21), 52 64 ISSN 27-5948, DOI 1.1285/i275948v3n1p52 28 Universià del Saleno SIBA hp://siba-ese.unile.i/index.php/ejasa/index
Types of Exponential Smoothing Methods. Simple Exponential Smoothing. Simple Exponential Smoothing
M Business Forecasing Mehods Exponenial moohing Mehods ecurer : Dr Iris Yeung Room No : P79 Tel No : 788 8 Types of Exponenial moohing Mehods imple Exponenial moohing Double Exponenial moohing Brown s
SHIFTS IN PERSISTENCE IN TURKISH REAL EXCHANGE RATES HALUK ERLAT
Vol., Sepember 2009 SHIFTS IN PERSISTENCE IN TURKISH REAL EXCHANGE RATES HALUK ERLAT Deparmen of Economics Middle Eas Technical Universiy 0653 Ankara, Turkey Fax: 90-32-207964 Email: herla@meu.edu.r Prepared
FORECASTS GENERATING FOR ARCH-GARCH PROCESSES USING THE MATLAB PROCEDURES
FORECASS GENERAING FOR ARCH-GARCH PROCESSES USING HE MALAB PROCEDURES Dušan Marček, Insiue of Comuer Science, Faculy of Philosohy and Science, he Silesian Universiy Oava he Faculy of Managemen Science
A unit root test based on smooth transitions and nonlinear adjustment
MPRA Munich Personal RePEc Archive A uni roo es based on smooh ransiions and nonlinear adjusmen Aycan Hepsag Isanbul Universiy 5 Ocober 2017 Online a hps://mpra.ub.uni-muenchen.de/81788/ MPRA Paper No.
Identification of Trends and Cycles in Economic Time Series. by Bora Isik
Idenificaion of Trends and Cycles in Economic Time Series by Bora Isik An Honours essay submied o Carleon Universiy in fulfillmen of he requiremens for he course ECON 498, as credi oward he degree of Bachelor
Chapter 15. Time Series: Descriptive Analyses, Models, and Forecasting
Chaper 15 Time Series: Descripive Analyses, Models, and Forecasing Descripive Analysis: Index Numbers Index Number a number ha measures he change in a variable over ime relaive o he value of he variable
Dynamic Models, Autocorrelation and Forecasting
ECON 4551 Economerics II Memorial Universiy of Newfoundland Dynamic Models, Auocorrelaion and Forecasing Adaped from Vera Tabakova s noes 9.1 Inroducion 9.2 Lags in he Error Term: Auocorrelaion 9.3 Esimaing
Solutions: Wednesday, November 14
Amhers College Deparmen of Economics Economics 360 Fall 2012 Soluions: Wednesday, November 14 Judicial Daa: Cross secion daa of judicial and economic saisics for he fify saes in 2000. JudExp CrimesAll
Oil price shocks and domestic inflation in Thailand
MPRA Munich Personal RePEc Archive Oil rice shocks and domesic inflaion in Thailand Komain Jiranyakul Naional Insiue of Develomen Adminisraion March 205 Online a h://mra.ub.uni-muenchen.de/62797/ MPRA
Use of Unobserved Components Model for Forecasting Non-stationary Time Series: A Case of Annual National Coconut Production in Sri Lanka
Tropical Agriculural Research Vol. 5 (4): 53 531 (014) Use of Unobserved Componens Model for Forecasing Non-saionary Time Series: A Case of Annual Naional Coconu Producion in Sri Lanka N.K.K. Brinha, S.
Robust critical values for unit root tests for series with conditional heteroscedasticity errors: An application of the simple NoVaS transformation
WORKING PAPER 01: Robus criical values for uni roo ess for series wih condiional heeroscedasiciy errors: An applicaion of he simple NoVaS ransformaion Panagiois Manalos ECONOMETRICS AND STATISTICS ISSN
Kriging Models Predicting Atrazine Concentrations in Surface Water Draining Agricultural Watersheds
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Kriging Models Predicing Arazine Concenraions in Surface Waer Draining Agriculural Waersheds Paul L. Mosquin, Jeremy Aldworh, Wenlin Chen Supplemenal Maerial Number
Cointegration in Theory and Practice. A Tribute to Clive Granger. ASSA Meetings January 5, 2010
Coinegraion in heory and Pracice A ribue o Clive Granger ASSA Meeings January 5, 00 James H. Sock Deparmen of Economics, Harvard Universiy and he NBER /4/009 /4/009 Coinegraion: he Hisorical Seing Granger
Distribution of Estimates
Disribuion of Esimaes From Economerics (40) Linear Regression Model Assume (y,x ) is iid and E(x e )0 Esimaion Consisency y α + βx + he esimaes approach he rue values as he sample size increases Esimaion
Do Steel Consumption and Production Cause Economic Growth?: A Case Study of Six Southeast Asian Countries
JOURNAL OF INTERNATIONAL AND AREA STUDIES Volume 5, Number, 008, pp.-5 Do Seel Consumpion and Producion Cause Economic Growh?: A Case Sudy of Six Souheas Asian Counries Hee-Ryang Ra This sudy aims o deermine
Position predictive measurement method for time grating CNC rotary table
Posiion redicive measuremen mehod for ime graing CC roary able Liu Xiaokang a, Peng Donglin a, Yang Wei a and Fei Yeai b a Engineering Research Cener of Mechanical Tesing Technology and Equimen, Minisry
Diebold, Chapter 7. Francis X. Diebold, Elements of Forecasting, 4th Edition (Mason, Ohio: Cengage Learning, 2006). Chapter 7. Characterizing Cycles
Diebold, Chaper 7 Francis X. Diebold, Elemens of Forecasing, 4h Ediion (Mason, Ohio: Cengage Learning, 006). Chaper 7. Characerizing Cycles Afer compleing his reading you should be able o: Define covariance
Forecasting. Summary. Sample StatFolio: tsforecast.sgp. STATGRAPHICS Centurion Rev. 9/16/2013
STATGRAPHICS Cenurion Rev. 9/16/2013 Forecasing Summary... 1 Daa Inpu... 3 Analysis Opions... 5 Forecasing Models... 9 Analysis Summary... 21 Time Sequence Plo... 23 Forecas Table... 24 Forecas Plo...
