Business Topics. Patrick Minford October 2009

Size: px
Start display at page:

Download "Business Topics. Patrick Minford October 2009"

Transcription

1 Business Topics Patrick Minford October 2009

2 Agenda Indicators and forecasts Housing Testing the climate change model

3 Polynomial Cointegration Tests of the Anthropogenic Theory of Global Warming Michael Beenstock Yaniv Reingewertz Hebrew University of Jerusalem October 2009

4 Introduction IPCC: Carbon emissions induce global warming Anthropogenic theory tested by calibration Since 1997 climatologists use cointegration Greenhouse gases are I(2) Polynomial cointegration tests reject anthropogenic theory

5 Road Map Climate Data: Palaeoclimatics IPCC and politics of global warming Circular Flow Models Greenhouse gas theory Polynomial cointegration Results

6 Global Temperature since 1850

7 CO2 Emissions since 1850

8 IPCC Intergovernmental Panel on Climate Change Established in 1988 by UNEP & WMO Report: The balance of evidence suggests a discernable human influence on global climate. Doubling of atmospheric CO2 will raise global temperature by 1-3.5ºC. Kyoto Protocol Report: Greenhouse effect very likely to exist. Doubling of CO2 concentrations raises temperature by ºC Report: Most of the observed increase in global average temperature since the mid 20 th century is very likely.. Doubling raises temperature by ºC. Stern Review 2007 Obama 2009

9 Main Conclusions No long run relationship between global temperature and the level of atmospheric CO2 concentration. Temperature and solar irradiance are stationary in 1 st differences. Greenhouse gases are stationary in 2 nd differences since Greenhouse theory confirmed in changes in atmospheric CO2. Blame the sun: solar irradiance at 10,000 year high. Economic growth not to blame for global warming. Reverse causality is likely from temperature to atmospheric CO2. Greenhouse theory corroborated by calibration in IPCC.

10 Newsweek April 18, 1975 The central fact is that after three quarters of a century of extraordinarily mild conditions, the earth s climate seems to be cooling down Climatologists are pessimistic that political leaders will take any action to compensate for climatic change, or even to allay its effects. They concede that some of the more spectacular solutions proposed such as melting the arctic ice cap by covering it with black soot might create problems far greater than they solve.

11 Greenhouse Gases Atmospheric concentrations (particles per mil): Oxygen (~20%), Nitrogen (~80%), Greenhouse gas (~0.04%), Water (~1%) CO %, methane (CH 4 ) %, nitrous oxide (N 2 O) % Residence times (approx): CH 4 10 years, N 2 O 100 years, CO2 absorbed by oceans & biosphere Radiative forcing (watts per m 2 ): rfco 2 = αlnco 2 α varies inversely with CO 2 α N2O = 10α α CH4 CH4 = 20α CO2

12 The Greenhouse Effect Source: DEFRA (2005)

13 Palaeoclimatics

14

15

16 Temperature since 800

17 Theory

18 A Simple GCM T = temperature S = solar irradiance C = atmospheric CO2 concentration E = anthropogenic emissions of CO2 A Uptake of atmospheric CO2 GWP = gross world product t t t t t t t t t t t t t t t e GWP E Law Henrys v E C T A A E C u C S T + + = = = = ln ln ) ' ( π φ β β β β α α α

19 General Solution of GCM E(u) = E(v) = 0 (10) ) (1 1 ) ( (9) ) (1 ) (1 1 ) ( = + = + = + = + = = = i i t i i i t i o t i i t i i i t i i i t i t E S C E S S T λ β λ αβ λ β βα λ λ β α λ α λ β α λ β α βα λ ) (1 ) ( βα β β α βα β βα β λ = > < + = de dt

20 Climatic Equilibrium

21 Empirical Methodology

22 Calibration v Estimation Meteorologists & climatologists calibrate. Economists borrowed calibration in 1980s. DSGE models = GCM models. Calibration mimics empirical moments. Critique: Pagan (1994), Hansen & Heckman (1996), Sims (1996). IPCC informed by calibration. Observational equivalence. Policy implications not robust.

23 Estimation Spurious regression in nonstationary data. Cointegration tests: Stern & Kaufmann (1997), Kaufmann & Stern (2002) Kaufmann, Kauppi & Stock (2006) Mills (2009) Liu & Rodriguez (2005) Results confirm IPCC.

24 Order of Integration Covariance stationarity: mean, variance, covariance independent of time. d Y t = e t ~ I(0) Y ~ I(d) Dickey-Fuller (1976) test: H 0 : d = 1 Phillips-Perron (1988) test: H 0 : d =1 Kwiatkowsky, Phillips, Schmidt & Shin (1992): H 0 : d = 0

25 Principles of Cointegration Y = α + βx + u Y ~ I(1) X ~ I(0) or I(2) plimβ = 0 X ~ I(1) u ~ I(1) spurious regression u ~ I(0) cointegrated (not spurious) Superconsistency (T 1½ ): plimβ = β despite dependence between X and u t statistics invalid

26 Polynomial Cointegration Engle-Granger T = α 0 + α 1 S + α 2 C + α 3 M + u T ~ I(1), S ~ I(1), C ~ I(2), M ~ I(2) u ~ I(0)? C = γ 0 + γ 1 M + g g ~ I(1)? T = α 0 + α 1 S + α 4 g + u Haldrup (1994): C = β 0 +β 1 S + β 2 T + β 3 M + v Kaufmann et al (2006), Mills (2009) use T as regressor!

