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LazyCam is a BETA-release free importer included with Mach3. It’s purpose is to import standard dxf, cmx, and other file types to allow those that do not use CAM programs to more easily generate Gcode to be run under Mach3. We will email you the license key, and No refund after we email you the license file Windows 2000, Windows XP, Windows Vista, or Windows 7 Operating System will be ok Due to the availability of other, more mature, low cost CAM packages, LazyCam development has ceased and it will remain in permanent Beta status. Nov 25, 2015 So I want to pull my license files so that if I can swap the tower and reload my licenses. Can I just go into the software and copy them to a USB? I read on the net that the license file is just named mach1lic.dat.xxxxxx and is in the main folder, but havent found anything about lazycam. https://omginbox.netlify.app/lazy-cam-license-file.html. Here are some of the basic features and functions provided by Mach3: Converts a standard PC to a fully featured, 6-axis CNC controller; Allows direct import of DXF, BMP, JPG, and HPGL files.
Elements of econometrics, Jan Kmenta, 1971, Business & Economics, 655 pages. Http:// Jan Kmenta: Elements of Econometrics. 1990 pdf Machine pdf writer that produces quality PDF files. Http:// Jan Kmenta, Elements of Econometrics, 2 editionnd (New York: Macmillan Publishing Company, 1986), p. [PDF] elements of econometrics (28 pages) - purpose of econometrics jan kmenta pdf essaycorrections, elements of econometrics jan kmenta. Traducere din pdf in word. Elements Of Econometrics Kmenta Pdf Editor Cricketfreeware, The Elements Of Financial Econometrics 豆瓣, Elements Of Time Series Econometrics Evzen Kocenda, Welcome To.
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Microsoft Word - Module 29-30_Time Series Basics</title>n<meta http-equiv='Content-Type' content='text/html; charset='UTF-8'>n</head>nn<div><div><p><b>2 | </b>P a g e <b> n</b> n</p>n<p>2 n n</p>n<p><b>THE BASIC STEPS IN THE BOX-JENKINS METHODOLOGY n</b></p>n<p> Differencing the series so as to achieve stationarity n</p>n<p> Identification of a tentative model n</p>n<p> Estimation of the model n</p>n<p> Diagnostic checking n</p>n<p> Using the model for forecasting and control n</p>n<p><b>The above points have been discussed in brief below: n</b></p>n<p><b> n</b></p>n<p><b>Step 1: n</b></p>n<p> Differencing to achieve stationarity: How do we conclude whether a time series is n</p>n<p>stationary or not? n</p>n<p> We can do this by studying the graph of the correlogram of the series. n</p>n<p> The correlogram of a stationary series drops off as k, the number of lags, becomes large, n</p>n<p>but this is not usually the case for a nonstationary series. n</p>n<p> Thus the common procedure is to plot the correlogram of the given series yt and n</p>n<p>successive differences Δyt, Δyt, and so on, and look at the correlograms at each stage. n</p>n<p> We keep differencing until the correlogram dampens. n</p>n<p> n</p>n<p>Docsity.com</p>nn</div></div>n<div><div><p><b>3 | </b>P a g e <b> n</b> n</p>n<p>3 n n</p>n<p><b>Step 2: n</b></p>n<p>Once we have used the differencing procedure to get a stationary time series, we examine the n</p>n<p>correlogram to decide on the appropriate orders of the AR and MA components. n</p>n<p> The correlogram of a MA process is zero after a point. n</p>n<p> That of an ARprocess declines geometrically. The correlograms of ARMA processes n</p>n<p>show different patterns. n</p>n<p> Based on these, one arrives at a tentative ARMA model. n</p>n<p> This step involves more of a judgmental procedure than the use of any clear-cut rules. n</p>n<p><b>Step 3: n</b></p>n<p>The next step is the estimation of the tentative ARMA