Imo Udo Moffat Department of Mathematics/Statistics, University of Uyo, Nigeria
Inernaional Journals of Advanced Research in Compuer Science and Sofware Engineering ISSN: 2277-128 (Volume-7, Issue-8) a Research Aricle Augus 2017 Applicaion of Inerruped Time Series Modelling o Prime
Dynamic Econometric Models: Y t = + 0 X t + 1 X t X t k X t-k + e t. A. Autoregressive Model:
Dynamic Economeric Models: A. Auoregressive Model: Y = + 0 X 1 Y -1 + 2 Y -2 + k Y -k + e (Wih lagged dependen variable(s) on he RHS) B. Disribued-lag Model: Y = + 0 X + 1 X -1 + 2 X -2 + + k X -k + e
UNIT ROOT TESTS, COINTEGRATION, ECM, VECM, AND CAUSALITY MODELS
UNIT ROOT TESTS, COINTEGRATION, ECM, VECM, AND CAUSALITY MODELS Compiled by M&B EFA is desroying he brains of curren generaion s researchers in his counry. Please sop i as much as you can. Thank you. The
PROJECTING ECONOMIC ACTIVITY AT THE STATE
FORECASTING STATE LEVEL ECONOMIC ACTIVITY: AN ERROR CORRECTION MODEL WITH EXOGENOUS NATIONAL STRUCTURAL FORECAST COMPONENTS Michael Hicks, Ball Sae Universiy* INTRODUCTION PROJECTING ECONOMIC ACTIVITY
Answers to Exercises in Chapter 7 - Correlation Functions
M J Robers - //8 Answers o Exercises in Chaper 7 - Correlaion Funcions 7- (from Papoulis and Pillai) The random variable C is uniform in he inerval (,T ) Find R, ()= u( C), ()= C (Use R (, )= R,, < or
Empirical Estimation of Is-Lm Model for the US Economy by Applying Jmulti
Empirical Esimaion of Is-Lm Model for he US Economy by Applying Jmuli ISSN 1857-9973 338:303.725.3(73) Dushko Josheski 1, Darko Lazarov 2 1 FTBL, UGD, Krse Misirkov bb, Sip, Macedonia, e-mail: dushkojosheski@gmail.com
Tourism forecasting using conditional volatility models
Tourism forecasing using condiional volailiy models ABSTRACT Condiional volailiy models are used in ourism demand sudies o model he effecs of shocks on demand volailiy, which arise from changes in poliical,
Sectoral oil consumption and economic growth in Pakistan: An ECM approach
AMERICAN JOURNAL OF SCIENTIFIC AND INDUSTRIAL RESEARCH 20, Science Huβ, h://www.scihub.org/ajsir ISSN: 253-649X, doi:0.525/ajsir.20.2.2.49.56 Secoral oil consumion and economic growh in Pakisan: An ECM
Vector autoregression VAR. Case 1
Vecor auoregression VAR So far we have focused mosl on models where deends onl on as. More generall we migh wan o consider oin models ha involve more han one variable. There are wo reasons: Firs, we migh
Impact of International Information Technology Transfer on National Productivity. Online Supplement
Impac of Inernaional Informaion Technology Transfer on Naional Prouciviy Online Supplemen Jungsoo Park Deparmen of Economics Sogang Universiy Seoul, Korea Email: jspark@sogang.ac.kr, Tel: 82-2-705-8697,
A STRUCTURAL VECTOR ERROR CORRECTION MODEL WITH SHORT-RUN AND LONG-RUN RESTRICTIONS
199 THE KOREAN ECONOMIC REVIEW Volume 4, Number 1, Summer 008 A STRUCTURAL VECTOR ERROR CORRECTION MODEL WITH SHORT-RUN AND LONG-RUN RESTRICTIONS KYUNGHO JANG* We consider srucural vecor error correcion
14 Autoregressive Moving Average Models
14 Auoregressive Moving Average Models In his chaper an imporan parameric family of saionary ime series is inroduced, he family of he auoregressive moving average, or ARMA, processes. For a large class
SEASONAL ADJUSTMENT VERSUS SEASONALITY MODELING: Effect on Tourism Demand Forecasting
Seasonal Asian-African Adjusmen Journal Versus of Economics Seasonaliy and Modeling: Economerics, Effec Vol. on Tourism 13, No. 1, Demand 2013: 71-84 Forecasing 71 SEASONAL ADJUSTMENT VERSUS SEASONALITY