HalSnarr.comn 






A
government which robs Peter 

All
instructional videos that are available on The
Snarr Institute are organized below by course: Principles
of Macroeconomics, Business Quantitative Methods
(statistics), and Labor Economics.
An example is shown below. In this video, I analyze the economics of
Hurricane Katrina (at minute 0:00), math teacher shortages (15:20), minimum
wage hikes (22:30), immigration (26:50), and Lebron
James's pay (31:00).










minute 


top a 







Deriving
Big Mac demand 
1:40 





Deriving
oil supply 
12:10 





Law
of demand and supply 
23:54 










Economics
of Hurricane Katrina 
0:00 





Economics
of the math teacher shortage 
15:20 





Economics
of minimum wage hikes 
22:30 





Economics
of immigration 
26:50 





Economics
of Lebron James pay 
31:00 










Economics
of health care reform 
1:20 





Economics
of budget deficits 
14:00 





A
linear model of free trade 
26:10 









top a 







Consumer
Price Index (CPI) 
0:56 





Price
level or cost of the market basket 
3:35 





Inflation
rate 
11:10 





Cost
of living adjustment (COLA) 
16:34 





Real
price 
26:00 





Real
wage 
28:25 





Real
interest rate 
38:10 










Nominal
GDP 
0:23 





Real
GDP 
12:18 





Economic
growth rate 
15:24 





Stand
of living 
20:08 





Business
cycle 
23:15 





Determination
of GDP and price level 
24:20 





AD
fluctuations and changes in price level and GDP 
24:40 










Unemployment 
0:00 





Natural
rate of unemployment 
3:34 





High
unemployment in a recessionary gap 
9:43 





Low
unemployment in an inflationary gap 
12:22 





Phillips
Curve 
19:36 





Augmented
Phillips Curve and natural rate of unemployment 
21:32 









top a 







Snarrian Keynesian consumption 
7:24 





Snarrian consumption 
16:18 





iMport function 
20:22 





Snarrian aggregate expenditure 
23:21 





Keynesian
equilibrium 
29:20 





Government
spending multiplier 
34:10 





Tax
cut multiplier 
38:10 









top a 







Deriving
aggregate demand from aggregate expenditure 
0:00 





Government
spending stimulus 
9:23 





Tax
cut stimulus 
11:40 





Full
employment output and the economy's production function 
15:35 










Snarrian aggregate supply 
2:15 





Inflationary
gap 
14:04 





Recessionary
gap 
21:58 





Induced
inflationary gap 
29:00 









top a 







Budget
surplus/deficit 
0:00 





Financing
budget deficits with government bonds 
1:28 





Mock
Treasury auction 
4:27 





Keynesian
expansionary fiscal policy 
8:39 










Keynesian
expansionary fiscal policy (continued) 
0:00 





Keynesian
restrictive fiscal policy 
1:18 





Keynesian
intervention and the crowding out effect 
4:05 





New
classical view of fiscal policy 
4:53 





New
classical view of fiscal policy and the loanable funds market 
7:37 





Timing
of Keynesian fiscal policy 
9:24 










Timing
of Keynesian fiscal policy (continued) 
0:00 





Applying
the Augmented Phillips Curve 
6:18 





Automatic
stabilizers 
9:30 





A
modern synthesis of fiscal policy 
11:49 





Supplyside
fiscal policy 
12:20 










Laffer curve 
0:00 





Supplyside
tax cuts 
3:55 





Percent
of federal income tax paid by each income group 
6:33 





History
of US fiscal policy 
10:20 





Economic
growth vs. the growth rate of tax revenue 
12:38 





Fiscal
policy impotent? 
13:37 









top a 







Definition
and history of money (0:00), 
0:00 





from
the GOLD standard to the GOD standard (3:45), 
3:45 





money
demand and supply model (4:52) 
4:52 










Definition
of money 
0:00 





Hyperinflation 
2:17 


























hyperinflation
in Germany 
6:20 





hyperinflation
in Yugoslavia 
8:50 





Banks 
10:44 










Money
creation via increased bank lending 
0:00 





Money
destruction via decreased bank lending 
3:19 





The
Snarrian model of demand in the federal funds
market 
5:00 





A
numerical example of Snarrian demand in the federal
funds market 
11:57 










Supply
in the federal funds market 
0:00 





A
numerical example of supply 
3:45 





Federal
funds market equilibrium in normal mode 
4:49 





An
example of federal funds market equilibrium in normal mode 
6:40 





Raising
the discount rate in normal mode 
8:50 





Raising
the required reserve ratio & emergency mode 
10:16 





Raising
