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### Midterm 2001

Course: ECON 240, Fall 2009
School: UCSB
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Answer 5-8-2001 all questions. 1. (15 min) Economics 240C Midterm 1 Mr. Phillips a. For a random walk, rw(t), without drift, rw(t) = rw(t-1) + wn(t), where wn(t) is white noise, what is the forecast at time t-1 of this random walk at time t, i.e. Et-1 rw(t) = ? _______________________ b. What is the variance of the forecast error? _______________ c. What is the forecast at time t-1 of this random walk at time...

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Answer 5-8-2001 all questions. 1. (15 min) Economics 240C Midterm 1 Mr. Phillips a. For a random walk, rw(t), without drift, rw(t) = rw(t-1) + wn(t), where wn(t) is white noise, what is the forecast at time t-1 of this random walk at time t, i.e. Et-1 rw(t) = ? _______________________ b. What is the variance of the forecast error? _______________ c. What is the forecast at time t-1 of this random walk at time t + 1, i.e. Et-1 rw(t + 1) = ? ________ d. What is the variance of the forecast error? _______________ e. Does this have any practical applications to markets? Explain. ___________________________ _______________________________________________________________________________ 2. (15 min) a. Finish expressing, i.e. expand or write out in full, this general mod...
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UCSB - ECON - 240
Economics 240C Answers To Study Questions #2Due 501021. (a) For x(t) = 0.5 x(t1) + e(t), multiplying by x(t1)
UCSB - ECON - 240
5-23-2002Econ 240C Takehome Project TwoDue: 6-12-2002The U S Treasury Yield curve plots the interest rate of a security against its term or maturity. For example, the current interest rate on a 3-month Treasury Bill is 1.69% and the current int
UCSB - ECON - 240
Econ 240C I. PoolingLAB One14-3-2002A. Open data file XM 14-02 in Excel 1. Note there are 200 rows of data, one for each teenager, with the columns labeled for day of the week from Sunday through Saturday. 2. Go to the Tools menu, Data Analys
UCSB - ECON - 240
4-10-2002Lab Two1I. &quot; Demonstration&quot; from Eviews (handout) II. White Noise A. Generate a white noise series of 1000 observations. 1. In Eviews, open a new workfile of 1000 undated observations. 2. Generate wn = nrnd B. Characteristics of White
UCSB - ECON - 240
Spring 2002Lab Nine 1Econ240CI. An Example: The Producer Price Index, 1982=100, seasonally adjusted, for finished goods. A. The Producer Price Index Open EViews File Menu/Open: ppi2000.wf1( in the Econ 240C Folder) available at FRED Workfile Wi
UCSB - ECON - 240
4-24-2002Lab Four1I. Simulation of a Moving Average Process of Order Three, MA(3) Open Eviews Object Menu: New Workfile Object: &quot;Moving Average&quot; Frequency: undated observations: 1 1000 Workfile Menu: GENR &quot;WN=NRND&quot; Workfile Menu: GENR &quot;MATHREE
UCSB - ECON - 240
Lecture Eleven 1I. Dynamic Causal Models592002Our discussion of dynamic relationships between two(or more) variables will be conducted within the conceptual and historical framework of permanent consumption. The idea is that permanent consum
UCSB - ECON - 240
5-21-2002Econ 240C Lecture 141I. Intervention Models There is often a qualitative change in events which can have an effect on quantitative measures. For example, price may increase sharply at a point in time, affecting demand. An example of su
UCSB - ECON - 240