model identified in step 2. We have n</p>n<p>discussed in the preceding section the estimation of ARMA models. n</p>n<p><b>Step 4: n</b></p>n<p>The next step is diagnostic checking to check the adequacy of the tentative model. We discussed n</p>n<p>in the preceding section the Q and Q* statistics commonly used in diagnostic checking. n</p>n<p><b>Step 5: n</b></p>n<p>The final step is forecasting. It is the process of making predictions about the economy. n</p>n<p>Forecasts can be carried out at a high level of aggregation - for example for GDP, inflation, n</p>n<p>unemployment or the fiscal deficit - or at a more disaggregated level, for specific sectors of the n</p>n<p>economy or even specific firms. n</p>n<p>Docsity.com</p>nn</div></div>n<div><div><p><b>4 | </b>P a g e <b> n</b> n</p>n<p>4 n n</p>n<p><b>THE SAMPLE PROBLEMS n</b></p>n<p><b>REFERENCES FOR FURTHER READING: n</b></p>n<p>Dhrymes, Phoebus J.: <i>Introductory Econometrics, </i>Springer‐Verlag, New York, 1978. n</p>n<p>Dielman, Terry E.: <i>Applied Regression Analysis for Business and Economics,</i> PWS‐Kent, Boston, 1991. n</p>n<p>Draper, N. R., and H. Smith: <i>Applied Regression Analysis, </i>3d ed., John Wiley & Sons, New York, 1998. n</p>n<p>Dutta, M.: <i>Econometric Methods, </i>South‐Western Publishing Company, Cincinnati, 1975. n</p>n<p>Frank, C. R., Jr.: <i>Statistics and Econometrics, </i>Holt, Rinehart and Winston, New York, 1971. n</p>n<p>Goldberger, A. S.: <i>A Course in Econometrics, </i>Harvard University Press, Cambridge, Mass., 1991. n</p>n<p>Goldberger, A. S.: <i>Topics in Regression Analysis, </i>Macmillan, New York, 1968. n</p>n<p>Goldberger, Arthur S.: <i>Introductory Econometrics, </i>Harvard University Press, 1998. n</p>n<p>Greene, William H.: <i>Econometric Analysis, </i>4th ed., Prentice Hall, Englewood Cliffs, N. J., 2000. n</p>n<p>Griffiths, William  E.,  R.  Carter Hill  and George G.  Judge:  <i>Learning  and</i>  <i>Practicing  Econometrics,  </i>John n</p>n<p>Wiley & Sons, New York, 1993. n</p>n<p>Gujarati, Damodar N.: <i>Essentials of Econometrics, </i>2d ed., McGraw‐Hill, New York, 1999. n</p>n<p>Hamilton, James D.: <i>Time Series Analysis, </i>Princeton University Press, Princeton, N. J., 1994. n</p>n<p>Harvey, A. C.: <i>The Econometric Analysis of Time Series, </i>2d ed., MIT Press, Cambridge, Mass., 1990. n</p>n<p>Hayashi, Fumio: <i>Econometrics, </i>Princeton University Press, Princeton, N. J., 2000. n</p>n<p>Hill, Carter, William Griffiths, and George Judge: <i>Undergraduate Econometrics, </i>John Wiley & Sons, New n</p>n<p>York, 2001. n</p>n<p>Hu, Teh‐Wei: <i>Econometrics: An Introductory Analysis, </i>University Park Press, Baltimore, 1973. n</p>n<p>Johnston, J.: <i>Econometric Methods, </i>3d ed., McGraw‐Hill, New York, 1984. n</p>n<p>Docsity.com</p>nn</div></div>n<div><div><p><b>5 | </b>P a g e <b> n</b> n</p>n<p>5 n n</p>n<p>Judge, George G., Carter R. Hill, William E. Griffiths, Helmut Lütkepohl, and Tsoung‐Chao Lee: <i>Theory and n</i></p>n<p><i>Practice of Econometrics, </i>John Wiley & Sons, New York, 1980. n</p>n<p>Katz, David A.: <i>Econometric Theory and Applications, </i>Prentice Hall, Englewood Cliffs, N.J., 1982. n</p>n<p>Klein, Lawrence R.: <i>A Textbook of Econometrics, </i>2d ed., Prentice Hall, Englewood Cliffs, N.J., 1974. n</p>n<p>Klein, Lawrence R.: <i>An Introduction to Econometrics, </i>Prentice Hall, Englewood Cliffs, N.J., 1962. n</p>n<p>Kmenta, Jan: <i>Elements of Econometrics, </i>2d ed., Macmillan, New York, 1986. n</p>n<p>Koop, Gary: <i>Analysis of Economic Data, </i>John Wiley & Sons, New York, 2000. n</p>n<p>Madda, G. S., and Kim In‐Moo: <i>Unit Roots, Cointegration, and Structural</i> <i>Change, </i>Cambridge University n</p>n<p>Press, New York, 1998. n</p>n<p>Mills, T. C.: <i>The