27 Polynomial Cointegration Johansen Method Johansen (1995) Juselius (2007) C t = ω 0 + ω 1 M t + z t z ~ I(1) C t = ω 0 t + ω 1 M t + z t T t = π 0 + π 1 S t + π 2 z t + p t Liu and Rodriguez (2005) S strongly exogenous! p ~ I(0)

28 Data

29 Data Sources Annual Most data from NASA. Emissions: Boden, Marland & Andres (2009). Temperature data from Uptake of CO2. Data problems

30 Uptake of CO2 Biosphere Ocean Invisible Sink CO2 = E A E anthropogenic emissions V volcanic emissions A = B + O + IS - V (net uptake) Deforestation decreases B

31 Residual Net Uptake of CO2 Absorption

32 Rate of Uptake of CO2 Absorption rate

33 Solar Irradiance

34 Methane CH4 concentration (PPM)

35 Nitrous Oxide N2O concentration (PPM)

36 Temperature v C02 CO2 and T Index, 1880= CO2 T

37 DlnCO2-5 year av. Growth in CO2 MA(5)

38 Results

39 The Order of Integration of Atmospheric CO2 Test d Root Trend log lags ADF DW PP KPSS yes no no no no no no yes no no no no no no no no The PP test used the Newey-West bandwidth default of 4 lags and the KPSS test uses a bandwidth of 3 lags. The number of lags in the table refers to the number of augmentations in the ADF test statistic. In tests 2-8 the critical values for ADF and PP at p = are and for KPSS In test 1 these critical values are and respectively.

40 Orders of Integration Series d CO2 2 Temperature 1 CO2 Emissions 1 Solar irradiation 1 Methane 2 N2O 2 CO2 absorption 1 Rate of absorption 1

41 Greenhouse Gases rfco 2 = rfCH N 2 O+g Sample: s = R 2 = d g = 0: ADF4 = PP = KPSS = d g = 1: ADF4 = PP = KPSS = 0.085

42 Temperature Model Engle - Granger Modified greenhouse model: 1 st differences T = S rfco rfn 2 O 41.5 rfch g Sample: se = R 2 = ADF4 = *** PP = *** KPSS = 0.311*** Haldrup critical value = -5.2 Dropping g makes no difference Dropping S does! ADF = -2.1 KPSS = 1.1 Dropping GHG: ADF = KPSS = 0.5

43 A COMMON MISTAKE Haldrup (1994): LHS should be I(2) Kaufmann & Stern (2002), Kaufmann, Kauppi and Stock (2006), Mills (2009): LHS = T ~ I(1) Incorrect ADF -7 Correct ADF - 2.5! Critical value -5.2

44 Uptake of C02 Engle - Granger A t = E t T t Sample s = R 2 = ADF4 = PP = KPSS = Drop T: ADF = PP = -9 KPSS = Drop E: ADF = PP = KPSS = 1.34 A/CO2 = U t = E t 1.298T t Sample s = 2.02 R 2 adj = ADF4 = PP = KPSS = Drop T : ADF = PP = KPSS = Drop E: ADF = PP KPSS = 1.36

45 Error Correction Model Temperature T t = T t T t ( S t - S t-2 ) rfc t (0.5) (1.71) (2.51) (2.09) (4.08) rfn 2 O t u t-1 (2.41) (6.38) Adj R squared = DW = 1.97 LM = 2.24 se = 0.12

46 Error Correction Model Uptake Rate CO2 U t = EC t U t-1 (-0.06) (-4.85) (-2.15) -0.16( U t-1 - U t-4 ) E t-5 (-3.02) (1.65) T t-2 (1.23) R 2 adj = se = DW = LM = 0.909

47 Impulse Responses for Temperature Year Solar Irradiance 1 watt/m 2 rfco 2 1 watt/m 2 rfco 2 1 watt/m ? rfco 2 = lnCO 2 T e= CO 2 CO T 2 = dco2 m drfco 2 CO T 2 = ( e + CO m T 2 ) CO 2

48 Conclusions Greenhouse gases are I(2) since 1850 Temperature and solar irradiance are I(1) Global temperature does not polycointegrate with greenhouse gases Sun is main driver of global warming Misuse of cointegration Anthropogenic evidence is spurious Johansen polycointegration: methodological lacuna

49 The Bottom Line Rise in global temperature in 20th century 0.7 degrees C. Solar irradiance rose by about 0.3 watts (per sq.m.), contributing 0.44 degrees. Atmospheric CO2 rose 35% in the century; delta(rfco2) also rose, contributing 0.15 degrees- is now stable. On latest data T has stabilized since 1990; so has solar irradiance. If the sun continues to be stable, T will also stabilize.

50

ARDL Cointegration Tests for Beginner

ARDL Cointegration Tests for Beginner ARDL Cointegration Tests for Beginner Tuck Cheong TANG Department of Economics, Faculty of Economics & Administration University of Malaya Email: tangtuckcheong@um.edu.my DURATION: 3 HOURS On completing

More information

Darmstadt Discussion Papers in Economics

Darmstadt Discussion Papers in Economics Darmstadt Discussion Papers in Economics The Effect of Linear Time Trends on Cointegration Testing in Single Equations Uwe Hassler Nr. 111 Arbeitspapiere des Instituts für Volkswirtschaftslehre Technische

More information

9) Time series econometrics

9) Time series econometrics 30C00200 Econometrics 9) Time series econometrics Timo Kuosmanen Professor Management Science http://nomepre.net/index.php/timokuosmanen 1 Macroeconomic data: GDP Inflation rate Examples of time series

More information

This chapter reviews properties of regression estimators and test statistics based on

This chapter reviews properties of regression estimators and test statistics based on Chapter 12 COINTEGRATING AND SPURIOUS REGRESSIONS This chapter reviews properties of regression estimators and test statistics based on the estimators when the regressors and regressant are difference