ECON 240CLecture 1515-23-2002I. Accidents, Disasters, Loss in Wealth and the Impact on Consumer Demand A series of crashes, considered by some observers as a string of bad luck, afflicted US Air, with perhaps the worst occurring on approach t
UCSB - ECON - 240
492002 Lecture Three 1I. Time Averages of Stationary Stochastic Processes Recall the generation of ten observations from the uniform distribution that we discussed in Lecture Two. If we index these observations by time we have a strictly stat
UCSB - ECON - 240
4112002 I. Random WalkLecture Four 1A random walk, RW(t), is the sum of the current and past observations of a white noise process, e(t) which we can assume for convenience has mean zero and variance one: RW(t) = e(t) + e(t1) + e(t2) + e(t3) +
UCSB - ECON - 240
4182002Lecture Six 1I. Moving Average Processes of Order One A moving average process of order one, MA(1), has the following structure: x(t) = e(t) + a e(t1), where e(t) is white noise. If a is zero then we have white noise or a moving average
UCSB - ECON - 240
4232002Lecture Seven 1I. Summary: Autoregressive and Moving Average Processes to Date A. Definition 1. AR(1) : x(t) = b1x(t1) + e(t) 2. AR(2) : x(t) = b1x(t1) + b2x(t2) + e(t) 3. AR(p) : x(t) = bixi(ti) + e(t) 4. MA(1) : x(t) = e(t) + a1e(t1) 5
UCSB - ECON - 240
4172002ECON 240CProblem Set One Due 42420021. A finite sample of a time series that is a random walk, or close to a random walk, for example the price of gold, has a finite and calculable variance. Try calculating it for a sample from a ran
UCSB - ECON - 240
4242002Economics 240C:1Problem Set #2 Due 5120021. For the time series, x(t) = 0.5 x(t1) + e(t), a. Plot the autocorrelation function at lags 0,1,2,3,4, &amp; 5. b. Plot the partial autocorrelation function at lags 0,1, &amp; 2. 2. For the time seri
UCSB - ECON - 240
5212002 Economics 240C:Problem Set #3 Due 52820011. For the moving average process of order one, MA(1), x(t) = e(t) + 0.5 e(t-1) express x(t) as an infinite autoregressive process, AR(). 2. For the autoregressive-moving average process of orders
UCSB - ECON - 240
5282002Economics 240C Due 60402 Study Questions #4 1. The simple exponential smoothing process, f(t+1) = y(t) + (1) f(t)can also be expressed as the forecast, f(t+1), being a weighted distributed lag of the current and past values of th
UCSB - ECON - 160
Economics 160Lecture 12 Professor VoteyAlternatives to Control ViolenceVotey, Lecture 6, Notes, p. 81, Syllabus XIIAlternative Strategies to Control Crime:qCrimes Discussed:qTaking, Violence, Black Market Crimes, Motoring Offenses Most of
UCSB - ECON - 160
Social WelfareThe Impact of Crime on SocietyLlad Phillips1Preview of Coming AttractionssEvaluate public sector activities in terms of benefits and costsx xx xCosts: \$ Benefits ? How do we value public safety? There is not a &quot;market&quot; fo
UCSB - ECON - 160
Jobs and CrimeLlad Phillips1A theme for this coursesUS and CA criminal justice systems will be case studies, but are there larger issues about the public sector?Llad Phillips2Llad Phillips3Llad Phillips4Public Sector Health Saf
UCSB - ECON - 160
1Name_ Dr. Phillips Dr. Votey Economics 160 FINAL EXAMINATION (possible points 300) Spring 2002Part I (40 minutes) Answer all 40 questions. Choose the BEST answer and mark your Scantron Sheet accordingly. 80 Points . 1. The Economic Paradigm a. b.
UCSB - ECON - 240
Oct. 5, 2006 LEC #3I. IntroductionECON 240A-1 ProbabilityL. PhillipsProbability has its origins in curiosity about the laws governing gambles. During the Renaissance, the Chevalier De Mere posed the following puzzle. Which is more likely (1) r
UCSB - ECON - 240