Econometric Modelling of Financial Time Series, </i>Cambridge University Press, 1993. n</p>n<p>Mills, T. C.: <i>Time Series Techniques for Economists, </i>Cambridge University Press, 1990. n</p>n<p>Mukherjee, Chandan, Howard White, and Marc Wuyts: <i>Econometrics and</i> <i>Data Analysis for Developing n</i></p>n<p><i>Countries, </i>Routledge, New York, 1998. n</p>n<p>Patterson, Kerry: <i>An  Introduction  to Applied Econometrics: A Time Series</i> <i>Approach, </i>St. Martin’s Press, n</p>n<p>New York, 2000. n</p>n<p>Rao, C. R.: <i>Linear Statistical Inference and Its Applications, </i>2d ed., John Wiley & Sons, New York, 1975. n</p>n<p>Rao, Potluri, and Roger LeRoy Miller: <i>Applied Econometrics, </i>Wadsworth, Belmont, Calif., 1971. n</p>n<p>Walters, A. A.: <i>An Introduction to Econometrics, </i>Macmillan, London, 1968. n</p>n<p>Zellner, A.: <i>An Introduction to Bayesian Inference in Econometrics, </i>John Wiley & Sons, New York, 1971. n</p>n<p><b>FAQS (FREQUENTLY ASKED QUESTIONS): n</b></p>n<p>1. A time series data whose mean variance and autocovariance is time in variant is  n</p>n<p>a) Stationary series  n</p>n<p>b) Non‐Stationary series  n</p>n<p>Docsity.com</p>nn</div></div>n<div><div><p><b>6 | </b>P a g e <b> n</b> n</p>n<p>6 n n</p>n<p>c) Purely random series n</p>n<p>d) Random walk n</p>n<p>e) None n</p>n<p>2. A  non‐stationary time series is one with  n</p>n<p>a) Time‐varying mean n</p>n<p>b) Time‐varying variance n</p>n<p>c) Both a) and b) n</p>n<p>d) Time variant mean and variance n</p>n<p>e) None n</p>n<p>3. A purely random process is stationary series with n</p>n<p>a) Zero variance n</p>n<p>b) Zero mean n</p>n<p>c) Positive mean n</p>n<p>d) Zero mean and Zero variance n</p>n<p>e) All of the above n</p>n<p>4.  A series that is inherently non‐stationary is n</p>n<p>a) Random walk with drift n</p>n<p>b) Random walk without drift n</p>n<p>c) Both a) and b) n</p>n<p>d) GDP of a country n</p>n<p>e) None of the above n</p>n<p><b>SELF EVALUATION TESTS/ QUIZZES n</b></p>n<p>1. An approach to forecasting using time‐series data is  n</p>n<p>Docsity.com</p>nn</div></div>n<div><div><p><b>7 | </b>P a g e <b> n</b> n</p>n<p>7 n n</p>n<p>a) Single equation regression model n</p>n<p>b) Simultaneous equation regression model n</p>n<p>c) Simultaneous equation regression model n</p>n<p>d) All the above n</p>n<p>e) None of the above n</p>n<p>2. An example of model usually not derived from any economic theory is n</p>n<p>a) Single equation regression model n</p>n<p>b) Simultaneous equation regression model n</p>n<p>c) ARIMA n</p>n<p>d) All the above n</p>n<p>e) None of the above n</p>n<p>3. The model where value of Y depends only on its value in the previous time period and random n</p>n<p>term is n</p>n<p>a) Single equation model n</p>n<p>b) AR (1) Model n</p>n<p>c) MA (1) Model n</p>n<p>d) ARMA (1,1) Model n</p>n<p>e) None of the above n</p>n<p>4.  When Y depends on current and previous time period error term ,it is a  n</p>n<p>a) Single equation model n</p>n<p>b) AR (1) Model n</p>n<p>c) MA (1) Model n</p>n<p>d) ARMA (1,1) Model n</p>n<p>e) None of the above n</p>n<p>Docsity.com</p>nn</div></div>n<div><div><p><b>8 | </b>P a g e <b> n</b> n</p>n<p>8 n n</p>n<p> n</p>n<p>5. Decide if you agree or disagree with each of the following statements and give a brief n</p>n<p>explanation of your decision: n</p>n<p>a) Like cross-sectional observations, we can assume that most time series observations n</p>n<p>are independently distributed. n</p>n<p>b) The OLS estimator in a time series regression is unbiased under the first three Gauss-n</p>n<p>Markov assumptions. n</p>n<p>c) A trending variable cannot be used as the dependent variable in multiple regression n</p>n<p>analysis. n</p>n<p>d) Seasonality is not an issue when using