More information

Fig. 3.2 on Page 101. Warming. Evidence for CO 2. History of Global Warming-2. Fig. 3.2 Page 101. Drilled cores from ocean floors

Fig. 3.2 on Page 101. Warming. Evidence for CO 2. History of Global Warming-2. Fig. 3.2 Page 101. Drilled cores from ocean floors Chemistry in Context: Chapter 3:The Chemistry of Global Warming Practice Problems: All Ch. 3 problems with the blue codes or answers on Page 521. Venus Atmospheric pressure is 90x that of Earth 96% CO

More information

Testing for non-stationarity

Testing for non-stationarity 20 November, 2009 Overview The tests for investigating the non-stationary of a time series falls into four types: 1 Check the null that there is a unit root against stationarity. Within these, there are

More information

1 Regression with Time Series Variables

1 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 information

7. Integrated Processes

7. 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 information

Does temperature contain a stochastic trend? Evaluating conflicting statistical results

Does temperature contain a stochastic trend? Evaluating conflicting statistical results Climatic Change (2010) 101:395 405 DOI 10.1007/s10584-009-9711-2 Does temperature contain a stochastic trend? Evaluating conflicting statistical results Robert K. Kaufmann Heikki Kauppi James H. Stock

More information

Volume 30, Issue 1. EUAs and CERs: Vector Autoregression, Impulse Response Function and Cointegration Analysis

Volume 30, Issue 1. EUAs and CERs: Vector Autoregression, Impulse Response Function and Cointegration Analysis Volume 30, Issue 1 EUAs and CERs: Vector Autoregression, Impulse Response Function and Cointegration Analysis Julien Chevallier Université Paris Dauphine Abstract EUAs are European Union Allowances traded

More information

ECON 4160, Spring term Lecture 12

ECON 4160, Spring term Lecture 12 ECON 4160, Spring term 2013. Lecture 12 Non-stationarity and co-integration 2/2 Ragnar Nymoen Department of Economics 13 Nov 2013 1 / 53 Introduction I So far we have considered: Stationary VAR, with deterministic

More information

7. Integrated Processes

7. 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 information

Introductory Workshop on Time Series Analysis. Sara McLaughlin Mitchell Department of Political Science University of Iowa

Introductory Workshop on Time Series Analysis. Sara McLaughlin Mitchell Department of Political Science University of Iowa Introductory Workshop on Time Series Analysis Sara McLaughlin Mitchell Department of Political Science University of Iowa Overview Properties of time series data Approaches to time series analysis Stationarity

More information

THE IMPACT OF REAL EXCHANGE RATE CHANGES ON SOUTH AFRICAN AGRICULTURAL EXPORTS: AN ERROR CORRECTION MODEL APPROACH

THE IMPACT OF REAL EXCHANGE RATE CHANGES ON SOUTH AFRICAN AGRICULTURAL EXPORTS: AN ERROR CORRECTION MODEL APPROACH THE IMPACT OF REAL EXCHANGE RATE CHANGES ON SOUTH AFRICAN AGRICULTURAL EXPORTS: AN ERROR CORRECTION MODEL APPROACH D. Poonyth and J. van Zyl 1 This study evaluates the long run and short run effects of

More information

Questions and Answers on Unit Roots, Cointegration, VARs and VECMs

Questions 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 information

Lecture 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 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 information

Cointegration modelling of climatic time series

Cointegration modelling of climatic time series Cointegration modelling of climatic time series Submitted by Alemtsehai Abate Turasie to the University of Exeter as a thesis for the degree of Doctor of Philosophy in Mathematics, September 2012 This

More information

What is Climate? Climate Change Evidence & Causes. Is the Climate Changing? Is the Climate Changing? Is the Climate Changing? Is the Climate Changing?

What is Climate? Climate Change Evidence & Causes. Is the Climate Changing? Is the Climate Changing? Is the Climate Changing? Is the Climate Changing? What is Climate? 1 Climate Change Evidence & Causes Refers to the average environmental conditions (i.e. temperature, precipitation, extreme events) in a given location over many years Climate is what

More information

CHAPTER 21: TIME SERIES ECONOMETRICS: SOME BASIC CONCEPTS

CHAPTER 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 information

Lecture 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 Lecture 5: Unit Roots, Cointegration and Error Correction Models The Spurious Regression Problem Prof. Massimo Guidolin 20192 Financial Econometrics Winter/Spring 2018 Overview Defining cointegration Vector

More information

Lecture 8a: Spurious Regression

Lecture 8a: Spurious Regression Lecture 8a: Spurious Regression 1 Old Stuff The traditional statistical theory holds when we run regression using (weakly or covariance) stationary variables. For example, when we regress one stationary

More information

Econometric Modelling of Climate Systems: The Equivalence of Energy Balance Models and Cointegrated Vector Autoregressions

Econometric Modelling of Climate Systems: The Equivalence of Energy Balance Models and Cointegrated Vector Autoregressions Econometric Modelling of Climate Systems: The Equivalence of Energy Balance Models and Cointegrated Vector Autoregressions Felix Pretis 1,2 1 Department of Economics & Nuffield College, University of Oxford

More information

BCT Lecture 3. Lukas Vacha.