Oct. 17, 2006 LEC #6 ECON 240A-1 Interval Estimation and Hypothesis TestingI. IntroductionL. PhillipsFrom a simple random sample of voters we obtain a sample proportion of the voters supporting a candidate but we do not know the proportion for t
UCSB - ECON - 240
Oct. 26, 2006 LEC #9 ECON 240A-1 L. Phillips Experimental Method, Clinical Trials and Experimental DesignI. Introduction Economists usually rely on regression to estimate the effect of one variable on another. An example is the effect of deterrence
UCSB - ECON - 240
Nov. 2, 2006 LEC #11 ECON 240A-1 L. Phillips Weibull Distribution; Transformations; Poisson DistributionI. Introduction In Lecture Ten we introduced the exponential distribution as a parametric approach to estimating the distribution of time until f
UCSB - ECON - 240
Nov. 21, 2006 LEC #14 ECON 240A-1 L. Phillips Goodman Log-Linear Model for Qualitative DataI. Introduction Goodman's log-linear model can be used to extend bivariate analysis for qualitative variables from 2x2 Chi-Square tables (or more generally m
UCSB - ECON - 240
Nov. 21, 2006 ECON 240A1 L. PHILLIPS ANALYSIS OF VARIANCE: ONEWAY AND TWOWAY I. Introduction Analysis of variance has many applications. In connection with regression, we have already examined the allocation of the total sum of squares into the exp
UCSB - ECON - 240
Nov. 28, 2006 LEC # 16 I. IntroductionECON 240A-1 Nonparametric StatisticsL. PHILLIPSA principal use of nonparametric methods is for samples whose frequency distribution is not normal. This can be ascertained visually by looking at the histogra
UCSB - ECON - 240
11Econ 240APower Four1Last Time Probability2The Big Picture3The Classical Statistical TrailRates &amp; Inferential Descriptive Statistics Probability Statistics Proportions ApplicationDiscrete Random VariablesBinomialDiscrete Pr
UCSB - ECON - 240
Lecture ElevenProbability Models1Outline Bayesian Probability Duration Models2Bayesian Probability Facts Incidence of the disease in the population is one in a thousand The probability of testing positive if you have the disease is 99 o
UCSB - ECON - 240
Econ 240APower 171Outline Review Projects2Review: Big Picture 1 #1 Descriptive Statistics Numerical central tendency: mean, median, mode dispersion: std. dev., IQR, max-min skewness kurtosis Graphical Bar plots Histograms Scatter pl
UCSB - ECON - 240
Nov. 8, 2006 LAB #6 ECON 240A-1 L. Phillips Exploratory Data Analysis, Scatterplots, Regression and ANOVAI.This first example uses the Anscombe data set, four data files of eleven observationseach on the dependent and explanatory variable. Open
UCSB - ECON - 240
Nov. 29, 2006 Lab #8 Econ240A-1 L. Phillips Goodness of Fit, Chi-Square, and Contingency Table Analysis I. Goodness of Fit For A Variable with the Multinomial Distribution This is an example from the text, Chapter 16, problem 16.7, p.555. It uses the
UCSB - ECON - 240
Dec. 6, 2006 Lab #9 Econ240A-1 One-Way and Two-Way Analysis of Variance I. One-Way Analysis of Variance A. ExcelL. PhillipsOpen the data file xm15-01 that has data on weekly apple juice sales organized by advertising strategy: (1) convenience(cit
UCSB - ECON - 240
Oct. 5, 2006 Due: Oct. 12, 20061. ChevalierECON 240A-1 Problems #1L. PhillipsDe Mere posed the following puzzle. Which is more likely (1) rolling at least one six in four throws of a single die or (2) rolling at least one double six in 24 thro
UCSB - ECON - 240
Nov. 2, 2006 Due Nov 9, 2006 1. 2. Ch. Six: Problems 6.75, 6.77, and 6.82 Ch. 7: Problems 7.116 and 7.117Econ 240A-1L. Phillips3. Ch. 8: Problems 8.75 and 8.76.
