Interdependence between the euro area and the US: What role for EMU?

Size: px
Start display at page:

Download "Interdependence between the euro area and the US: What role for EMU?"

Transcription

1 Michael Ehrmann and Marcel Fratzscher: Interdependence between the euro area and the US: What role for EMU? Jon Faust faustj/jon Federal Reserve Board Fed/ECB/CGES/CFS Conference, 2003 p.1/19

2 Interesting paper Good topic Fed/ECB/CGES/CFS Conference, 2003 p.2/19

3 Interesting paper Good topic How does EMU show in the data? Fed/ECB/CGES/CFS Conference, 2003 p.2/19

4 Interesting paper Good topic How does EMU show in the data? Good approach Fed/ECB/CGES/CFS Conference, 2003 p.2/19

5 Interesting paper Good topic How does EMU show in the data? Good approach Effects may show first in financial markets especially in response standardized macro news Fed/ECB/CGES/CFS Conference, 2003 p.2/19

6 Interesting paper Good topic How does EMU show in the data? Good approach Effects may show first in financial markets especially in response standardized macro news Good implementation Fed/ECB/CGES/CFS Conference, 2003 p.2/19

7 Interesting paper Good topic How does EMU show in the data? Good approach Effects may show first in financial markets especially in response standardized macro news Good implementation Follows good earlier work (ABDV) Careful and clear Fed/ECB/CGES/CFS Conference, 2003 p.2/19

8 Interesting results Fed/ECB/CGES/CFS Conference, 2003 p.3/19

9 Interesting results Growing links among announcements (Table 10) fascinating Fed/ECB/CGES/CFS Conference, 2003 p.3/19

10 Interesting results Growing links among announcements (Table 10) fascinating Big lit. on changing co-movement Hard to find evidence (many papers including Doyle and Faust) Fed/ECB/CGES/CFS Conference, 2003 p.3/19

11 Interesting results Growing links among announcements (Table 10) fascinating Big lit. on changing co-movement Hard to find evidence (many papers including Doyle and Faust) Surprising find anything in 10 yr. sample Requires careful interpretation Fed/ECB/CGES/CFS Conference, 2003 p.3/19

12 My discussion: 2 main issues Fed/ECB/CGES/CFS Conference, 2003 p.4/19

13 My discussion: 2 main issues Role of lagged foreign rate Complicates the interpretation I ll show may be important Fed/ECB/CGES/CFS Conference, 2003 p.4/19

14 My discussion: 2 main issues Role of lagged foreign rate Complicates the interpretation I ll show may be important Intradaily data/longer sample Can do intradaily: FRWW/ABDV I ll show a few results Fed/ECB/CGES/CFS Conference, 2003 p.4/19

15 The lagged foreign rate change The U.S. mean equation: r US t = α 2 + β2 EA rt EA + other terms Fed/ECB/CGES/CFS Conference, 2003 p.5/19

16 The lagged foreign rate change The U.S. mean equation: r US t = α 2 + β2 EA rt EA + other terms r EA t lagged because of quote timing Fed/ECB/CGES/CFS Conference, 2003 p.5/19

17 The lagged foreign rate change The U.S. mean equation: r US t = α 2 + β2 EA rt EA + other terms r EA t lagged because of quote timing Rise in magnitude and significance of ˆβ 2 is major part of story Fed/ECB/CGES/CFS Conference, 2003 p.5/19

18 What is lagged EA rate change measuring? Fed/ECB/CGES/CFS Conference, 2003 p.6/19

19 What is lagged EA rate change measuring? LHS: r US t is from 17:30 to 17:30, NY time Fed/ECB/CGES/CFS Conference, 2003 p.6/19

20 What is lagged EA rate change measuring? LHS: r US t is from 17:30 to 17:30, NY time RHS: r EA t is from 5am to 5am, NY Fed/ECB/CGES/CFS Conference, 2003 p.6/19

21 What is lagged EA rate change measuring? LHS: r US t is from 17:30 to 17:30, NY time RHS: r EA t is from 5am to 5am, NY EA change at t determined before most NY markets open at t; before U.S. macro news at t. Fed/ECB/CGES/CFS Conference, 2003 p.6/19

22 But... r EA t not predetermined in U.S. regression Fed/ECB/CGES/CFS Conference, 2003 p.7/19

23 But... r EA t not predetermined in U.S. regression Time periods spanned by LHS and RHS rate changes overlap from 17:30 at t 1 to 5:00 at t. Fed/ECB/CGES/CFS Conference, 2003 p.7/19

24 But... r EA t not predetermined in U.S. regression Time periods spanned by LHS and RHS rate changes overlap from 17:30 at t 1 to 5:00 at t. News moving rates in this window will be attributed to β 2 even if no EA US causality. Fed/ECB/CGES/CFS Conference, 2003 p.7/19

25 But... r EA t not predetermined in U.S. regression Time periods spanned by LHS and RHS rate changes overlap from 17:30 at t 1 to 5:00 at t. News moving rates in this window will be attributed to β 2 even if no EA US causality. ˆβ2 should not be interpreted as importance of EA for US. Fed/ECB/CGES/CFS Conference, 2003 p.7/19

26 Practical importance? Fed/ECB/CGES/CFS Conference, 2003 p.8/19

27 Practical importance? ˆβ 2 could be rise in share of news arriving overlap window Fed/ECB/CGES/CFS Conference, 2003 p.8/19

28 Practical importance? ˆβ 2 could be rise in share of news arriving overlap window Intuition: surely not much happens from 5:30pm to 5am NY Fed/ECB/CGES/CFS Conference, 2003 p.8/19