BCT Lecture 3. Lukas Vacha. BCT Lecture 3 Lukas Vacha vachal@utia.cas.cz Stationarity and Unit Root Testing Why do we need to test for Non-Stationarity? The stationarity or otherwise of a series can strongly influence its behaviour

More information

The Chemistry of Global Warming

The Chemistry of Global Warming The Chemistry of Global Warming Venus Atmospheric pressure is 90x that of Earth 96% CO 2 and sulfuric acid clouds Average temperature = 450 C Expected temperature based on solar radiation and distance

More information

The Power of the KPSS Test for Cointegration when Residuals are Fractionally Integrated 1

The Power of the KPSS Test for Cointegration when Residuals are Fractionally Integrated 1 The Power of the KPSS Test for Cointegration when Residuals are Fractionally Integrated 1 by Philipp Sibbertsen 2 and Walter Krämer Fachbereich Statistik, Universität Dortmund, D-44221 Dortmund, Germany

More information

Oil price and macroeconomy in Russia. Abstract

Oil price and macroeconomy in Russia. Abstract Oil price and macroeconomy in Russia Katsuya Ito Fukuoka University Abstract In this note, using the VEC model we attempt to empirically investigate the effects of oil price and monetary shocks on the

More information

Vector error correction model, VECM Cointegrated VAR

Vector error correction model, VECM Cointegrated VAR 1 / 58 Vector error correction model, VECM Cointegrated VAR Chapter 4 Financial Econometrics Michael Hauser WS17/18 2 / 58 Content Motivation: plausible economic relations Model with I(1) variables: spurious

More information

Time Series Analysis. James D. Hamilton PRINCETON UNIVERSITY PRESS PRINCETON, NEW JERSEY

Time Series Analysis. James D. Hamilton PRINCETON UNIVERSITY PRESS PRINCETON, NEW JERSEY Time Series Analysis James D. Hamilton PRINCETON UNIVERSITY PRESS PRINCETON, NEW JERSEY & Contents PREFACE xiii 1 1.1. 1.2. Difference Equations First-Order Difference Equations 1 /?th-order Difference

More information

MEXICO S INDUSTRIAL ENGINE OF GROWTH: COINTEGRATION AND CAUSALITY

MEXICO S INDUSTRIAL ENGINE OF GROWTH: COINTEGRATION AND CAUSALITY NÚM. 126, MARZO-ABRIL DE 2003, PP. 34-41. MEXICO S INDUSTRIAL ENGINE OF GROWTH: COINTEGRATION AND CAUSALITY ALEJANDRO DÍAZ BAUTISTA* Abstract The present study applies the techniques of cointegration and

More information

Advanced Econometrics

Advanced Econometrics Based on the textbook by Verbeek: A Guide to Modern Econometrics Robert M. Kunst robert.kunst@univie.ac.at University of Vienna and Institute for Advanced Studies Vienna May 2, 2013 Outline Univariate

More information

CHAPTER III RESEARCH METHODOLOGY. trade balance performance of selected ASEAN-5 countries and exchange rate

CHAPTER III RESEARCH METHODOLOGY. trade balance performance of selected ASEAN-5 countries and exchange rate CHAPTER III RESEARCH METHODOLOGY 3.1 Research s Object The research object is taking the macroeconomic perspective and focused on selected ASEAN-5 countries. This research is conducted to describe how

More information

The causal relationship between energy consumption and GDP in Turkey

The causal relationship between energy consumption and GDP in Turkey The causal relationship between energy consumption and GDP in Turkey Huseyin Kalyoncu1, Ilhan Ozturk2, Muhittin Kaplan1 1Meliksah University, Faculty of Economics and Administrative Sciences, 38010, Kayseri,

More information

Stationarity and Cointegration analysis. Tinashe Bvirindi

Stationarity and Cointegration analysis. Tinashe Bvirindi Stationarity and Cointegration analysis By Tinashe Bvirindi tbvirindi@gmail.com layout Unit root testing Cointegration Vector Auto-regressions Cointegration in Multivariate systems Introduction Stationarity

More information

Nonstationary Panels

Nonstationary Panels Nonstationary Panels Based on chapters 12.4, 12.5, and 12.6 of Baltagi, B. (2005): Econometric Analysis of Panel Data, 3rd edition. Chichester, John Wiley & Sons. June 3, 2009 Agenda 1 Spurious Regressions

More information

Econ 423 Lecture Notes: Additional Topics in Time Series 1

Econ 423 Lecture Notes: Additional Topics in Time Series 1 Econ 423 Lecture Notes: Additional Topics in Time Series 1 John C. Chao April 25, 2017 1 These notes are based in large part on Chapter 16 of Stock and Watson (2011). They are for instructional purposes

More information

Climate Change. April 21, 2009

Climate Change. April 21, 2009 Climate Change Chapter 16 April 21, 2009 Reconstructing Past Climates Techniques Glacial landscapes (fossils) CLIMAP (ocean sediment) Ice cores (layering of precipitation) p Otoliths (CaCO 3 in fish sensory

More information

Defence Spending and Economic Growth: Re-examining the Issue of Causality for Pakistan and India

Defence Spending and Economic Growth: Re-examining the Issue of Causality for Pakistan and India The Pakistan Development Review 34 : 4 Part III (Winter 1995) pp. 1109 1117 Defence Spending and Economic Growth: Re-examining the Issue of Causality for Pakistan and India RIZWAN TAHIR 1. INTRODUCTION

More information

OUTWARD FDI, DOMESTIC INVESTMENT AND INFORMAL INSTITUTIONS: EVIDENCE FROM CHINA WAQAR AMEER & MOHAMMED SAUD M ALOTAISH

OUTWARD FDI, DOMESTIC INVESTMENT AND INFORMAL INSTITUTIONS: EVIDENCE FROM CHINA WAQAR AMEER & MOHAMMED SAUD M ALOTAISH International Journal of Economics, Commerce and Research (IJECR) ISSN(P): 2250-0006; ISSN(E): 2319-4472 Vol. 7, Issue 1, Feb 2017, 25-30 TJPRC Pvt. Ltd. OUTWARD FDI, DOMESTIC INVESTMENT AND INFORMAL INSTITUTIONS:

