Do the following data sets satisfy GARP? (P1, P2) (21, 22) (1,1) (15,15) (2,1) (10,20) (1,2) (20,10) (2,5) (30,12) (5,2) |(12,30)

(e) Suppose you have been given the following ordinary least squares (OLS) regression result. Estimated Long Run Coefficients using the ARDL Approach ARDL (1,2,2,2,0,2) selected based on Akaike Information Criterion Dependent variable is LY 33 observations used for estimation from 1987 to 2019 T-Ratio [Prob.] 4.6671[0.000] 4.6678[0.051] 7.9897[0.043] -4.802[0.009] 2.3898[0.028] 1.0498[0.308] Regressor Coefficient Standard Error 0.36068 0.45447 LK 0.077280 LM 0.097363 0.48751 -0.41208 0.19057 0.52521 LE 0.061017 LF 0.085800 LT 0.079744 C 0.500320 where, Y = Economic growth K = Capital M = Employment E = Electricity consumption F = Foreign direct investment T= Technology (i) Write the regression equation. Interpret the estimated coefficients. (ii) Which explanatory variables are significant at the 1%, 5% and 10% level? Which variables are insignificant? Briefly explain.

You are given the following data: The regression equation is: A. -0.66 B. -1.20 (X'X)*¹ C. 1.12 O D. 2.06 = 1.3 2.1 0.8 -1.4 1.9 2.1 -1.4 s² = 0.86. T = 103 The correlation between ₁ and 3 (i.e., corr(Â₁, Â3)) is: -1.6] 1.9 (X'y) = 2.9 3.4 0.8 Yt = B₁ + B₂X2+ + B3X3t + Ut.

You estimated a regression with the following output.         Source |       SS           df       MS      Number of obs   =       268 -------------+----------------------------------   F(1, 266)       =     23.48        Model |  668419.175         1  668419.175   Prob > F        =    0.0000     Residual |  7572666.51       266  28468.6711   R-squared       =    0.0811 -------------+----------------------------------   Adj R-squared   =    0.0777        Total |  8241085.68       267  30865.4895   Root MSE        =    168.73   ------------------------------------------------------------------------------            Y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval] -------------+----------------------------------------------------------------            X |   1.014128   .2092916     4.85   0.000     .6020489    1.426207        _cons |   9.173163   21.13463     0.43   0.665    -32.43929    50.78561…

In the model Y = Bo +B 1X 1 + B 2X 2 + 8, which of these parameters represents a coefficient of an independent variable? the Y the X1 the B1 the e

Based on the following data, estimate the slope of the equation using OLS (3 significant digits in final answer): K=ẞ1+ B2J+ε Observation. 1 2 3 U 2 4 9 K 16 15 14 Answer:

You estimated a regression with the following output.         Source |       SS           df       MS      Number of obs   =       223 -------------+----------------------------------   F(1, 221)       =  17592.99        Model |   182392130         1   182392130   Prob > F        =    0.0000     Residual |  2291176.96       221  10367.3166   R-squared       =    0.9876 -------------+----------------------------------   Adj R-squared   =    0.9875        Total |   184683307       222  831906.786   Root MSE        =    101.82   ------------------------------------------------------------------------------            Y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval] -------------+----------------------------------------------------------------            X |   11.97037   .0902481   132.64   0.000     11.79252    12.14823        _cons |   74.40159   10.96696     6.78   0.000     52.78839    96.01479…

The models below have been estimated using monthly data for the period 2000: 1-2020: 12. The natural logarithm of the variables used in this model is taken and the transaction is made. D08 is the dummy variable that takes a value of 1 in 2008 and after and 0 in other periods. s.e. indicates the standard error. Comment on the coefficients of variables D08 and (X2t * D08) in the model (3) above.

A researcher investigating whether government expenditure crowds out investment estimates a regression on data for 30 countries. I-investment; G-government recurrent expenditure; Y=gross domestic product; all measured in $US billion. P= population measured in million. Standard errors are in parentheses. Î= 18.10 (7.79) R² = 0.99 1.07G + 36Y (0.14) (0.02) She suspects that countries with higher GDP may have more variability in their investment. She sorts the observations by increasing size of gdp per capita (Y)and estimates the regression again for the 11 countries with the lowest gdp(Y)and the 11 countries with the largest gdp(Y). The RSS1 from the first regression is 7186. The RSS2 from the second regresison is 28101. Perform a Goldfeld-Quandt Test at a 5% significance level. a. The test statistic for this test is 0.256 b. The critical value defining the rejection region for Ho is 3.18 c. Is there heterscedasticity? Yes=1 or No-0. The answer is 0

Bureau of Economic Analysis in the USA is responsible for construction and maintenance of national income and product accounts (NIPA). Measurement began in the 1930s due to frustration of Roosevelt and Hoover trying to design policies to combat the Great Depression. Simon Kuznets (Nobel laureate) was commissioned to develop initial methodology and estimates. In 1947, the process became much more consistent. Methodologies have frequently been changed (improved?) as a result of advances in economics, accounting, and data collection. Past data are then revised to reflect new definitions. On the following information, calculate GNP at factor cost. Whether GNPFC derive from income method equivalent to expenditure method? S.No. Items Rs. (In Crores) 1 Private final consumption expenditure 1000 2 Net domestic capital formation (Investment) 200 3 Profit 400 4 Compensation of employers (Wages and salaries) 800 5 Rent 250 6 Government final consumption expenditure…