29 Practical importance? ˆβ 2 could be rise in share of news arriving overlap window Intuition: surely not much happens from 5:30pm to 5am NY In Fi. Mkt. only reliable intuition is: you should verify your intuitions Fed/ECB/CGES/CFS Conference, 2003 p.8/19

30 A quick check Fed/ECB/CGES/CFS Conference, 2003 p.9/19

31 A quick check There are $ markets open at 5:00am NY: $ LIBOR. Fed/ECB/CGES/CFS Conference, 2003 p.9/19

32 A quick check There are $ markets open at 5:00am NY: $ LIBOR. Include r $5am t in the regression. Fed/ECB/CGES/CFS Conference, 2003 p.9/19

33 A quick check There are $ markets open at 5:00am NY: $ LIBOR. Include r $5am t in the regression. May soak up effect of news in problem time window. Fed/ECB/CGES/CFS Conference, 2003 p.9/19

34 A quick check There are $ markets open at 5:00am NY: $ LIBOR. Include r $5am t in the regression. May soak up effect of news in problem time window. Just suggestive; does not fix the simultaneity problem. Fed/ECB/CGES/CFS Conference, 2003 p.9/19

35 Quick and dirty estimates My equation: r US t = α 2 + β2 EA rt EA + γ r 5am t + 3 lags LHS + ε t Fed/ECB/CGES/CFS Conference, 2003 p.10/19

36 Quick and dirty estimates My equation: r US t = α 2 + β2 EA rt EA + γ r 5am t + 3 lags LHS + ε t My data; slightly different sample period; no dummies or news surprises simple OLS. Fed/ECB/CGES/CFS Conference, 2003 p.10/19

37 Results ˆβ 2 ˆγ ˆβ2 ˆγ EF *** new *** add γ *** *** Fed/ECB/CGES/CFS Conference, 2003 p.11/19

38 Digression on units ABDV, EF, others put surprises in std. dev. units Fed/ECB/CGES/CFS Conference, 2003 p.12/19

39 Digression on units ABDV, EF, others put surprises in std. dev. units I prefer natural units of the data item. Fed/ECB/CGES/CFS Conference, 2003 p.12/19

40 Digression on units ABDV, EF, others put surprises in std. dev. units I prefer natural units of the data item. At one level, just normalization isssue Fed/ECB/CGES/CFS Conference, 2003 p.12/19

41 But... Intuitions/theory on magnitude are about natural units Fed/ECB/CGES/CFS Conference, 2003 p.13/19

42 But... Intuitions/theory on magnitude are about natural units Failure to convert back yields interp. errors. Fed/ECB/CGES/CFS Conference, 2003 p.13/19

43 But... Intuitions/theory on magnitude are about natural units Failure to convert back yields interp. errors. Difficult to compare across papers with diff. samples. Fed/ECB/CGES/CFS Conference, 2003 p.13/19

44 But... Intuitions/theory on magnitude are about natural units Failure to convert back yields interp. errors. Difficult to compare across papers with diff. samples. I ll report in natural units below Fed/ECB/CGES/CFS Conference, 2003 p.13/19

45 Intradaily data/longer sample U.S. data releases carefully timed; can study surprises in narrow time window Fed/ECB/CGES/CFS Conference, 2003 p.14/19

46 Intradaily data/longer sample U.S. data releases carefully timed; can study surprises in narrow time window Much work says: Response is complete in minutes Can obtain much more precise estimates Fed/ECB/CGES/CFS Conference, 2003 p.14/19

47 Intradaily data/longer sample U.S. data releases carefully timed; can study surprises in narrow time window Much work says: Response is complete in minutes Can obtain much more precise estimates Benefits for Europe as well; need bigger window. Fed/ECB/CGES/CFS Conference, 2003 p.14/19

48 Intradaily data/longer sample U.S. data releases carefully timed; can study surprises in narrow time window Much work says: Response is complete in minutes Can obtain much more precise estimates Benefits for Europe as well; need bigger window. I ll give examples: longer sample/smaller window Fed/ECB/CGES/CFS Conference, 2003 p.14/19

49 Basics Details, see: Faust, Rogers, Wang, Wright (2002) Fed/ECB/CGES/CFS Conference, 2003 p.15/19

50 Basics Details, see: Faust, Rogers, Wang, Wright (2002) Sample , 2 U.S. recessions Fed/ECB/CGES/CFS Conference, 2003 p.15/19

51 Basics Details, see: Faust, Rogers, Wang, Wright (2002) Sample , 2 U.S. recessions Basic regression: d t = βs t + ε t d t rate change from -5 min to +15 min s t the data surprise Fed/ECB/CGES/CFS Conference, 2003 p.15/19

52 Basics Details, see: Faust, Rogers, Wang, Wright (2002) Sample , 2 U.S. recessions Basic regression: d t = βs t + ε t d t rate change from -5 min to +15 min s t the data surprise Separate eqn. for each announcement type Fed/ECB/CGES/CFS Conference, 2003 p.15/19

53 Basics Details, see: Faust, Rogers, Wang, Wright (2002) Sample , 2 U.S. recessions Basic regression: d t = βs t + ε t d t rate change from -5 min to +15 min s t the data surprise Separate eqn. for each announcement type 1 obs. per announcement; no lag needed. Fed/ECB/CGES/CFS Conference, 2003 p.15/19

54 Remember Different data, different method, different sample period. Fed/ECB/CGES/CFS Conference, 2003 p.16/19

55 Significance, pre-emu US EA effects EF Payrolls *** *** GDP ** R Sales *** CPI *** Fed/ECB/CGES/CFS Conference, 2003 p.17/19

56 Significance, pre-emu US EA effects EF Payrolls *** *** GDP ** R Sales *** CPI *** Note: for all are significant Fed/ECB/CGES/CFS Conference, 2003 p.17/19