the required reserve ratio and its effect on money supply 
13:17 





Raising
the required reserve ratio and its effect on the ADAS model 
13:30 










Open
Market Purchase (OMP) 
0:00 





Economics
of an OMP in normal mode 
1:59 





The
effect of an OMP on money supply 
3:15 





The
effect of an OMP on the ADAS model 
3:38 





Applying
monetary policy using the Augmented Phillips Curve 
5:00 





Open
Market Sale, summarized 
6:32 





The
financial crisis, interest on reserves, and crisis mode 
7:43 





Unwinding
the Fed's balance sheet 
9:56 










Politicians
side with Keynes not freedom's friend, Hayek 
0:00 





A
tree analogy of Keynes vs. Hayek 
5:22 





The
forest management analogy of Keynes vs. Hayek 
6:35 





Economics
of the Keynes vs. Hayek debate 
7:26 





The
flat Keynesian BushObama recovery 
8:58 





The
Hayek trajectory that would not be 
9:38 





































minute 


top a 

0:00 
















Categorical
data 
0:00 





Frequency
distribution 
1:00 





Relative
frequency table 
2:50 





Percent
frequency 
4:28 





Bar
char 
4:39 





Pie
chart 
6:01 





Quantitative
data 
7:48 





Frequency
example 
8:20 





Histogram 
15:00 





Cumulative
frequency distribution 
18:39 





Ogive 
23:07 





Crosstabulation (or pivot) tables 
25:42 





Row
and column percentages of a crosstabulation table 
30:12 





Simpson's
Paradox 
36:40 





scatterplots 
48:13 









top a 







Introduction
to summarizing quantitative data 
0:00 





Population
mean 
3:00 





Sample
mean 
6:27 





Median 
10:09 





Mode 
14:32 





Percentiles 
17:05 





Quartiles 
21:36 










Variation
in data 
0:00 





Range 
2:18 





Interquartile
range 
2:42 





Population
variance 
3:46 





Biased
sample variance 
6:12 





Sample
variance 
7:43 





Population
vs sample standard deviation 
7:58 





Coefficient
of variation 
9:06 





Sample
standard deviation example 
10:46 










introduction
of distributional shape and correlation 
0:00 





zscore 
0:21 





Skewness
and histograms 
7:35 





Chebyshev's Theorem 
15:47 





Empirical
Rule 
17:21 





Outliers 
18:16 





Using
zscores to verify Chebyshev's Theorem or the
Empirical Rule 
19:28 





Correlation
and covariance 
22:18 





Population
covariance vs sample covariance 
23:09 





Scatterplot 
22:41 





Correlation 
28:54 









top a 







Definition
of probability 
0:00 





Experiment
and sample space 
2:07 





Product
rule 
5:05 





Tree
diagram 
6:54 





Counting
rule 1order matters, replacement 
10:13 





Counting
rule 2order does not matter, no replacement 
16:51 





Counting
rule 3order matters, no replacement 
20:59 





Assigning
probabilities 
23:52 





Classical
probabilities 
24:33 





Relative
frequency probabilities 
27:29 





Subjective
probabilities 
30:30 





Events 
36:14 










Probability
rules 
0:00 





Complement
rule 
1:19 





Intersection
of nonmutually exclusive events 
2:55 





Intersection
of mutually exclusive events 
4:41 





Union
of nonmutually exclusive events 
6:47 





Union
of mutually exclusive events 
9:13 





Conditional
probability 
11:27 





Intersection
of dependent events 
15:05 





Independent
events 
17:17 





Intersection
of independent events 
17:44 





Bayes'
Theorem and drug testing 
20:25 









top a 







Discrete
vs continuous random variables 
0:00 





Numerical
example of discrete PDF 
6:09 





Expected
value of discrete variable 
8:53 





Standard
deviation of discrete variable 
10:46 





Uniform
discrete PDF 
13:18 





Binomial
PDF 
19:48 










Numerical
example of Binomial PDF 
0:00 





Using
Binomial PDF to compute Pick Three Lottery 
11:58 





Expected
winnings of Pick Three Lottery 
13:37 





Poisson
PDF 
14:40 










Hypergeometric
vs Binomial 
0:00 





Hypergeometric
PDF 
0:58 





Numerical
example of Hypergeometric PDF 
3:25 





Hypergeometric
and PowerBall Lottery 
10:55 





Expected
winnings of PowerBall Lottery 
14:21 









top a 







Continuous
vs. Discrete Probability Distributions 
0:00 





Uniform
continuous distribution 
2:53 





Expected
value and variance of uniform continuous distribution 
4:49 





Normal
distribution 
8:18 





Changing
the mean of a normal distribution 
10:00 





Changing
the standard deviation of a normal distribution 
10:32 