annual time series observations. n</p>n<p>e) None of the above n</p>n<p> n</p>n<p>6. Suppose you have quarterly data on new housing starts, interest rates, and real per capita n</p>n<p>income. Specify a model for housing starts that accounts for possible trends and n</p>n<p>seasonality in the variables. n</p>n<p> n</p>n<p>7. From the U.K. private sector housing starts (X) for the period 1948 to 1984, obtained the n</p>n<p>following regression results: n</p>n<p>Xt = 31.03 − 0.188Xt−1 n</p>n<p> n</p>n<p>se = (12.50) (0.080) n</p>n<p> n</p>n<p>(t =) τ (−2.35) n</p>n<p> n</p>n<p>Docsity.com</p>nn</div></div>n<div><div><p><b>9 | </b>P a g e <b> n</b> n</p>n<p>9 n n</p>n<p>Note: The 5 percent critical τ value is −2.95 and the 10 percent critical τ value is −2.60. n</p>n<p>a) On the basis of these results, is the housing starts time series stationary or n</p>n<p>nonstationary? Alternatively, is there a unit root in this time series? How do you n</p>n<p>know? n</p>n<p>b) If you were to use the usual t test, is the observed t value statistically significant? On n</p>n<p>this basis, would you have concluded that this time series is stationary? n</p>n<p> n</p>n<p>8. In random walk without drift n</p>n<p>a) The effect of shock persists throughout the time period n</p>n<p>b) The effect of shock in the past die out over time period n</p>n<p>c) The effect of shock drift away quickly n</p>n<p>d) There is no effect of past shock n</p>n<p>e) None of the above n</p>n<p><b> n</b></p>n<p><b> n</b></p>n<p>Docsity.com</p>nn</div></div>n</body></html>','canEdit':false,'canDelete':false,'canReport':false,'userVote':null,'previewLimit':3,'advEnabled':true,'totalVotes':0,'title':'Basic Steps in the Box Jenkins - Econometric Modeling - Lecture Notes, Study notes for Econometrics and Mathematical Economics','isPremiumEnabled':false,'hasQuizcardSet':null}'><div><div><div><header><div><div><div><div></div><div><div><span>3</span><span>Number of download</span></div></div><div><div><div><span>Econometric models are statistical models used in econometric. This modelling tool help economist develop future economy plan for the company. This lecture note discuss important points for understanding Econometric modelling, it includes Steps, Box Jenkins, Methodology, Identification, Tentative, Model, Estimation, Diagnostic</span></div></div></div><div><div><div></div></div><div><div><div><div><span><span>20</span> points</span><div><div><div><div>Download points needed to download</div></div></div></div></div></div></div></div></div></div></div></div></header><div><main><div><div><div><span><span>Preview</span><span>3 pages / 8</span></span></div><div><div><div><div><div><div></div><div></div></div></div></div></div></div></div></div><div><div><div></div><div></div><div></div></div></div><div><div><div><div><div><div>Microsoft Word - Module 29-30_Time Series Basics<div><div><p><b>2 | </b>P a g e <b></b></p><p>2 </p><p><b>THE BASIC STEPS IN THE BOX-JENKINS METHODOLOGY </b></p><p> Differencing the series so as to achieve stationarity </p><p> Identification of a tentative model </p><p> Estimation of the model </p><p> Diagnostic checking </p><p> Using the model for forecasting and control </p><p><b>The above points have been discussed in brief below: </b></p><p><b></b></p><p><b>Step 1: </b></p><p> Differencing to achieve stationarity: How do we conclude whether a time series is </p><p>stationary or not? </p><p> We can do this by studying the graph of the correlogram of the series. </p><p> The correlogram of a stationary series drops off as k, the number of lags, becomes large, </p><p>but this is not usually the case for a nonstationary series. </p><p> Thus the common procedure is to plot the correlogram of the given series yt and </p><p>successive differences