57 Size (BP) of pre-emu US EA effects EF Payrolls 100T GDP %AR R Sales % CPI % Fed/ECB/CGES/CFS Conference, 2003 p.18/19

58 Summary Paper is on right track, small changes will yield big gains Fed/ECB/CGES/CFS Conference, 2003 p.19/19

59 Summary Paper is on right track, small changes will yield big gains Resolve simultaneity, think about longer sample/intradaily Fed/ECB/CGES/CFS Conference, 2003 p.19/19

60 Summary Paper is on right track, small changes will yield big gains Resolve simultaneity, think about longer sample/intradaily Table 10 very intriguing Fed/ECB/CGES/CFS Conference, 2003 p.19/19

61 Summary Paper is on right track, small changes will yield big gains Resolve simultaneity, think about longer sample/intradaily Table 10 very intriguing New idea: add UK as a control group Fed/ECB/CGES/CFS Conference, 2003 p.19/19

LECTURE 3 The Effects of Monetary Changes: Statistical Identification. September 5, 2018

LECTURE 3 The Effects of Monetary Changes: Statistical Identification. September 5, 2018 Economics 210c/236a Fall 2018 Christina Romer David Romer LECTURE 3 The Effects of Monetary Changes: Statistical Identification September 5, 2018 I. SOME BACKGROUND ON VARS A Two-Variable VAR Suppose the

More information

Introduction to Econometrics

Introduction to Econometrics Introduction to Econometrics STAT-S-301 Introduction to Time Series Regression and Forecasting (2016/2017) Lecturer: Yves Dominicy Teaching Assistant: Elise Petit 1 Introduction to Time Series Regression

More information

EMERGING MARKETS - Lecture 2: Methodology refresher

EMERGING MARKETS - Lecture 2: Methodology refresher EMERGING MARKETS - Lecture 2: Methodology refresher Maria Perrotta April 4, 2013 SITE http://www.hhs.se/site/pages/default.aspx My contact: maria.perrotta@hhs.se Aim of this class There are many different

More information

TESTING FOR CO-INTEGRATION

TESTING FOR CO-INTEGRATION Bo Sjö 2010-12-05 TESTING FOR CO-INTEGRATION To be used in combination with Sjö (2008) Testing for Unit Roots and Cointegration A Guide. Instructions: Use the Johansen method to test for Purchasing Power

More information

BEE2006: Statistics and Econometrics

BEE2006: Statistics and Econometrics BEE2006: Statistics and Econometrics Tutorial 2: Time Series - Regression Analysis and Further Issues (Part 1) February 1, 2013 Tutorial 2: Time Series - Regression Analysis and Further Issues (Part BEE2006:

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

11. Simultaneous-Equation Models

11. Simultaneous-Equation Models 11. Simultaneous-Equation Models Up to now: Estimation and inference in single-equation models Now: Modeling and estimation of a system of equations 328 Example: [I] Analysis of the impact of advertisement

More information

Surprise indexes and nowcasting: why do markets react to macroeconomic news?

Surprise indexes and nowcasting: why do markets react to macroeconomic news? Introduction: surprise indexes Surprise indexes and nowcasting: why do markets react to macroeconomic news? Alberto Caruso Confindustria and Université libre de Bruxelles Conference on Real-Time Data Analysis,

More information

SIMULTANEOUS EQUATION MODEL

SIMULTANEOUS EQUATION MODEL SIMULTANEOUS EQUATION MODEL ONE Equation Model (revisited) Characteristics: One dependent variable (Y): as a regressand One ore more independent variables (X): as regressors One way causality relationship:

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

ISM Manufacturing Index Update & Breakdown

ISM Manufacturing Index Update & Breakdown ISM Manufacturing Index Update & Breakdown February 2018 Monthly Update Based On The Leading ISM Manufacturing Index What Is This Report About? (1/2) This presentation breaks down the latest ISM manufacturing

More information

Granger Causality and Dynamic Structural Systems 1

Granger Causality and Dynamic Structural Systems 1 Granger Causality and Dynamic Structural Systems 1 Halbert White and Xun Lu Department of Economics, University of California, San Diego December 10, 2009 1 forthcoming, Journal of Financial Econometrics

More information

Lucrezia Reichlin London Business School & Now-Casting Economics Ltd and Silvia Miranda Agrippino, Now-Casting Economics Ltd

Lucrezia Reichlin London Business School & Now-Casting Economics Ltd and Silvia Miranda Agrippino, Now-Casting Economics Ltd NOW-CASTING AND THE REAL TIME DATA FLOW Lucrezia Reichlin London Business School & Now-Casting Economics Ltd and Silvia Miranda Agrippino, Now-Casting Economics Ltd PRESENTATION AT BIS, HONG KONG 22 ND

More information

LECTURE 15: SIMPLE LINEAR REGRESSION I

LECTURE 15: SIMPLE LINEAR REGRESSION I David Youngberg BSAD 20 Montgomery College LECTURE 5: SIMPLE LINEAR REGRESSION I I. From Correlation to Regression a. Recall last class when we discussed two basic types of correlation (positive and negative).

