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    Econometric Models

    Econometric models are statistical equations used in econometrics that determines the statistical relationship between different economic quantities.  The three primary goals of econometrics are structural analysis, forecasting, and policy evaluation. Econometricians measure past relationships between different variables such as household income, tax rates, employment, and interest rates. The information from these relationships are then used in an equation to try to forecast how changes in some variables will affect others in the future.

    Econometric models are modelled after an equation that relates a dependent variable to a variety of independent determingin economic factors. Simple models can be ones such as a linear regression using an OLS estimator (Ordinary Least Squared). More complex models can involve time-series, autoregressions, moving averages, etc. Different models have idfferent applciations and will produce various levels of accuracy and significance depending on the situation and the available data. 

    Econometric models are a cornerstone to applied economics and are necessary to prove the validity of any sort of model or conjecture. It is commonly used to distinguish and determine correlations, causations, and counterfactual information. 

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    Prediction of ARMA(1,1) Model

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    Econometrics Modelling

    You have been given some information about a company's sales of a type of confectionary. The attached Excel spreadsheet contains weekly sales volumes, average selling prices and distribution (the percentage of retail stores stocking the product in that week) over a three year period for 4 Stock Keeping Units (SKUs) of this type

    Univariate time series modelling and forecasting

    1 -Consider the following 3 models that a researcher suggests might be a reasonable model of stock market prices Yt = Yt -1 +Ut Yt = 0.5 Yt -1 + Ut Yt = 0.8 Yt -1 + Ut a) What classes of models are these examples of ? b) What would the autocorrelation function for each of these processes look like ? (only consider the s

    Demand for Computers

    Explain how each of the following will affect the demand for computers: (i) a rise in incomes, (ii) an expected drop in the price of computers, (iii) a drop in the price of software, (iv) computers becoming simpler to operate, (v) a drop in the price of tablets. Explain any assumptions that you have to make to give your answer.

    Stochastic Regression Functions

    What are the similarities and/or differences between a stochastic population regression function (PRF) and a stochastic sample regression function (SRF)?

    Introduction to Econometrics problem

    Please see the table attached to use for the two questions below. Thank you. Q1) Calculate the R-squared in column (2), (3),(4), and (5) and use: R(squared) = 1 - (SSR/TSS). R(hat)(squared) = 1 - (n - 1/n - k - 1)(SSR/TSS) to find out the relationship between the adjusted R-squared (given in the table) and R-squ

    Intro to Econometrics practice problem

    ** Please see the attached file for a Word formatted copy of the problem description ** Each row in the table below with a variable name has a missing value. Knowing that [(n-1)/(n-k-1)] = 1.150819, find them (a)-(f) and show your work. You are looking at the relationship between rates of coronary heart disease and abu

    Central limit theorem and regression

    1.The central limit theorem says that: a) Y(bar) is consistent for its mean. b) Y has a standard normal distribution in large samples. c) Y(bar) is converge in probability to its mean. d) the distribution of Y is approximately normally distributed in large samples. 2. If the regression errors are homoskedastic, implies:

    Ordinary Least Squares regression

    Please see the attached document. Thanks. 1) Suppose, now, that we wish to investigate whether hourly wages differ, on average, between union manufacturing jobs and non-union manufacturing jobs among adult workers in the U.S in 2008. We collect data on hourly wages and union membership among manufacturing workers older than 1

    Interpretation of results in econometrics

    Can someone please explain how to read the result-sheet? The error correction term EqCM08q4(t) = cpeb (t-1)- 0.85y (t-1) - 0.15w (t) + 0.7RRa (t-1), Seasonal, Seasonal_1 and Seasonal_2 are dummy variables included to pick up changes in the quarterly distribution of income from 2002 and I'm disregarding the DKjstep02 dummies.