More information

10) Time series econometrics

10) Time series econometrics 30C00200 Econometrics 10) Time series econometrics Timo Kuosmanen Professor, Ph.D. 1 Topics today Static vs. dynamic time series model Suprious regression Stationary and nonstationary time series Unit

More information

Lecture 8a: Spurious Regression

Lecture 8a: Spurious Regression Lecture 8a: Spurious Regression 1 2 Old Stuff The traditional statistical theory holds when we run regression using stationary variables. For example, when we regress one stationary series onto another

More information

Economic modelling and forecasting. 2-6 February 2015

Economic modelling and forecasting. 2-6 February 2015 Economic modelling and forecasting 2-6 February 2015 Bank of England 2015 Ole Rummel Adviser, CCBS at the Bank of England ole.rummel@bankofengland.co.uk Philosophy of my presentations Everything should

More information

EC821: Time Series Econometrics, Spring 2003 Notes Section 9 Panel Unit Root Tests Avariety of procedures for the analysis of unit roots in a panel

EC821: Time Series Econometrics, Spring 2003 Notes Section 9 Panel Unit Root Tests Avariety of procedures for the analysis of unit roots in a panel EC821: Time Series Econometrics, Spring 2003 Notes Section 9 Panel Unit Root Tests Avariety of procedures for the analysis of unit roots in a panel context have been developed. The emphasis in this development

More information

Climate Change: Global Warming Claims

Climate Change: Global Warming Claims Climate Change: Global Warming Claims Background information (from Intergovernmental Panel on Climate Change): The climate system is a complex, interactive system consisting of the atmosphere, land surface,

More information

Time Series Analysis. James D. Hamilton PRINCETON UNIVERSITY PRESS PRINCETON, NEW JERSEY

Time Series Analysis. James D. Hamilton PRINCETON UNIVERSITY PRESS PRINCETON, NEW JERSEY Time Series Analysis James D. Hamilton PRINCETON UNIVERSITY PRESS PRINCETON, NEW JERSEY PREFACE xiii 1 Difference Equations 1.1. First-Order Difference Equations 1 1.2. pth-order Difference Equations 7

More information

Introduction to Modern Time Series Analysis

Introduction to Modern Time Series Analysis Introduction to Modern Time Series Analysis Gebhard Kirchgässner, Jürgen Wolters and Uwe Hassler Second Edition Springer 3 Teaching Material The following figures and tables are from the above book. They

More information

Aviation Demand and Economic Growth in the Czech Republic: Cointegration Estimation and Causality Analysis

Aviation Demand and Economic Growth in the Czech Republic: Cointegration Estimation and Causality Analysis Analyses Aviation Demand and Economic Growth in the Czech Republic: Cointegration Estimation and Causality Analysis Bilal Mehmood 1 Government College University, Lahore, Pakistan Amna Shahid 2 Government

More information

ECON 4160, Lecture 11 and 12

ECON 4160, Lecture 11 and 12 ECON 4160, 2016. Lecture 11 and 12 Co-integration Ragnar Nymoen Department of Economics 9 November 2017 1 / 43 Introduction I So far we have considered: Stationary VAR ( no unit roots ) Standard inference

More information

Ladu and Meleddu, International Journal of Applied Economics, 13(1), March 2016, 15-31

Ladu and Meleddu, International Journal of Applied Economics, 13(1), March 2016, 15-31 15 Productivity, Wage and Inflation Relationship for a Sample of Developed Countries: New Evidence from Panel Cointegration Tests with Multiple Structural s Maria Gabriela Ladu a* & Marta Meleddu b* a

More information

Government Expenditure and Economic Growth in Iran

Government Expenditure and Economic Growth in Iran International Letters of Social and Humanistic Sciences Online: 2013-09-26 ISSN: 2300-2697, Vol. 11, pp 76-83 doi:10.18052/www.scipress.com/ilshs.11.76 2013 SciPress Ltd., Switzerland Government Expenditure

More information

Working Papers in Economics

Working Papers in Economics Working Papers in Economics Department of Economics, Rensselaer Polytechnic Institute, 110 8 th Street, Troy, NY, 12180-3590, USA. Tel: +1-518-276-6387; Fax: +1-518-276-2235; URL: http://www.rpi.edu/dept/economics/;

More information

Equation for Global Warming

Equation for Global Warming Equation for Global Warming Derivation and Application Contents 1. Amazing carbon dioxide How can a small change in carbon dioxide (CO 2 ) content make a critical difference to the actual global surface

More information

What is the IPCC? Intergovernmental Panel on Climate Change

What is the IPCC? Intergovernmental Panel on Climate Change IPCC WG1 FAQ What is the IPCC? Intergovernmental Panel on Climate Change The IPCC is a scientific intergovernmental body set up by the World Meteorological Organization (WMO) and by the United Nations

More information

A distinct stronger warming in the tropical tropopause layer during using GPS radio occultation: Association with minor volcanic eruptions

A distinct stronger warming in the tropical tropopause layer during using GPS radio occultation: Association with minor volcanic eruptions A distinct stronger warming in the tropical tropopause layer during 2001-2010 using GPS radio occultation: Association with minor volcanic eruptions Sanjay Kumar Mehta 1*, Masatomo Fujiwara 2, and Toshitaka