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

Forecasting. Simultaneous equations bias (Lect 16)

Forecasting. Simultaneous equations bias (Lect 16) Forecasting. Simultaneous equations bias (Lect 16) Ragnar Nymoen University of Oslo 11 April 2013 1 / 20 References Same as to Lecture 15 (as a background to the forecasting part) HGL, Ch 9.7.2 (forecasting

More information

Lecture#12. Instrumental variables regression Causal parameters III

Lecture#12. Instrumental variables regression Causal parameters III Lecture#12 Instrumental variables regression Causal parameters III 1 Demand experiment, market data analysis & simultaneous causality 2 Simultaneous causality Your task is to estimate the demand function

More information

Section 4.6 Negative Exponents

Section 4.6 Negative Exponents Section 4.6 Negative Exponents INTRODUCTION In order to understand negative exponents the main topic of this section we need to make sure we understand the meaning of the reciprocal of a number. Reciprocals

More information

Forecasting. Chapter Copyright 2010 Pearson Education, Inc. Publishing as Prentice Hall

Forecasting. Chapter Copyright 2010 Pearson Education, Inc. Publishing as Prentice Hall Forecasting Chapter 15 15-1 Chapter Topics Forecasting Components Time Series Methods Forecast Accuracy Time Series Forecasting Using Excel Time Series Forecasting Using QM for Windows Regression Methods

More information

Volatility. Gerald P. Dwyer. February Clemson University

Volatility. Gerald P. Dwyer. February Clemson University Volatility Gerald P. Dwyer Clemson University February 2016 Outline 1 Volatility Characteristics of Time Series Heteroskedasticity Simpler Estimation Strategies Exponentially Weighted Moving Average Use

More information

NOWCASTING REPORT. Updated: May 5, 2017

NOWCASTING REPORT. Updated: May 5, 2017 NOWCASTING REPORT Updated: May 5, 217 The FRBNY Staff Nowcast stands at 1.8% for 217:Q2. News from this week s data releases reduced the nowcast for Q2 by percentage point. Negative surprises from the

More information

Lecture 15.2 :! Final Exam Review, Part 1

Lecture 15.2 :! Final Exam Review, Part 1 Lecture 15.2 :! Final Exam Review, Part 1 April 23, 2015 1 Announcements Online Evaluation e-mails should have been sent to you.! Please fill out the evaluation form...it is completely confidential. May

More information

The Dynamics of the U.S. Trade Balance and the Real Exchange Rate: The J Curve and Trade Costs? by George Alessandria (Rochester) Horag Choi (Monash)

The Dynamics of the U.S. Trade Balance and the Real Exchange Rate: The J Curve and Trade Costs? by George Alessandria (Rochester) Horag Choi (Monash) Discussion of The Dynamics of the U.S. Trade Balance and the Real Exchange Rate: The J Curve and Trade Costs? by George Alessandria (Rochester) Horag Choi (Monash) Brent Neiman University of Chicago and

More information

Lecture: Difference-in-Difference (DID)

Lecture: Difference-in-Difference (DID) Lecture: Difference-in-Difference (DID) 1 2 Motivation Q: How to show a new medicine is effective? Naive answer: Give the new medicine to some patients (treatment group), and see what happens This before-and-after

More information

Monetary shocks at high-frequency and their changing FX transmission around the globe

Monetary shocks at high-frequency and their changing FX transmission around the globe Introduction Data Baseline Results UMP Time Varying Robustness Conclusions Monetary shocks at high-frequency and their changing FX transmission around the globe Massimo Ferrari Jonathan Kearns Andreas

More information

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Exam: ECON3150/ECON4150 Introductory Econometrics Date of exam: Wednesday, May 15, 013 Grades are given: June 6, 013 Time for exam: :30 p.m. 5:30 p.m. The problem

More information

Wind prediction to support reduced aircraft wake vortex separation standards

Wind prediction to support reduced aircraft wake vortex separation standards Wind prediction to support reduced aircraft wake vortex separation standards Rodney Cole Weather Sensing Group WakeNet-2 Europe Dec 1, 2004 Wakenet-2 Europe-1 Outline Overview of closely spaced parallel

More information

NOWCASTING REPORT. Updated: April 15, 2016

NOWCASTING REPORT. Updated: April 15, 2016 NOWCASTING REPORT Updated: April 15, 2016 GDP growth prospects remain moderate for the rst half of the year: the nowcasts stand at 0.8% for 2016:Q1 and 1.2% for 2016:Q2. News from this week's data releases

More information

The Regression Tool. Yona Rubinstein. July Yona Rubinstein (LSE) The Regression Tool 07/16 1 / 35

The Regression Tool. Yona Rubinstein. July Yona Rubinstein (LSE) The Regression Tool 07/16 1 / 35 The Regression Tool Yona Rubinstein July 2016 Yona Rubinstein (LSE) The Regression Tool 07/16 1 / 35 Regressions Regression analysis is one of the most commonly used statistical techniques in social and

More information

Autoregressive models with distributed lags (ADL)

Autoregressive models with distributed lags (ADL) Autoregressive models with distributed lags (ADL) It often happens than including the lagged dependent variable in the model results in model which is better fitted and needs less parameters. It can be

More information

Week 3: Simple Linear Regression

Week 3: Simple Linear Regression Week 3: Simple Linear Regression Marcelo Coca Perraillon University of Colorado Anschutz Medical Campus Health Services Research Methods I HSMP 7607 2017 c 2017 PERRAILLON ALL RIGHTS RESERVED 1 Outline

More information

Statistics 910, #5 1. Regression Methods

Statistics 910, #5 1. Regression Methods Statistics 910, #5 1 Overview Regression Methods 1. Idea: effects of dependence 2. Examples of estimation (in R) 3. Review of regression 4. Comparisons and relative efficiencies Idea Decomposition Well-known

More information

Discussion by. Valerie A. Ramey

Discussion by. Valerie A. Ramey The Roles of Comovement and Inventory Investment in the Reduction of Output Volatility by Owen Irvine and Scott Schuh Discussion by Valerie A. Ramey Summary of Variance Decompositions of Output (Table

More information

Business Statistics 41000: Homework # 5

Business Statistics 41000: Homework # 5 Business Statistics 41000: Homework # 5 Drew Creal Due date: Beginning of class in week # 10 Remarks: These questions cover Lectures #7, 8, and 9. Question # 1. Condence intervals and plug-in predictive

More information

12A Reflection & Transmission (Normal Incidence)

12A Reflection & Transmission (Normal Incidence) 12A Reflection & Transmission (Normal Incidence) Topics: Reflection and transmission, boundary conditions, complex exponentials. Summary: Students begin by expressing in exponential notation the boundary

More information

Lab 6 - Simple Regression

Lab 6 - Simple Regression Lab 6 - Simple Regression Spring 2017 Contents 1 Thinking About Regression 2 2 Regression Output 3 3 Fitted Values 5 4 Residuals 6 5 Functional Forms 8 Updated from Stata tutorials provided by Prof. Cichello

More information

The Impact of Oil Expenses and Credit on the U.S. GDP.

