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Lecture_24_Prof._Arkonac's_Slides_(Ch_15.4-16)_Fall_10

# Lecture_24_Prof._Arkonac's_Slides_(Ch_15.4-16)_Fall_10 -...

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Time Series Regression IV 1 Lecture 24 Seyhan Erden Arkonac, PhD Please take 2 minutes to complete teacher evaluation on Courseworks (deadline is Dec 15 th)

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2 The Orange Juice Data (reminder) Data Monthly, Jan. 1950 – Dec. 2000 ( T = 612) Price = price of frozen OJ (a sub-component of the producer price index; US Bureau of Labor Statistics) %ChgP = percentage change in price at an annual rate, so %ChgP t = 1200 ln( Price t ) FDD = number of freezing degree-days during the month, recorded in Orlando FL Example: If November has 2 days with lows < 32 o , one at 30 o and at 25 o , then FDD Nov = (32-30)+(32-25)=2 + 7 = 9
3 Example : OJ and HAC estimators in STATA, ctd. . global lfdd6 "fdd l1fdd l2fdd l3fdd l4fdd l5fdd l6fdd"; . newey dlpoj \$lfdd6 if tin(1950m1,2000m12), lag(7); Regression with Newey-West standard errors Number of obs = 612 maximum lag : 7 F( 7, 604) = 3.56 Prob > F = 0.0009 ------------------------------------------------------------------------------ | Newey-West dlpoj | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- fdd | .4693121 .1359686 3.45 0.001 .2022834 .7363407 l1fdd | .1430512 .0837047 1.71 0.088 -.0213364 .3074388 l2fdd | .0564234 .0561724 1.00 0.316 -.0538936 .1667404 l3fdd | .0722595 .0468776 1.54 0.124 -.0198033 .1643223 l4fdd | .0343244 .0295141 1.16 0.245 -.0236383 .0922871 l5fdd | .0468222 .0308791 1.52 0.130 -.0138212 .1074657 l6fdd | .0481115 .0446404 1.08 0.282 -.0395577 .1357807 _cons | -.6505183 .2336986 -2.78 0.006 -1.109479 -.1915578 ------------------------------------------------------------------------------ global lfdd6 defines a string which is all the additional lags What are the estimated dynamic multipliers (dynamic effects)?

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Why use 7 lags? There are 612 monthly observations the rule is m=0.75 (612) 1/3 =6.37 round this up to 7. What are the estimated dynamic multipliers (dynamic effects)? A single freezing degree day is estimated to increase the prices by 0.47% over the month in which the freezing degree day occurs (impact effect) One-month multiplier is 0.14% (one-period dynamic multiplier) Cumulative dynamic (long-run) multiplier is .47+.14+.06+.07+.03+.05+.05 = 0.87 4
not even in the weather/OJ example (why?) OJ market participants use forecast of FDD when they decide how much to produce or sell at a given price; this will cause OJ prices and thus the error term u t to carry information about future FDD; that would make u t a good predictor of FDD. This means u t will be correlated with future values of FDD t . Because u t includes forecasts of future Florida weather, FDD would be exogenous (past and present) but not strictly exogenous (past, present and future) In the tomato example X is fertilization not FDD, not weather that can be forecasted, future fertilization does not affect today’s tomato yield. 5

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Lecture_24_Prof._Arkonac's_Slides_(Ch_15.4-16)_Fall_10 -...

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