UCSB - ECON - 240
Nov. 30, 2006 Due Dec. 7, 2006 1. Problem 15.44.Econ 240A-1L. Phillips
UCSB - ECON - 240
Nov. 7, 2006I.ECON 240A-1 Take-home I : Tuesday Nov. 21L. PhillipsYour report should have an executive summary of one to two pages that summarizes your findings in words for a non-technical reader. It should explain the problem being examined
UCSB - ECON - 240
Fall 2006Econ 240A Reading list - 1Llad PhillipsI. Perspective This is a class in applied statistics, and will emphasize concepts, examples and applications. The lectures will establish a foundation for application and data analysis, covering c
UCSB - ECON - 240
Nov. 2, 2004ECON 240A-1 MidtermL. Phillips1. (15) The box plot for running times from a random sample of Boston Marathon runners is shown below. Table 1-1 lists the data sorted in descending order.BoxPlot145.11 140.1175.18 219.96164.17
UCSB - ECON - 240
Sept. 26, 2002 LEC #1I. I. IntroductionECON 240A-1 Exploratory Data AnalysisL. PhillipsAt the beginning of the course we will study three branches of statistics: (1) data analysis, (2) probability, and (3) statistical inference. Data analysis
UCSB - ECON - 240
Oct 1, 2002 Lecture #2Econ240A-1 Exploratory Data Analysis and JMPL. PhillipsI. Open the JMP program by going to Start, Programs, Statistics, JMP 4 (select). II. Open the data file students by clicking on the open data table button in the JMP s
UCSB - ECON - 240
Oct. 15, 2002 LEC #6 ECON 240A-1 Interval Estimation and Hypothesis TestingI. IntroductionL. PhillipsFrom a simple random sample of voters we obtain a sample proportion of the voters supporting a candidate but we do not know the proportion for t
UCSB - ECON - 240
Oct. 29, 2002 LEC #9 ECON 240A-1 L. Phillips Experimental Method, Clinical Trials and Experimental DesignI. Introduction Economists usually rely on regression to estimate the effect of one variable on another. An example is the effect of deterrence
UCSB - ECON - 240
Nov. 19, 2002 LEC #13 ECON 240A-1 L. Phillips Expected Vs. Observed Frequencies, Contingency Tables &amp; Chi SquareI. Introduction The Chi Square Distribution can be used to compare expected and observed distributions. There are a number of application
UCSB - ECON - 240
Nov. 21, 2002 LEC #14 ECON 240A-1 L. Phillips Goodman Log-Linear Model for Qualitative DataI. Introduction Goodman's log-linear model can be used to extend bivariate analysis for qualitative variables from 2x2 Chi-Square tables (or more generally m
UCSB - ECON - 240
1RegressionEcon 240AOutlineA cognitive device to help understand the formulas for estimating the slope and the intercept, as well as the analysis of variance Table of Analysis of Variance (ANOVA) for regression F distribution for testing the
UCSB - ECON - 240
1Power FifteenAnalysis of Variance (ANOVA)Analysis of VarianceOne-Way ANOVA Tabular RegressionTwo-Way ANOVA Tabular Regression2One-Way ANOVAApple Juice Concentrate Example, Data File xm 15-01 New product Try 3 different adverti
UCSB - ECON - 240
Power 161Projects2Logistics Put power point slide show on a high density floppy disk for a WINTEL machine. Email Llad@econ.ucsb.edu the slide-show as a PowerPoint attachment3Assignments 1. Project choice 2. Data Retrieval 3. Stati
UCSB - ECON - 240
Oct. 2, 2002 LAB #1 ECON 240A-1 L. Phillips Orientation to Excel; Exploratory Data AnalysisI. Orientation to Excel Help Menu: &quot;About Microsoft Excel&quot; &quot;System Info&quot; Help Menu: &quot;Contents and Index&quot; &quot;Key Information&quot;: &quot;If you are new to spreadsheets&quot; &quot;
UCSB - ECON - 240
Oct. 9, 2002 LAB #2ECON 240A-1 Binomial DistributionL. PhillipsI. Calculating the Binomial Distribution: Flips of a Fair Coin ( # of trials) 1. One flip Open Excel, In cell B3, type &quot;Binomial 1 flip&quot; In cell B5 type &quot;k&quot; for the binomial variabl
UCSB - ECON - 240
Oct. 30, 2002 LAB #5 ECON 240A-1 L. Phillips Exploratory Data Analysis, Scatterplots, and RegressionI. The Fortune 500, 1999 : Fifty Firms Ranked by Revenues Source: http:/www. fortune.com/fortune/ Data for these fifty firms includes, in addition to
UCSB - ECON - 240
Nov. 6, 2002 LAB #6 ECON 240A-1 L. Phillips Exploratory Data Analysis, Scatterplots, Regression and ANOVAI.This first example uses the Anscombe data set, four data files of eleven observationseach on the dependent and explanatory variable. Open
UCSB - ECON - 240
Nov. 13, 2002 Lab #7 Econ240A-1 L. Phillips Exploratory Data Analysis and Failure Times; Linear Probability Model I. Failure Time Analysis, Cumulative Hazard Rate, Survivor Function The data is time until failure of the fan, in hours, for diesel gene
UCSB - ECON - 240
Nov. 20, 2002 Lab #8 Econ240A-1 L. Phillips Goodness of Fit, Chi-Square, and Contingency Table Analysis I. Goodness of Fit For A Variable with the Multinomial Distribution This is an example from the text, Chapter 16, problem 16.7, p.550. It uses the