The Impact of Oil Expenses and Credit on the U.S. GDP. The Impact of Oil Expenses and Credit on the U.S. GDP. Florent Mc Isaac 1 Agence Française de Développement (AFD); Université Paris 1 - Panthéon Sorbonne; Chair Energy and Prosperity Wednesday, 28th of

More information

NOWCASTING REPORT. Updated: May 20, 2016

NOWCASTING REPORT. Updated: May 20, 2016 NOWCASTING REPORT Updated: May 20, 2016 The FRBNY Staff Nowcast for GDP growth in 2016:Q2 is 1.7%, half a percentage point higher than last week. Positive news came from manufacturing and housing data

More information

WORKING PAPER NO DO GDP FORECASTS RESPOND EFFICIENTLY TO CHANGES IN INTEREST RATES?

WORKING PAPER NO DO GDP FORECASTS RESPOND EFFICIENTLY TO CHANGES IN INTEREST RATES? WORKING PAPER NO. 16-17 DO GDP FORECASTS RESPOND EFFICIENTLY TO CHANGES IN INTEREST RATES? Dean Croushore Professor of Economics and Rigsby Fellow, University of Richmond and Visiting Scholar, Federal

More information

Forecasting the term structure interest rate of government bond yields

Forecasting the term structure interest rate of government bond yields Forecasting the term structure interest rate of government bond yields Bachelor Thesis Econometrics & Operational Research Joost van Esch (419617) Erasmus School of Economics, Erasmus University Rotterdam

More information

Limits Involving Infinity (Horizontal and Vertical Asymptotes Revisited)

Limits Involving Infinity (Horizontal and Vertical Asymptotes Revisited) Limits Involving Infinity (Horizontal and Vertical Asymptotes Revisited) Limits as Approaches Infinity At times you ll need to know the behavior of a function or an epression as the inputs get increasingly

More information

Horizontal asymptotes

Horizontal asymptotes Roberto s Notes on Differential Calculus Chapter 1: Limits and continuity Section 5 Limits at infinity and Horizontal asymptotes What you need to know already: The concept, notation and terminology of

More information

Linear Modelling in Stata Session 6: Further Topics in Linear Modelling

Linear Modelling in Stata Session 6: Further Topics in Linear Modelling Linear Modelling in Stata Session 6: Further Topics in Linear Modelling Mark Lunt Arthritis Research UK Epidemiology Unit University of Manchester 14/11/2017 This Week Categorical Variables Categorical

More information

LECTURE 9: GENTLE INTRODUCTION TO

LECTURE 9: GENTLE INTRODUCTION TO LECTURE 9: GENTLE INTRODUCTION TO REGRESSION WITH TIME SERIES From random variables to random processes (cont d) 2 in cross-sectional regression, we were making inferences about the whole population based

More information

CS341 info session is on Thu 3/1 5pm in Gates415. CS246: Mining Massive Datasets Jure Leskovec, Stanford University

CS341 info session is on Thu 3/1 5pm in Gates415. CS246: Mining Massive Datasets Jure Leskovec, Stanford University CS341 info session is on Thu 3/1 5pm in Gates415 CS246: Mining Massive Datasets Jure Leskovec, Stanford University http://cs246.stanford.edu 2/28/18 Jure Leskovec, Stanford CS246: Mining Massive Datasets,

More information

14. Time- Series data visualization. Prof. Tulasi Prasad Sariki SCSE, VIT, Chennai

14. Time- Series data visualization. Prof. Tulasi Prasad Sariki SCSE, VIT, Chennai 14. Time- Series data visualization Prof. Tulasi Prasad Sariki SCSE, VIT, Chennai www.learnersdesk.weebly.com Overview What is forecasting Time series & its components Smooth a data series Moving average

More information

appstats27.notebook April 06, 2017

appstats27.notebook April 06, 2017 Chapter 27 Objective Students will conduct inference on regression and analyze data to write a conclusion. Inferences for Regression An Example: Body Fat and Waist Size pg 634 Our chapter example revolves

More information

Principal Component Analysis

Principal Component Analysis Principal Component Analysis 1 Principal Component Analysis Principal component analysis is a technique used to construct composite variable(s) such that composite variable(s) are weighted combination

More information

05 Regression with time lags: Autoregressive Distributed Lag Models. Andrius Buteikis,

05 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 information

Dynamics of Firms and Trade in General Equilibrium. Robert Dekle, Hyeok Jeong and Nobuhiro Kiyotaki USC, Seoul National University and Princeton

Dynamics of Firms and Trade in General Equilibrium. Robert Dekle, Hyeok Jeong and Nobuhiro Kiyotaki USC, Seoul National University and Princeton Dynamics of Firms and Trade in General Equilibrium Robert Dekle, Hyeok Jeong and Nobuhiro Kiyotaki USC, Seoul National University and Princeton Figure a. Aggregate exchange rate disconnect (levels) 28.5

More information

Computer Science, Informatik 4 Communication and Distributed Systems. Simulation. Discrete-Event System Simulation. Dr.