Δyt, Δyt, and so on, and look at the correlograms at each stage. </p><p> We keep differencing until the correlogram dampens. </p><p>Docsity.com</p></div></div><div><div><p><b>3 | </b>P a g e <b></b></p><p>3 </p><p><b>Step 2: </b></p><p>Once we have used the differencing procedure to get a stationary time series, we examine the </p><p>correlogram to decide on the appropriate orders of the AR and MA components. </p><p> The correlogram of a MA process is zero after a point. </p><p> That of an ARprocess declines geometrically. The correlograms of ARMA processes </p><p>show different patterns. </p><p> Based on these, one arrives at a tentative ARMA model. </p><p> This step involves more of a judgmental procedure than the use of any clear-cut rules. </p><p><b>Step 3: </b></p><p>The next step is the estimation of the tentative ARMA model identified in step 2. We have </p><p>discussed in the preceding section the estimation of ARMA models. </p><p><b>Step 4: </b></p><p>The next step is diagnostic checking to check the adequacy of the tentative model. We discussed </p><p>in the preceding section the Q and Q* statistics commonly used in diagnostic checking. </p><p><b>Step 5: </b></p><p>The final step is forecasting. It is the process of making predictions about the economy. </p><p>Forecasts can be carried out at a high level of aggregation - for example for GDP, inflation, </p><p>unemployment or the fiscal deficit - or at a more disaggregated level, for specific sectors of the </p><p>economy or even specific firms. </p><p>Docsity.com</p></div></div><div><div><p><b>4 | </b>P a g e <b></b></p><p>4 </p><p><b>THE SAMPLE PROBLEMS </b></p><p><b>REFERENCES FOR FURTHER READING: </b></p><p>Dhrymes, Phoebus J.: <i>Introductory Econometrics, </i>Springer‐Verlag, New York, 1978. </p><p>Dielman, Terry E.: <i>Applied Regression Analysis for Business and Economics,</i> PWS‐Kent, Boston, 1991. </p><p>Draper, N. R., and H. Smith: <i>Applied Regression Analysis, </i>3d ed., John Wiley & Sons, New York, 1998. </p><p>Dutta, M.: <i>Econometric Methods, </i>South‐Western Publishing Company, Cincinnati, 1975. </p><p>Frank, C. R., Jr.: <i>Statistics and Econometrics, </i>Holt, Rinehart and Winston, New York, 1971. </p><p>Goldberger, A. S.: <i>A Course in Econometrics, </i>Harvard University Press, Cambridge, Mass., 1991. </p><p>Goldberger, A. S.: <i>Topics in Regression Analysis, </i>Macmillan, New York, 1968. </p><p>Goldberger, Arthur S.: <i>Introductory Econometrics, </i>Harvard University Press, 1998. </p><p>Greene, William H.: <i>Econometric Analysis, </i>4th ed., Prentice Hall, Englewood Cliffs, N. J., 2000. </p><p>Griffiths, William E., R. Carter Hill and George G. Judge: <i>Learning and</i><i>Practicing Econometrics, </i>John </p><p>Wiley & Sons, New York, 1993. </p><p>Gujarati, Damodar N.: <i>Essentials of Econometrics, </i>2d ed., McGraw‐Hill, New York, 1999. </p><p>Hamilton, James D.: <i>Time Series Analysis, </i>Princeton University Press, Princeton, N. J., 1994. </p><p>Harvey, A. C.: <i>The Econometric Analysis of Time Series, </i>2d ed., MIT Press, Cambridge, Mass., 1990. </p><p>Hayashi, Fumio: <i>Econometrics, </i>Princeton University Press, Princeton, N. J., 2000. </p><p>Hill, Carter, William Griffiths, and George Judge: <i>Undergraduate Econometrics, </i>John Wiley & Sons, New </p><p>York, 2001. </p><p>Hu, Teh‐Wei: <i>Econometrics: An Introductory Analysis, </i>University Park Press, Baltimore, 1973. </p><p>Johnston, J.: <i>Econometric Methods, </i>3d ed., McGraw‐Hill, New York, 1984. </p><p>Docsity.com</p></div></div><div><div><p><b>5 | </b>P a g e <b></b></p><p>5 </p><p>Judge, George G., Carter R. Hill, William E. Griffiths, Helmut Lütkepohl, and Tsoung‐Chao Lee: <i>Theory and </i></p><p><i>Practice of Econometrics, </i>John