More information

Why build a climate model

Why build a climate model Climate Modeling Why build a climate model Atmosphere H2O vapor and Clouds Absorbing gases CO2 Aerosol Land/Biota Surface vegetation Ice Sea ice Ice sheets (glaciers) Ocean Box Model (0 D) E IN = E OUT

More information

Econometrics I. Professor William Greene Stern School of Business Department of Economics 25-1/25. Part 25: Time Series

Econometrics I. Professor William Greene Stern School of Business Department of Economics 25-1/25. Part 25: Time Series Econometrics I Professor William Greene Stern School of Business Department of Economics 25-1/25 Econometrics I Part 25 Time Series 25-2/25 Modeling an Economic Time Series Observed y 0, y 1,, y t, What

More information

HANSEN MARS CHALLENGE

HANSEN MARS CHALLENGE HANSEN MARS CHALLENGE A challenge to Hansen et al 1988: No matter what scientific facts are presented to challenge the AGW ideology it is impossible for scientists to sway public opinion on this issue

More information

Outline 24: The Holocene Record

Outline 24: The Holocene Record Outline 24: The Holocene Record Climate Change in the Late Cenozoic New York Harbor in an ice-free world (= Eocene sea level) Kenneth Miller, Rutgers University An Ice-Free World: eastern U.S. shoreline

More information

Extremes of Weather and the Latest Climate Change Science. Prof. Richard Allan, Department of Meteorology University of Reading

Extremes of Weather and the Latest Climate Change Science. Prof. Richard Allan, Department of Meteorology University of Reading Extremes of Weather and the Latest Climate Change Science Prof. Richard Allan, Department of Meteorology University of Reading Extreme weather climate change Recent extreme weather focusses debate on climate

More information

Thursday, November 1st.

Thursday, November 1st. Thursday, November 1st. Announcements. Homework 7 - due Tuesday, Nov. 6 Homework 8 - paper 2 topics, questions and sources due Tuesday, Nov. 13 Midterm Paper 2 - due Tuesday, Nov. 20 I will hand out a

More information

Title. Description. Quick start. Menu. stata.com. xtcointtest Panel-data cointegration tests

Title. Description. Quick start. Menu. stata.com. xtcointtest Panel-data cointegration tests Title stata.com xtcointtest Panel-data cointegration tests Description Quick start Menu Syntax Options Remarks and examples Stored results Methods and formulas References Also see Description xtcointtest

More information

Relationship between Energy Consumption and GDP in Iran

Relationship between Energy Consumption and GDP in Iran Relationship between Energy Consumption and GDP in Iran Gudarzi Farahani, Yazdan 1 Soheli Ghasemi, Banafshe 2 * 1. M.A. student in Economics, University of Tehran, Faculty of economics, Shomali Kargar,

More information

Population Growth and Economic Development: Test for Causality

Population Growth and Economic Development: Test for Causality The Lahore Journal of Economics 11 : 2 (Winter 2006) pp. 71-77 Population Growth and Economic Development: Test for Causality Khalid Mushtaq * Abstract This paper examines the existence of a long-run relationship

More information

Why I Am a Climate Realist. by Dr. Willem de Lange

Why I Am a Climate Realist. by Dr. Willem de Lange Why I Am a Climate Realist by Dr. Willem de Lange SPPI Commentary & Essay Series! May 27, 2009 Why I Am a Climate Realist by Dr. Willem de Lange May 23, 2009 In 1996 the United Nations Intergovernmental

More information

Stationarity and cointegration tests: Comparison of Engle - Granger and Johansen methodologies

Stationarity and cointegration tests: Comparison of Engle - Granger and Johansen methodologies MPRA Munich Personal RePEc Archive Stationarity and cointegration tests: Comparison of Engle - Granger and Johansen methodologies Faik Bilgili Erciyes University, Faculty of Economics and Administrative

More information

Multivariate Time Series: Part 4

Multivariate Time Series: Part 4 Multivariate Time Series: Part 4 Cointegration Gerald P. Dwyer Clemson University March 2016 Outline 1 Multivariate Time Series: Part 4 Cointegration Engle-Granger Test for Cointegration Johansen Test

More information

XV. Understanding recent climate variability

XV. Understanding recent climate variability XV. Understanding recent climate variability review temperature from thermometers, satellites, glacier lengths and boreholes all show significant warming in the 2th C+ reconstruction of past temperatures

More information

Section 2: The Atmosphere

Section 2: The Atmosphere Section 2: The Atmosphere Preview Classroom Catalyst Objectives The Atmosphere Composition of the Atmosphere Air Pressure Layers of the Atmosphere The Troposphere Section 2: The Atmosphere Preview, continued

More information

11/18/2008. So run regression in first differences to examine association. 18 November November November 2008

11/18/2008. So run regression in first differences to examine association. 18 November November November 2008 Time Series Econometrics 7 Vijayamohanan Pillai N Unit Root Tests Vijayamohan: CDS M Phil: Time Series 7 1 Vijayamohan: CDS M Phil: Time Series 7 2 R 2 > DW Spurious/Nonsense Regression. Integrated but

More information

E 4160 Autumn term Lecture 9: Deterministic trends vs integrated series; Spurious regression; Dickey-Fuller distribution and test

E 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 information

Econometrics and Structural

Econometrics and Structural Introduction to Time Series Econometrics and Structural Breaks Ziyodullo Parpiev, PhD Outline 1. Stochastic processes 2. Stationary processes 3. Purely random processes 4. Nonstationary processes 5. Integrated

More information

Climate Change 2007: The Physical Science Basis

Climate Change 2007: The Physical Science Basis Climate Change 2007: The Physical Science Basis Working Group I Contribution to the IPCC Fourth Assessment Report Presented by R.K. Pachauri, IPCC Chair and Bubu Jallow, WG 1 Vice Chair Nairobi, 6 February

More information

Financial Econometrics

Financial Econometrics Financial Econometrics Long-run Relationships in Finance Gerald P. Dwyer Trinity College, Dublin January 2016 Outline 1 Long-Run Relationships Review of Nonstationarity in Mean Cointegration Vector Error

More information

IS THERE A COINTEGRATION RELATIONSHIP BETWEEN ENERGY CONSUMPTION AND GDP IN IRAN?