Computer Science, Informatik 4 Communication and Distributed Systems. Simulation. Discrete-Event System Simulation. Dr. Simulation Discrete-Event System Simulation Chapter 9 Verification and Validation of Simulation Models Purpose & Overview The goal of the validation process is: To produce a model that represents true

More information

CS246: Mining Massive Datasets Jure Leskovec, Stanford University

CS246: Mining Massive Datasets Jure Leskovec, Stanford University CS246: Mining Massive Datasets Jure Leskovec, Stanford University http://cs246.stanford.edu 2/26/2013 Jure Leskovec, Stanford CS246: Mining Massive Datasets, http://cs246.stanford.edu 2 More algorithms

More information

EC402 - Problem Set 3

EC402 - Problem Set 3 EC402 - Problem Set 3 Konrad Burchardi 11th of February 2009 Introduction Today we will - briefly talk about the Conditional Expectation Function and - lengthily talk about Fixed Effects: How do we calculate

More information

Warwick Business School Forecasting System. Summary. Ana Galvao, Anthony Garratt and James Mitchell November, 2014

Warwick Business School Forecasting System. Summary. Ana Galvao, Anthony Garratt and James Mitchell November, 2014 Warwick Business School Forecasting System Summary Ana Galvao, Anthony Garratt and James Mitchell November, 21 The main objective of the Warwick Business School Forecasting System is to provide competitive

More information

Productivity and Capital Allocation in Europe

Productivity and Capital Allocation in Europe Discussion of: Productivity and Capital Allocation in Europe by Gopinath, Kalemli-Ozcan, Karabarbounis, and Villegas-Sanchez Brent Neiman University of Chicago AEA Meetings 215 Static Misallocation (Quick

More information

Chapter 10 Verification and Validation of Simulation Models. Banks, Carson, Nelson & Nicol Discrete-Event System Simulation

Chapter 10 Verification and Validation of Simulation Models. Banks, Carson, Nelson & Nicol Discrete-Event System Simulation Chapter 10 Verification and Validation of Simulation Models Banks, Carson, Nelson & Nicol Discrete-Event System Simulation Purpose & Overview The goal of the validation process is: To produce a model that

More information

= (, ) V λ (1) λ λ ( + + ) P = [ ( ), (1)] ( ) ( ) = ( ) ( ) ( 0 ) ( 0 ) = ( 0 ) ( 0 ) 0 ( 0 ) ( ( 0 )) ( ( 0 )) = ( ( 0 )) ( ( 0 )) ( + ( 0 )) ( + ( 0 )) = ( + ( 0 )) ( ( 0 )) P V V V V V P V P V V V

More information

Identifying the Monetary Policy Shock Christiano et al. (1999)

Identifying the Monetary Policy Shock Christiano et al. (1999) Identifying the Monetary Policy Shock Christiano et al. (1999) The question we are asking is: What are the consequences of a monetary policy shock a shock which is purely related to monetary conditions

More information

Sections 1.1 and 1.2. Speaking the language Science and the scientific process Forces Matter. Sections 1.3 and 1.4. Representations of matter Scale

Sections 1.1 and 1.2. Speaking the language Science and the scientific process Forces Matter. Sections 1.3 and 1.4. Representations of matter Scale Introduction Chapter 1 The Study of Chemistry (1.1) The Scientific Method (1.2) Classifications of Matter (1.3) Physical and Chemical Properties of Matter (1.4) Measurement (1.5) Handling Numbers (1.6)

More information

How News and Its Context Drive Risk and Returns Around the World

How News and Its Context Drive Risk and Returns Around the World How News and Its Context Drive Risk and Returns Around the World Charles Calomiris and Harry Mamaysky Columbia Business School Q Group Spring 2018 Meeting Outline of talk Introduction Data and text measures

More information

PhD/MA Econometrics Examination January 2012 PART A

PhD/MA Econometrics Examination January 2012 PART A PhD/MA Econometrics Examination January 2012 PART A ANSWER ANY TWO QUESTIONS IN THIS SECTION NOTE: (1) The indicator function has the properties: (2) Question 1 Let, [defined as if using the indicator

More information

Regression. ECO 312 Fall 2013 Chris Sims. January 12, 2014

Regression. ECO 312 Fall 2013 Chris Sims. January 12, 2014 ECO 312 Fall 2013 Chris Sims Regression January 12, 2014 c 2014 by Christopher A. Sims. This document is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License What

More information

Mgmt 469. Causality and Identification

Mgmt 469. Causality and Identification Mgmt 469 Causality and Identification As you have learned by now, a key issue in empirical research is identifying the direction of causality in the relationship between two variables. This problem often

More information

NOWCASTING REPORT. Updated: September 23, 2016

NOWCASTING REPORT. Updated: September 23, 2016 NOWCASTING REPORT Updated: September 23, 216 The FRBNY Staff Nowcast stands at 2.3% and 1.2% for 216:Q3 and 216:Q4, respectively. Negative news since the report was last published two weeks ago pushed

More information

Physics 8 Wednesday, November 18, 2015

Physics 8 Wednesday, November 18, 2015 Physics 8 Wednesday, November 18, 2015 Remember HW10 due Friday. For this week s homework help, I will show up Wednesday, DRL 2C4, 4pm-6pm (when/where you normally find Camilla), and Camilla will show

More information

Time Series Analysis. Smoothing Time Series. 2) assessment of/accounting for seasonality. 3) assessment of/exploiting "serial correlation"

Time Series Analysis. Smoothing Time Series. 2) assessment of/accounting for seasonality. 3) assessment of/exploiting serial correlation Time Series Analysis 2) assessment of/accounting for seasonality This (not surprisingly) concerns the analysis of data collected over time... weekly values, monthly values, quarterly values, yearly values,

