Kenji Sato

2016-12-07

- Name:
**Kenji Sato**- PLEASE DO NOT call me “Professor!”
- “Kenji” or “Sato-
*san*” are preferable. “Sato-*sensei*” is acceptable.

- Fields of Interest:
- Macroeconomic Dynamics
- Economics of Innovation
- Comparative Statics/Dynamics

- For course mechanics, take a look at the handout.

- Office hours
- Textbook
- Schedule
- Grading
- Course websites

- Macroeconomics is a study of such aggregate economic evariables as …
- Gross Domestic Product (GDP)
- Growth Rate
- Business Cycles
- Unemployment Rate
- Inflation Rate
- Income Inequality

- In this course, you are going to study …
- basic machinery of graduate-level macroeconomics;
- how to code computer programs to compare theory with data;
**theories**on how GDP and growth rates move.

- To learn how to solve growth models, you will need to learn
- differential equations and difference equations, and
- dynamic optimization.

- Because of the limited time, this course will focus on a study of economic growth theory.
- The
**foundation**of modern macroeconomic research. - If we have time, we will move on to the study of innovation or business cycles.
- No money, unemployment or inequality.

- The

- You are
**highly recommended**to take more advanced/applied courses together with this course.**Inequality and Macroeconomics**by Professor Dr. Reto FÃ¶llmi, from January 26 to 31.- Please go to Kyoumu for enrollment!

After taking Watkins's courses, I believe you are familiar with R programing language.

- I will also use R when I show you data.
- Highly recommended to type yourself to reproduce plots.
- Building muscle memory is a necessary part of climbing up the learning curve.

- Sometimes I will use Python for performing simulations.
- Because I like Python.

- Feenstra, Inklaar and Timmer (2015) “The next
generation of the Penn World Table,”
*American Economic Review*105(10). 3150–82.

```
library(readstata13)
library(dplyr)
library(ggplot2)
pwt8 = read.dta13("pwt90.dta")
```

You can easily analyse the data with R + dplyr.

```
pwt8 %>%
filter(country == "Japan", year > 2010) %>%
mutate(rgdp_per_capita = rgdpo / pop) %>%
select(year, rgdp_per_capita)
```

```
year rgdp_per_capita
1 2011 34979.34
2 2012 35191.24
3 2013 35476.46
4 2014 35566.22
```

Unit: 2011US$

Note: With R it is difficult to see the descriptive labels that STATA variables have. Install gretl to work around.

We are interested in evolution of real GDP per person (per capita). Becase it is a nice statistic to measure the standard of living for a country.

Let's compare those of three countries since 1980.

```
rgdp <- pwt8 %>%
filter(countrycode %in% c("JPN","USA", "KOR"),
year > 1980) %>%
mutate(rgdp_pc = rgdpo / pop) %>%
select(country, year, rgdp_pc) %>%
as.data.frame
```

```
ggplot(rgdp, aes(x=year, y=rgdp_pc, color=country)) +
geom_point() + geom_line(aes(group=country))
```

When studying the growth rates, semi-log scale is helpful.

```
rgdp %>% ggplot(aes(x=year, y=rgdp_pc, color=country)) + geom_point() +
scale_y_log10() + # Plot in semi-log scale
geom_smooth(method = "lm", se = FALSE)
```

\[ \begin{aligned} & \text{Gross annual growth rate from 2000 to 2010} \\ &= \left(\frac{GDP_{2010}}{GDP_{2000}}\right)^{1/10} \end{aligned} \]

And so, ….

\[ \begin{aligned} & \log (\text{Gross annual growth rate from 2000 to 2010}) \\ &= \frac{\log GDP_{2010} - \log GDP_{2000}}{2010 - 2000} \end{aligned} \]

The slopes of the lines you observed in the semi-log plot correspond to the growth rates of the countries.

The growth rates change over time.

```
rgdp %>% ggplot(aes(x=year, y=rgdp_pc, color=country)) + geom_point() +
scale_y_log10() + # Plot in semi-log scale
geom_smooth(method="lm", se=FALSE) +
geom_smooth(data=filter(rgdp, year > 1995),
method="lm", se=FALSE)
```