IS THERE A COINTEGRATION RELATIONSHIP BETWEEN ENERGY CONSUMPTION AND GDP IN IRAN? IS THERE A COINTEGRATION RELATIONSHIP BETWEEN ENERGY CONSUMPTION AND GDP IN IRAN? Aliasghar Sadeghimojarad- Sina Mehrabirad Abstract This paper tries to unfold the linkage between energy consumption and

More information

On Consistency of Tests for Stationarity in Autoregressive and Moving Average Models of Different Orders

On Consistency of Tests for Stationarity in Autoregressive and Moving Average Models of Different Orders American Journal of Theoretical and Applied Statistics 2016; 5(3): 146-153 http://www.sciencepublishinggroup.com/j/ajtas doi: 10.11648/j.ajtas.20160503.20 ISSN: 2326-8999 (Print); ISSN: 2326-9006 (Online)

More information

Nonstationary Time Series:

Nonstationary Time Series: Nonstationary Time Series: Unit Roots Egon Zakrajšek Division of Monetary Affairs Federal Reserve Board Summer School in Financial Mathematics Faculty of Mathematics & Physics University of Ljubljana September

More information

9/5/16. Section 3-4: Radiation, Energy, Climate. Common Forms of Energy Transfer in Climate. Electromagnetic radiation.

9/5/16. Section 3-4: Radiation, Energy, Climate. Common Forms of Energy Transfer in Climate. Electromagnetic radiation. Section 3-4: Radiation, Energy, Climate Learning outcomes types of energy important to the climate system Earth energy balance (top of atm., surface) greenhouse effect natural and anthropogenic forcings

More information

Stefan-Boltzmann law for the Earth as a black body (or perfect radiator) gives:

Stefan-Boltzmann law for the Earth as a black body (or perfect radiator) gives: 2. Derivation of IPCC expression ΔF = 5.35 ln (C/C 0 ) 2.1 Derivation One The assumptions we will make allow us to represent the real atmosphere. This remarkably reasonable representation of the real atmosphere

More information

Chapter 14: The Changing Climate

Chapter 14: The Changing Climate Chapter 14: The Changing Climate Detecting Climate Change Natural Causes of Climate Change Anthropogenic Causes of Climate Change Possible Consequences of Global Warming Climate Change? -Paleo studies

More information

It is easily seen that in general a linear combination of y t and x t is I(1). However, in particular cases, it can be I(0), i.e. stationary.

It is easily seen that in general a linear combination of y t and x t is I(1). However, in particular cases, it can be I(0), i.e. stationary. 6. COINTEGRATION 1 1 Cointegration 1.1 Definitions I(1) variables. z t = (y t x t ) is I(1) (integrated of order 1) if it is not stationary but its first difference z t is stationary. It is easily seen

More information

Weather and Climate Change

Weather and Climate Change Weather and Climate Change What if the environmental lapse rate falls between the moist and dry adiabatic lapse rates? The atmosphere is unstable for saturated air parcels but stable for unsaturated air

More information

Introduction to Algorithmic Trading Strategies Lecture 3

Introduction to Algorithmic Trading Strategies Lecture 3 Introduction to Algorithmic Trading Strategies Lecture 3 Pairs Trading by Cointegration Haksun Li haksun.li@numericalmethod.com www.numericalmethod.com Outline Distance method Cointegration Stationarity

More information

Contents. Part I Statistical Background and Basic Data Handling 5. List of Figures List of Tables xix

Contents. Part I Statistical Background and Basic Data Handling 5. List of Figures List of Tables xix Contents List of Figures List of Tables xix Preface Acknowledgements 1 Introduction 1 What is econometrics? 2 The stages of applied econometric work 2 Part I Statistical Background and Basic Data Handling

More information

The Seasonal KPSS Test When Neglecting Seasonal Dummies: A Monte Carlo analysis. Ghassen El Montasser, Talel Boufateh, Fakhri Issaoui

The Seasonal KPSS Test When Neglecting Seasonal Dummies: A Monte Carlo analysis. Ghassen El Montasser, Talel Boufateh, Fakhri Issaoui EERI Economics and Econometrics Research Institute The Seasonal KPSS Test When Neglecting Seasonal Dummies: A Monte Carlo analysis Ghassen El Montasser, Talel Boufateh, Fakhri Issaoui EERI Research Paper

More information

Economics 308: Econometrics Professor Moody

Economics 308: Econometrics Professor Moody Economics 308: Econometrics Professor Moody References on reserve: Text Moody, Basic Econometrics with Stata (BES) Pindyck and Rubinfeld, Econometric Models and Economic Forecasts (PR) Wooldridge, Jeffrey

More information

Econometrics Lab Hour Session 6

Econometrics Lab Hour Session 6 Econometrics Lab Hour Session 6 Agustín Bénétrix benetria@tcd.ie Office hour: Wednesday 4-5 Room 3021 Martin Schmitz schmitzm@tcd.ie Office hour: Monday 5-6 Room 3021 Outline Importing the dataset Time

More information

Arctic Climate Change. Glen Lesins Department of Physics and Atmospheric Science Dalhousie University Create Summer School, Alliston, July 2013

