Class 1 - uni-frankfurt.de · Class 1 Macroeconomics 2 Prof. Michael Haliassos. Cristian Badarinza. Goethe University, Frankfurt am Main. Wintersemester 2010/2011. October 19, 2010

Class 1

Macroeconomics 2

Prof. Michael HaliassosCristian Badarinza

Goethe University, Frankfurt am Main

Wintersemester 2010/2011

October 19, 2010

Introduction

• Contact: [email protected]

• Office hours: Monday, 16:00-18:00, HoF Room 363

• Our plan for the semester:– Class:

• Summary of the Lecture: clarification questions

• Problems and further applications

– Mentorium:• Solutions to Exercises

• Online material: check updates on the course website

October 19, 2010 2Class 1

DataConsumer

Firms

ECB

Modern macroeconomics

Why did macroeconomists fail to

forecast the crisis?

October 19, 2010 Class 1 3

A question you may have asked yourselves

Ignorance

True fundamental uncertainty

Uncertainty

Is macroeconomics really a science?

Yes

Empiricalanalysis

Theoreticalmodeling

Implications forgovernment and monetary policy

grossly underestimated byfinancial institutions

but: we learn from historyECB systemic risk board

can be reduced bybetter understandingthe incentives of agents

badarinz

Text Box

Modern macroeconomics


Ignorance

True fundamental uncertainty

Uncertainty

subject of this course

• Three elements– data analysis

– macro modeling

– policy implications

• Characteristics– formal mathematical treatment

– focus on general methods and principles

Summary of the Lecture

• trend and cyclical components

• stabilization policies

• business cycles– aggregate economic activity

– organization in business enterprises

– expansions and contractions

– duration of more than one year

– recurrent but not periodic

• dating and measurement

• volatility, correlation and persistence


Questions about the Lecture?

Problem 1

• GDP (real, trend, cycle)

• Private consumption

• Public (government) consumption

• Public (government) investment

• Exports, imports and the current account balance

• Employment

• Wages and household savings

• Inflation

• Short-term interest rates, long-term interest rates

• etc.


Statistical aggregated measures characterizing the business cycle

Logarithms


Logarithms


Volatility


Measurement

Volatility


Examples

Correlation


Measurement

Correlation


Examples

Problem 1


Volatility and persistence: the rate of inflation

-3

-2

-1

0

1

2

3

4

5

6

91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

United States inflation rate Euro area inflation rate

low volatility and high persistencevery low volatility in Euro area

high volatility and low persistence

-16

-12

-8

-4

0

4

8

12

91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

United States industrial production growthEuro area industrial production growth

Problem 1


Volatility and persistence: the rate of industrial production growth

high volatility and high persistencevery low volatility

very high volatility

0.0

0.4

0.8

1.2

1.6

2.0

91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

DAX Index DJ Euro Stoxx Index

Problem 1


Volatility and persistence: stock market indices

low volatility

high volatility

high persistence

Problem 1


-.04

.00

.04

.08

.12

.16

2

4

6

8

10

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

log GDP growth Unemployment rate

Co-movement in macroeconomic aggregates

Problem 1


-.05

.00

.05

.10

.15

2

4

6

8

10

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

log wages growth Unemployment rate


Problem 1


-4

0

4

8

12

16

0

4

8

12

16

20

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

Inflation Short term interest rate


Problem 1


-1.50E+12

-1.00E+12

-5.00E+11

0.00E+00

5.00E+11

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

Household net savings Government net savings


Problem 2

• decomposition of log GDP:

• the HP Filter:

• limiting cases


[ ]∑ ∑= =

−+ −−−+−←T

t

T

ttttttt

HPt gggggyg

1 1

211 )()()(min λ

ttt cgy +=

Hodrick-Prescott filter

Problem 2



-1.0

-0.5

0.0

0.5

1.0

4

6

8

10

12

14

16

93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

Variable Trend Cycle

lambda=0

Problem 2



-.4

-.2

.0

.2

.4

4

6

8

10

12

14

16

93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10


lambda=1

Problem 2



-.6

-.4

-.2

.0

.2

.4

.6

4

6

8

10

12

14

16

93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10


lambda=10

Problem 2



-1.2

-0.8

-0.4

0.0

0.4

0.8

4

6

8

10

12

14

16

93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10


lambda=500

Problem 2



-1.5

-1.0

-0.5

0.0

0.5

1.0

4

6

8

10

12

14

16

93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10


lambda=2000

Problem 2



-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

4

6

8

10

12

14

16

93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10


lambda=50000

Problem 2



-3

-2

-1

0

1

2

3

4

6

8

10

12

14

16

93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10


lambda=10000000

Problem 2


Hodrick-Prescott filter: actual data

Problem 2



Problem 2



Problem 2



• Drawbacks:– imprecise estimates at the end points of the time series

– arbitrary choice of lambda

– cannot capture structural breaks

• Further questions:– what is the trend component really?

• something like a permanent, long-run component, a potential value

– can we get a better understanding of it?

Problem 3

• why is it important to have proper identification of potential output?

– central bank• if output is below potential, decrease interest rates

• if output is above potential, increase interest rates, to avoid overheating

• very intense discussion around year 2000: did potential output increase?

– government• important to know whether output movements are due to cyclical or trend components

• cyclical movements short-run tax or industry policy

• trend movements long-run investment policy


Estimating potential output: the production function method

Problem 3



Assume an aggregate production function for the economy:

B = total factor productivityK = aggregate capital stockL = aggregate number of hours worked

Problem 3



Working hours are given by:

u = unemployment rateN = total labor forceH = average number of working hours per person employed

Problem 3

A short note:– don‘t let yourself get confused by equations like the ones on the two

previous slides

– both equations are assumed to look like they do, they are not derivedand there is no true justification (at least not yet) for choosing themlike we did

– this means you are not supposed to justify why and when and who, just take them as given and work out the results which they imply

– very important: don‘t confuse assumptions with derived results

(more on this also later during the course)



Problem 3



Replace the equation for labor in the production function:

Take logs on both sides:

Problem 3



Now let bars denote long-run trend levels:

Again take logs on both sides:

potential output long-run unemployment rate

potential factor productivity

Problem 3



Subtracting the two equations in logs, we obtain:

Now, data needed on:• total factor productivity• working hours• unemployment• labor force

But: is there any data on productivity ? Not really.

Problem 3



Solution: estimate productivity from the production function directly:

Cookbook recipe:

1. collect data for GDP, capital and labor variables

2. estimate productivity

3. estimate cyclical components of all variables by HP filter

4. calculate output gap


Problem 3Estimating potential output: the production function method

The contribution of the unemployment rate



The contribution of aggregate hours worked



The contribution of movements in the labor force

Documents

Class 1 - uni-frankfurt.de · Class 1 Macroeconomics 2 Prof. Michael Haliassos. Cristian Badarinza. Goethe University, Frankfurt am Main. Wintersemester 2010/2011. October 19, 2010