Ch14TimeSeriesPart1

# Ch14TimeSeriesPart1 - Introduction to Time Series...

This preview shows pages 1–4. Sign up to view the full content.

1 Introduction to Time Series Regression and Forecasting (SW Chapter 14) Time series data are data collected on the same observational unit at multiple time periods Aggregate consumption and GDP for a country (for example, 20 years of quarterly observations = 80 observations) Yen/\$, pound/\$ and Euro/\$ exchange rates (daily data for 1 year = 365 observations) Cigarette consumption per capita in a state, by year 2 Example #1 of time series data: US rate of price inflation, as measured by the quarterly percentage change in the Consumer Price Index (CPI), at an annual rate 3 Example #2: US rate of unemployment 4 Example #3: Phillips Curve ? -4 0 4 8 12 16 60 65 70 75 80 85 90 95 00 05 UNEMPLOYMENT INFLATION

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
5 Why use time series data? To develop forecasting models What will the rate of inflation be next year? To estimate dynamic causal effects If the Fed increases the Federal Funds rate now, what will be the effect on the rates of inflation and unemployment in 3 months? in 12 months? What is the effect over time on cigarette consumption of a hike in the cigarette tax? Or, because that is your only option … Rates of inflation and unemployment in the US can be observed only over time! 6 Time series data raises new technical issues Time lags Correlation over time ( serial correlation , a.k.a. autocorrelation ) Forecasting models built on regression methods: autoregressive (AR) models autoregressive distributed lag (ADL) models need not (typically do not) have a causal interpretation Conditions under which dynamic effects can be estimated, and how to estimate them Calculation of standard errors when the errors are serially correlated 7 Using Regression Models for Forecasting (SW Section 14.1) Forecasting and estimation of causal effects are quite different objectives. For forecasting, 2 R matters (a lot!) Omitted variable bias isn’t a problem as long as these omitted variables don’t change ! We will not worry about interpreting coefficients in forecasting models External validity is paramount: the model estimated using historical data must hold into the (near) future 8 Introduction to Time Series Data and Serial Correlation (SW Section 14.2) First, some notation and terminology. Notation for time series data Y t = value of Y in period t . Data set: Y 1 ,…, Y T = T observations on the time series random variable Y We consider only consecutive, evenly-spaced observations (for example, monthly, 1960 to 1999, no missing months) (missing and non-evenly spaced data introduce technical complications)
9 We will transform time series variables using lags, first differences, logarithms, & growth rates 10 Example : Quarterly rate of inflation at an annual rate (U.S.) CPI = Consumer Price Index (Bureau of Labor Statistics) CPI in the first quarter of 2004 (2004:I) = 186.57 CPI in the second quarter of 2004 (2004:II) = 188.60 Percentage change in CPI, 2004:I to 2004:II = 188.60 186.57 100 186.57 ⎛⎞ × ⎜⎟ ⎝⎠ = 2.03 100 186.57 × = 1.088% Percentage change in CPI, 2004:I to 2004:II, at an annual rate = 4 × 1.088 = 4.359 % 4.4 % (percent per year)

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

## This note was uploaded on 11/28/2010 for the course ECON Economics taught by Professor Davidbrownstone during the Spring '10 term at UC Irvine.

### Page1 / 16

Ch14TimeSeriesPart1 - Introduction to Time Series...

This preview shows document pages 1 - 4. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online