More information

Next week Professor Saez will discuss. This week John and I will

Next week Professor Saez will discuss. This week John and I will Next Week's Topic Income Inequality and Tax Policy Next week Professor Saez will discuss Atkinson, Anthony, Thomas Piketty and Emmanuel Saez, Top Incomes in the Long Run of History, Alvaredo, Facundo,

More information

14.32 Final : Spring 2001

14.32 Final : Spring 2001 14.32 Final : Spring 2001 Please read the entire exam before you begin. You have 3 hours. No books or notes should be used. Calculators are allowed. There are 105 points. Good luck! A. True/False/Sometimes

More information

Autocorrelation. Think of autocorrelation as signifying a systematic relationship between the residuals measured at different points in time

Autocorrelation. Think of autocorrelation as signifying a systematic relationship between the residuals measured at different points in time Autocorrelation Given the model Y t = b 0 + b 1 X t + u t Think of autocorrelation as signifying a systematic relationship between the residuals measured at different points in time This could be caused

More information

S ince 1980, there has been a substantial

S ince 1980, there has been a substantial FOMC Forecasts: Is All the Information in the Central Tendency? William T. Gavin S ince 1980, there has been a substantial improvement in the performance of monetary policy among most of the industrialized

More information

Forecasting. Dr. Richard Jerz rjerz.com

Forecasting. Dr. Richard Jerz rjerz.com Forecasting Dr. Richard Jerz 1 1 Learning Objectives Describe why forecasts are used and list the elements of a good forecast. Outline the steps in the forecasting process. Describe at least three qualitative

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

M I C R O C O N V E R S I O N G O A L S

M I C R O C O N V E R S I O N G O A L S C O N T A C T U S T h i s p a g e l i s t s a l l o f t h e d i f f e r e n t d e p a r t m e n t s t o c o n t a c t, d e p e n d i n g o n y o u r r e q u e s t. A t t h e b o t t o m o f t h e p a g

More information

Interest Rate Determination & the Taylor Rule JARED BERRY & JAIME MARQUEZ JOHNS HOPKINS SCHOOL OF ADVANCED INTERNATIONAL STUDIES JANURY 2017

Interest Rate Determination & the Taylor Rule JARED BERRY & JAIME MARQUEZ JOHNS HOPKINS SCHOOL OF ADVANCED INTERNATIONAL STUDIES JANURY 2017 Interest Rate Determination & the Taylor Rule JARED BERRY & JAIME MARQUEZ JOHNS HOPKINS SCHOOL OF ADVANCED INTERNATIONAL STUDIES JANURY 2017 Monetary Policy Rules Policy rules form part of the modern approach

More information

Lecture#17. Time series III

Lecture#17. Time series III Lecture#17 Time series III 1 Dynamic causal effects Think of macroeconomic data. Difficult to think of an RCT. Substitute: different treatments to the same (observation unit) at different points in time.

More information

STA Module 10 Comparing Two Proportions

STA Module 10 Comparing Two Proportions STA 2023 Module 10 Comparing Two Proportions Learning Objectives Upon completing this module, you should be able to: 1. Perform large-sample inferences (hypothesis test and confidence intervals) to compare

More information

Lecture: Simultaneous Equation Model (Wooldridge s Book Chapter 16)

Lecture: Simultaneous Equation Model (Wooldridge s Book Chapter 16) Lecture: Simultaneous Equation Model (Wooldridge s Book Chapter 16) 1 2 Model Consider a system of two regressions y 1 = β 1 y 2 + u 1 (1) y 2 = β 2 y 1 + u 2 (2) This is a simultaneous equation model

More information

Financial Econometrics

Financial 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 information

Estimating and Accounting for the Output Gap with Large Bayesian Vector Autoregressions

Estimating and Accounting for the Output Gap with Large Bayesian Vector Autoregressions Estimating and Accounting for the Output Gap with Large Bayesian Vector Autoregressions James Morley 1 Benjamin Wong 2 1 University of Sydney 2 Reserve Bank of New Zealand The view do not necessarily represent

More information

57:022 Principles of Design II Midterm Exam #2 Solutions

57:022 Principles of Design II Midterm Exam #2 Solutions 57:022 Principles of Design II Midterm Exam #2 Solutions Part: I II III IV V Total Possible Pts: 20 15 12 16 12 75 PART ONE Indicate "+" if True and "O" if False: _+_a. If a component's lifetime has exponential

More information

NOWCASTING REPORT. Updated: October 21, 2016

NOWCASTING REPORT. Updated: October 21, 2016 NOWCASTING REPORT Updated: October 21, 216 The FRBNY Staff Nowcast stands at 2.2% for 216:Q3 and 1.4% for 216:Q4. Overall this week s news had a negative effect on the nowcast. The most notable developments

More information

Quarterly Journal of Economics and Modelling Shahid Beheshti University * ** ( )

Quarterly Journal of Economics and Modelling Shahid Beheshti University * **  ( ) 392 Quarterly Journal of Economics and Modelling Shahid Beheshti University * ** 93/2/20 93//25 m_noferesti@sbuacir mahboubehbaiat@gmailcom ( ) * ** 392 5 4 2 E27 C53 C22 JEL - 3 (989) 3 (2006) 2 (2004)

More information

Discussion of Does a Big Bazooka Matter? Central Bank Balance-Sheet Policies and Exchange Rates

Discussion of Does a Big Bazooka Matter? Central Bank Balance-Sheet Policies and Exchange Rates Discussion of Does a Big Bazooka Matter? Central Bank Balance-Sheet Policies and Exchange Rates by L. Dedola, G. Georgiadis, J. Grab, A. Mehl Ambrogio Cesa-Bianchi (BoE and CfM) ESSIM May 26, 2017 *The