Wiley & Sons, New York, 1980. </p><p>Katz, David A.: <i>Econometric Theory and Applications, </i>Prentice Hall, Englewood Cliffs, N.J., 1982. </p><p>Klein, Lawrence R.: <i>A Textbook of Econometrics, </i>2d ed., Prentice Hall, Englewood Cliffs, N.J., 1974. </p><p>Klein, Lawrence R.: <i>An Introduction to Econometrics, </i>Prentice Hall, Englewood Cliffs, N.J., 1962. </p><p>Kmenta, Jan: <i>Elements of Econometrics, </i>2d ed., Macmillan, New York, 1986. </p><p>Koop, Gary: <i>Analysis of Economic Data, </i>John Wiley & Sons, New York, 2000. </p><p>Madda, G. S., and Kim In‐Moo: <i>Unit Roots, Cointegration, and Structural</i><i>Change, </i>Cambridge University </p><p>Press, New York, 1998. </p><p>Mills, T. C.: <i>The Econometric Modelling of Financial Time Series, </i>Cambridge University Press, 1993. </p><p>Mills, T. C.: <i>Time Series Techniques for Economists, </i>Cambridge University Press, 1990. </p><p>Mukherjee, Chandan, Howard White, and Marc Wuyts: <i>Econometrics and</i><i>Data Analysis for Developing </i></p><p><i>Countries, </i>Routledge, New York, 1998. </p><p>Patterson, Kerry: <i>An Introduction to Applied Econometrics: A Time Series</i><i>Approach, </i>St. Martin’s Press, </p><p>New York, 2000. </p><p>Rao, C. R.: <i>Linear Statistical Inference and Its Applications, </i>2d ed., John Wiley & Sons, New York, 1975. </p><p>Rao, Potluri, and Roger LeRoy Miller: <i>Applied Econometrics, </i>Wadsworth, Belmont, Calif., 1971. </p><p>Walters, A. A.: <i>An Introduction to Econometrics, </i>Macmillan, London, 1968. </p><p>Zellner, A.: <i>An Introduction to Bayesian Inference in Econometrics, </i>John Wiley & Sons, New York, 1971. </p><p><b>FAQS (FREQUENTLY ASKED QUESTIONS): </b></p><p>1. A time series data whose mean variance and autocovariance is time in variant is </p><p>a) Stationary series </p><p>b) Non‐Stationary series </p><p>Docsity.com</p></div></div><div><div><p><b>6 | </b>P a g e <b></b></p><p>6 </p><p>c) Purely random series </p><p>d) Random walk </p><p>e) None </p><p>2. A non‐stationary time series is one with </p><p>a) Time‐varying mean </p><p>b) Time‐varying variance </p><p>c) Both a) and b) </p><p>d) Time variant mean and variance </p><p>e) None </p><p>3. A purely random process is stationary series with </p><p>a) Zero variance </p><p>b) Zero mean </p><p>c) Positive mean </p><p>d) Zero mean and Zero variance </p><p>e) All of the above </p><p>4. A series that is inherently non‐stationary is </p><p>a) Random walk with drift </p><p>b) Random walk without drift </p><p>c) Both a) and b) </p><p>d) GDP of a country </p><p>e) None of the above </p><p><b>SELF EVALUATION TESTS/ QUIZZES </b></p><p>1. An approach to forecasting using time‐series data is </p><p>Docsity.com</p></div></div><div><div><p><b>7 | </b>P a g e <b></b></p><p>7 </p><p>a) Single equation regression model </p><p>b) Simultaneous equation regression model </p><p>c) Simultaneous equation regression model </p><p>d) All the above </p><p>e) None of the above </p><p>2. An example of model usually not derived from any economic theory is </p><p>a) Single equation regression model </p><p>b) Simultaneous equation regression model </p><p>c) ARIMA </p><p>d) All the above </p><p>e) None of the above </p><p>3. The model where value of Y depends only on its value in the previous time period and random </p><p>term is </p><p>a) Single equation model </p><p>b) AR (1) Model </p><p>c) MA (1) Model </p><p>d) ARMA (1,1) Model </p><p>e) None of the above </p><p>4. When Y depends on current and previous time period error term ,it is a </p><p>a) Single equation model </p><p>b) AR (1) Model </p><p>c) MA (1) Model </p><p>d) ARMA (1,1) Model </p><p>e) None of the above </p><p>Docsity.com</p></div></div><div><div><p><b>8 | </b>P a g e <b></b></p><p>8 </p><p>5. Decide if