    Revenue Management

    You have been retained by a regional food marketer, FoodKing, to forecast the demand for small cakes that are mass-produced and marketed under the name Johnny's Pies. To assist with your analysis, you are provided with data that was collected for 8 consecutive quarters and 6 geographic markets. (a) Estimate a regression model

    Estimated regression coefficients

    What is known as the characteristic line of modern investment analysis is simply the regression line obtained from the following model: rit = αi + βi rmt + et Where rit = the rate of return on the ith security in time t; rmt = the rate of return on the market portfolio in time t; et = stochastic disturbance

    Econometrics interpretation and slope parameters

    The median starting salary (SLRY) for new law school graduates is explained by log (SLRY) = β0 + β1 LSAT + β3 GPA + β4 log(LIBB) + β5 log(COST) + β6 RANK + e where LSAT is the median LSAT score for the graduating class, GPA is the median college GPA for the class, LIBB is the numbe


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    slope and intercept parameters

    An estimate of the model: Y= a + bX + u yielded the following values displayed in the table below y x y estimate u estimate 0 1 -1 1 0 1 -2 2 1 1 0 1 2 3 3 -1 3 2 4


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    Economics and Management

    Would there be any differences in the set of variables used in a regression model of the demand for consumer durables (e.g., automobiles, appliances, furniture) and a regression model of the demand for "fast-moving consumer goods" (e.g., food, beverages, personal care products)? Think about the factors that influence your d

    Problem Set: Value of the T-Statistics

    See the attached file. You have some data on a sample of investment bankers, and are interested in the impacts of height and of seniority on their success. You estimate what you call Model A using a software package (which, like most econometrics packages, always reports p-values for two-sided hypothesis tests): (A) SALARY

    Regressions, scattergrams - Econometrics

    (See attached file for full problem description) --- 3.7 Based on the data for the United States for the period 1970 to 1983, the following regression results were obtained: GNPt = -787.4723 + 8.0863M1t r2 = 0.9912 se = ( ) (0.2197) t= (-10.10001) ( ) where GNP is the gros

    Econometrics Estimated Models

    Suppose you want to estimate a model of women's earnings at age 50. You have data for a sample of employed women, provided by the alumni associations of Mills College and Smith College, on: ? A woman's salary at age 50 ? Her age ? Year of graduation ? Her high school GPA ? Her college GPA ? Her college major ? Her job te

    Problem Set

    Suppose you want to estimate a model of women's earnings at age 50. You have data for a sample of employed women, provided by the alumni associations of Mills College and Smith College, on: ? A woman's salary at age 50 ? Her age ? Year of graduation ? Her high school GPA ? Her college GPA ? Her college major ? Her job te

    Dummy variable

    An Economics department at a large university keeps track of its majors' starting salaries. We address the question of the value of taking econometrics, based on last year's crop of 50 majors. Let SAL=$ salary, GPA = grade point average on a 4.0 scale, METRICS=1 if student took econometrics, METRICS = 0 otherwise, SEX=1 if s

    Economics forecasting research

    When disparities exist from census data, housing starts data and other research data when seeking for information to help with forcasting the power tool market how do you reconcile the differences in the various forecasting services to provide an insight into the future that you can rely on and base decisions on?

    Hypothesis tests in STATA

    1. The file cocaine.dta contains 56 observations on variables related to sales of cocaine powder in northeastern California over the period 1984- 1991. The variables are price = price per gram in dollars for a cocaine sale quant = number of grams of cocaine in a given sale qual = quality of the cocaine expressed as percenta

    Statistics for economists/econometrics

    See attached file for full problem description --- Consider the following relationship between the amount of money spent by a province on health care (Y) and the province's GDP (X):... ---

    probability of the error (STATA)

    1. Yes, we use STATA to test. 2. Tax is the dependent variable: tax = B1 + B2income + e The gross income and tax paid by a cross-section of 30 companies in 1988 and 1989 is given in the file tax.dta Consider the estimate for B2 for 1989 and the corresponding estimated variance. Pretend that the estimated variance is

    Growth in Profits - Degree of Operating Leverage?

    If the sales of both software and deyboad divisions increase at the same rate, which will have the fastest growth in profits? I believe the key is derived by using DOL, or the degree of operating leverage. I am not sure how to apply this methodology to the sample data attached below.


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