More information

10. Time series regression and forecasting

10. 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 information

PHYS 3220 Tutorials S. Goldhaber, S. Pollock, and the Physics Education Group University of Colorado, Boulder

PHYS 3220 Tutorials S. Goldhaber, S. Pollock, and the Physics Education Group University of Colorado, Boulder Energy and the Art of Sketching Wave Functions 1 I: Sketching wave functions A. Review: The figure to the right shows an infinite square well potential (V = 0 from L/2 to L/2 and is infinite everywhere

More information

Lecture 10. Econ August 21

Lecture 10. Econ August 21 Lecture 10 Econ 2001 2015 August 21 Lecture 10 Outline 1 Derivatives and Partial Derivatives 2 Differentiability 3 Tangents to Level Sets Calculus! This material will not be included in today s exam. Announcement:

More information

Empirical Application of Panel Data Regression

Empirical Application of Panel Data Regression Empirical Application of Panel Data Regression 1. We use Fatality data, and we are interested in whether rising beer tax rate can help lower traffic death. So the dependent variable is traffic death, while

More information

Independent and conditionally independent counterfactual distributions

Independent and conditionally independent counterfactual distributions Independent and conditionally independent counterfactual distributions Marcin Wolski European Investment Bank M.Wolski@eib.org Society for Nonlinear Dynamics and Econometrics Tokyo March 19, 2018 Views

More information

Controlling for Time Invariant Heterogeneity

Controlling for Time Invariant Heterogeneity Controlling for Time Invariant Heterogeneity Yona Rubinstein July 2016 Yona Rubinstein (LSE) Controlling for Time Invariant Heterogeneity 07/16 1 / 19 Observables and Unobservables Confounding Factors

More information

Business Statistics. Lecture 5: Confidence Intervals

Business Statistics. Lecture 5: Confidence Intervals Business Statistics Lecture 5: Confidence Intervals Goals for this Lecture Confidence intervals The t distribution 2 Welcome to Interval Estimation! Moments Mean 815.0340 Std Dev 0.8923 Std Error Mean

More information

THE APPLICATION OF GREY SYSTEM THEORY TO EXCHANGE RATE PREDICTION IN THE POST-CRISIS ERA

THE APPLICATION OF GREY SYSTEM THEORY TO EXCHANGE RATE PREDICTION IN THE POST-CRISIS ERA International Journal of Innovative Management, Information & Production ISME Internationalc20 ISSN 285-5439 Volume 2, Number 2, December 20 PP. 83-89 THE APPLICATION OF GREY SYSTEM THEORY TO EXCHANGE

More information

NOWCASTING REPORT. Updated: July 20, 2018

NOWCASTING REPORT. Updated: July 20, 2018 NOWCASTING REPORT Updated: July 20, 2018 The New York Fed Staff Nowcast stands at 2.7% for 2018:Q2 and 2.4% for 2018:Q3. News from this week s data releases decreased the nowcast for 2018:Q2 by 0.1 percentage

More information

Applied Statistics and Econometrics. Giuseppe Ragusa Lecture 15: Instrumental Variables

Applied Statistics and Econometrics. Giuseppe Ragusa Lecture 15: Instrumental Variables Applied Statistics and Econometrics Giuseppe Ragusa Lecture 15: Instrumental Variables Outline Introduction Endogeneity and Exogeneity Valid Instruments TSLS Testing Validity 2 Instrumental Variables Regression

More information

NCERT solution for Integers-2

NCERT solution for Integers-2 NCERT solution for Integers-2 1 Exercise 6.2 Question 1 Using the number line write the integer which is: (a) 3 more than 5 (b) 5 more than 5 (c) 6 less than 2 (d) 3 less than 2 More means moving right

More information

Solving with Absolute Value

Solving with Absolute Value Solving with Absolute Value Who knew two little lines could cause so much trouble? Ask someone to solve the equation 3x 2 = 7 and they ll say No problem! Add just two little lines, and ask them to solve

More information

Simultaneous Equation Models Learning Objectives Introduction Introduction (2) Introduction (3) Solving the Model structural equations

Simultaneous Equation Models Learning Objectives Introduction Introduction (2) Introduction (3) Solving the Model structural equations Simultaneous Equation Models. Introduction: basic definitions 2. Consequences of ignoring simultaneity 3. The identification problem 4. Estimation of simultaneous equation models 5. Example: IS LM model

More information

Statistical Inference with Regression Analysis

Statistical Inference with Regression Analysis Introductory Applied Econometrics EEP/IAS 118 Spring 2015 Steven Buck Lecture #13 Statistical Inference with Regression Analysis Next we turn to calculating confidence intervals and hypothesis testing

More information

Constructing and solving linear equations

Constructing and solving linear equations Key Stage 3 National Strategy Guidance Curriculum and Standards Interacting with mathematics in Key Stage 3 Constructing and solving linear equations Teachers of mathematics Status: Recommended Date of

More information

Econometrics. 9) Heteroscedasticity and autocorrelation

Econometrics. 9) Heteroscedasticity and autocorrelation 30C00200 Econometrics 9) Heteroscedasticity and autocorrelation Timo Kuosmanen Professor, Ph.D. http://nomepre.net/index.php/timokuosmanen Today s topics Heteroscedasticity Possible causes Testing for

More information

What Fun! It's Practice with Scientific Notation!

What Fun! It's Practice with Scientific Notation! What Fun! It's Practice with Scientific Notation! Review of Scientific Notation Scientific notation provides a place to hold the zeroes that come after a whole number or before a fraction. The number 100,000,000

More information