Arctic Climate Change. Glen Lesins Department of Physics and Atmospheric Science Dalhousie University Create Summer School, Alliston, July 2013 Arctic Climate Change Glen Lesins Department of Physics and Atmospheric Science Dalhousie University Create Summer School, Alliston, July 2013 When was this published? Observational Evidence for Arctic

More information

E 4101/5101 Lecture 9: Non-stationarity

E 4101/5101 Lecture 9: Non-stationarity E 4101/5101 Lecture 9: Non-stationarity Ragnar Nymoen 30 March 2011 Introduction I Main references: Hamilton Ch 15,16 and 17. Davidson and MacKinnon Ch 14.3 and 14.4 Also read Ch 2.4 and Ch 2.5 in Davidson

More information

What factors affect climate? Dr. Michael J Passow

What factors affect climate? Dr. Michael J Passow What factors affect climate? Dr. Michael J Passow Energy from the Sun (mostly light and heat) radiates to Earth SUN 150 x 10 6 km (92 x 10 6 mi) EARTH Challenge: If the speed of light is 300,000 km/sec,

More information

APPLIED TIME SERIES ECONOMETRICS

APPLIED TIME SERIES ECONOMETRICS APPLIED TIME SERIES ECONOMETRICS Edited by HELMUT LÜTKEPOHL European University Institute, Florence MARKUS KRÄTZIG Humboldt University, Berlin CAMBRIDGE UNIVERSITY PRESS Contents Preface Notation and Abbreviations

More information

Cointegration and the joint con rmation hypothesis

Cointegration and the joint con rmation hypothesis Cointegration and the joint con rmation hypothesis VASCO J. GABRIEL Department of Economics, Birkbeck College, UK University of Minho, Portugal October 2001 Abstract Recent papers by Charemza and Syczewska

More information

Moreover, the second term is derived from: 1 T ) 2 1

Moreover, the second term is derived from: 1 T ) 2 1 170 Moreover, the second term is derived from: 1 T T ɛt 2 σ 2 ɛ. Therefore, 1 σ 2 ɛt T y t 1 ɛ t = 1 2 ( yt σ T ) 2 1 2σ 2 ɛ 1 T T ɛt 2 1 2 (χ2 (1) 1). (b) Next, consider y 2 t 1. T E y 2 t 1 T T = E(y

More information

Lecture 3. - Global Sulfur, Nitrogen, Carbon Cycles - Short-term vs. Long-term carbon cycle - CO 2 & Temperature: Last 100,000+ years

Lecture 3. - Global Sulfur, Nitrogen, Carbon Cycles - Short-term vs. Long-term carbon cycle - CO 2 & Temperature: Last 100,000+ years Lecture 3 - Global Sulfur, Nitrogen, Carbon Cycles - Short-term vs. Long-term carbon cycle - CO 2 & Temperature: Last 100,000+ years METR 113/ENVS 113 Spring Semester 2011 March 1, 2011 Suggested Reading

More information

3. Carbon Dioxide (CO 2 )

3. Carbon Dioxide (CO 2 ) 3. Carbon Dioxide (CO 2 ) Basic information on CO 2 with regard to environmental issues Carbon dioxide (CO 2 ) is a significant greenhouse gas that has strong absorption bands in the infrared region and

More information

International Monetary Policy Spillovers

International Monetary Policy Spillovers International Monetary Policy Spillovers Dennis Nsafoah Department of Economics University of Calgary Canada November 1, 2017 1 Abstract This paper uses monthly data (from January 1997 to April 2017) to

More information

Unique nature of Earth s atmosphere: O 2 present photosynthesis

Unique nature of Earth s atmosphere: O 2 present photosynthesis Atmospheric composition Major components N 2 78% O 2 21% Ar ~1% Medium components CO 2 370 ppmv (rising about 1.5 ppmv/year) CH 4 1700 ppbv H 2 O variable Trace components H 2 600 ppbv N 2 O 310 ppbv CO

More information

PHYS:1200 LECTURE 18 THERMODYNAMICS (3)

PHYS:1200 LECTURE 18 THERMODYNAMICS (3) 1 PHYS:1200 LECTURE 18 THERMODYNAMICS (3) This lecture presents a more detailed discussion of heat flow by radiation and its importance in the physics of the atmosphere. We will discuss some important

More information

Climate Variability and Change: Basic Concepts. Jeffrey A. Andresen Dept. of Geography Michigan State University

Climate Variability and Change: Basic Concepts. Jeffrey A. Andresen Dept. of Geography Michigan State University Climate Variability and Change: Basic Concepts Jeffrey A. Andresen Dept. of Geography Michigan State University Weather versus Climate The American Meteorological Society s Glossary of Meteorology defines

More information

The Canadian Climate Model 's Epic Failure November 2016

The Canadian Climate Model 's Epic Failure November 2016 The Canadian Climate Model 's Epic Failure November 2016 By: Ken Gregory The Canadian Centre for Climate Modeling and Analysis located at the University of Victoria in British Columbia submitted five runs

More information

An Econometric Modeling for India s Imports and exports during

An Econometric Modeling for India s Imports and exports during Inter national Journal of Pure and Applied Mathematics Volume 113 No. 6 2017, 242 250 ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version) url: http://www.ijpam.eu ijpam.eu An Econometric

More information

Wesleyan Economic Working Papers

Wesleyan Economic Working Papers Wesleyan Economic Working Papers http://repec.wesleyan.edu/ N o : 2016-002 Conventional monetary policy and the degree of interest rate pass through in the long run: a non-normal approach Dong-Yop Oh,

More information