you agree or disagree with each of the following statements and give a brief </p><p>explanation of your decision: </p><p>a) Like cross-sectional observations, we can assume that most time series observations </p><p>are independently distributed. </p><p>b) The OLS estimator in a time series regression is unbiased under the first three Gauss-</p><p>Markov assumptions. </p><p>c) A trending variable cannot be used as the dependent variable in multiple regression </p><p>analysis. </p><p>d) Seasonality is not an issue when using annual time series observations. </p><p>e) None of the above </p><p>6. Suppose you have quarterly data on new housing starts, interest rates, and real per capita </p><p>income. Specify a model for housing starts that accounts for possible trends and </p><p>seasonality in the variables. </p><p>7. From the U.K. private sector housing starts (X) for the period 1948 to 1984, obtained the </p><p>following regression results: </p><p>Xt = 31.03 − 0.188Xt−1 </p><p>se = (12.50) (0.080) </p><p>(t =) τ (−2.35) </p><p>Docsity.com</p></div></div><div><div><p><b>9 | </b>P a g e <b></b></p><p>9 </p><p>Note: The 5 percent critical τ value is −2.95 and the 10 percent critical τ value is −2.60. </p><p>a) On the basis of these results, is the housing starts time series stationary or </p><p>nonstationary? Alternatively, is there a unit root in this time series? How do you </p><p>know? </p><p>b) If you were to use the usual t test, is the observed t value statistically significant? On </p><p>this basis, would you have concluded that this time series is stationary? </p><p>8. In random walk without drift </p><p>a) The effect of shock persists throughout the time period </p><p>b) The effect of shock in the past die out over time period </p><p>c) The effect of shock drift away quickly </p><p>d) There is no effect of past shock </p><p>e) None of the above </p><p><b></b></p><p><b></b></p><p>Docsity.com</p></div></div></div></div></div></div></div></div><div><div><div><div></div></div></div></div></main><div><div><div><div></div></div></div></div><div><div><div><div><div><div><div><div><div><div></div></div><div><div></div></div></div></div></div></div></div><div><div><div><div><div><div><div></div></div><div><div></div></div></div></div></div></div></div><div><div><div><div><div><div><div></div></div><div><div></div></div></div></div></div></div></div><div><div><div><div><div><div><div></div></div><div><div></div></div></div></div></div></div></div><div><div><div><div><div><div><div></div></div><div><div></div></div></div></div></div></div></div><div><div><div><div><div><div><div></div></div><div><div></div></div></div></div></div></div></div></div></div></div><div><div><div><div><div><div><div><div><div><div></div></div><div><div></div></div></div></div></div></div></div><div><div><div><div><div><div><div></div></div><div><div></div></div></div></div></div></div></div><div><div><div><div><div><div><div></div></div><div><div></div></div></div></div></div></div></div><div><div><div><div><div><div><div></div></div><div><div></div></div></div></div></div></div></div><div><div><div><div><div><div><div></div></div><div><div></div></div></div></div></div></div></div></div></div></div></aside></div></div></div></div></div></div></div></div></body>
Elements Of Econometrics Kmenta Pdf Creator Online
As a textbook, Elements of Econometrics is intended for upper-level undergraduate and master's degree courses and may usefully serve as a supplement for traditional Ph.D. Courses in econometrics. Researchers in the social sciences will find it an invaluable reference tool. Cyberlink powerdvd exe download 11 ultra full keygen. NOTE: The solutions manual (Paper ISBN: 978-0-472-08476-0).
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