Standard
normal distribution 
11:07 





Verifying
the Empirical Rule 
12:42 





Using
the standard normal table to find probabilities 
13:41 





Using
the standard normal table to find z values 
27:43 





Standardizing
nonstandard normal distributions 
31:48 





Numerical
example of nonstandard normal distribution 
32:39 





Exponential
probability distribution 
39:31 









top a 







Difference
between a distribution and sampling distribution 
0:00 





Sampling
error 
1:18 





Example:
census versus a sample 
2:25 





Population
mean and proportion 
3:24 





Population
standard deviation 
4:51 





Simple
sampling 
7:33 





Sample
mean and proportion 
11:31 





Sample
standard deviation 
12:33 





Comparing
population parameters and sample statistics 
15:12 





Sampling
distribution of the mean 
17:03 





Standard
error of the mean 
17:54 





Normality
of the sampling distribution of the mean 
19:10 





Numerical
example of sampling distribution 
19:50 





Numerical
example of the Law of Large Numbers 
26:01 





Sampling
distribution of the proportion 
28:27 





Intuition
of the standard error of the proportion 
29:12 





Normality
of the sampling distribution of the proportion 
34:27 





Numerical
example of sampling distribution of the proportion 
34:54 









top a 







Intuition
of confidence interval 
0:00 





Interval
Estimate (IE) of mu with sigma known 
2:58 





Normality
conditions of IE with sigma known 
11:55 





Numerical
example of IE with sigma known 
12:36 





The
sigma unknown case 
16:01 





t
distribution 
16:45 





t
and standard normal are identical for large samples 
17:52 





Finding
t values in the t table 
19:22 





IE
of mu with sigma unknown 
21:31 





Numerical
example of IE with sigma unknown 
30:03 





Conditions
needed to use tdistribution 
32:19 





Necessary
sample size for IE of mu 
33:29 





IE
of the proportion 
36:46 





Normality
condition 
37:02 





Example
of IE of proportion 
38:34 





Necessary
sample size for IE of proportion 
40:39 









top a 







Definition
of null and alternative hypotheses 
2:34 





Example
of developing null and alternative hypotheses 
4:40 





Type
I and II errors 
6:54 





Onetail
test with known variance 
8:56 





Determining
the p value with known variance 
11:28 










Determining
the p value (continued) 
0:00 





Determining
z critical values with known variance 
4:23 










One
tail hypothesis test involving one mean with known variance 
0:00 





Two
tail hypothesis test involving one mean with known variance 
4:12 










Hypothesis
testing with unknown variance 
0:00 





A numerical
example of hypothesis testing with unknown variance 
2:58 





A
numerical example of a two tail test with unknown variance 
6:50 










Hypothesis
testing with unknown variance 
0:00 





Hypothesis
tests about a population proportion 
4:27 





A numerical
example of a hypothesis tests about a population proportion 
5:56 








top a 







The
sampling distribution of the mean 
2:18 





The
sampling distribution of the differences in means 
4:08 





Confidence
interval for the difference in means 
4:40 





A
numerical example with known variances 
6:48 










One
tail test with known populations variances 
0:00 





Deriving
the test statistic from the confidence interval 
3:08 





A
numerical example of a one tail test, known variances 
5:12 





Testing
when population, unknown variances 
10:30 





A
numerical example a confidence interval, unknown variances 
12:27 










Example
a confidence interval, unknown variances (continued) 
0:00 





Example
hypothesis testing, unknown variances 
5:07 





Example
matched samples, unknown variances 
9:33 










Example
matched samples, unknown variances (continued) 
0:00 





Sampling
distribution of a difference in proportions 
5:52 





Example
of interval estimate of a difference in proportions 
8:25 










Example
one tail test involving two proportions 
0:00 





Example
two tail test involving two proportions 
4:53 









top a 

Chapter 11 (1 of 3)—Hypothesis testing involving one
and two variances 






Sampling
distribution of chisquare statistic 
0:00 





Finding
chisquare critical values 
6:18 





Deriving
the interval estimate of the population variance 
10:02 





Example
of confidence interval of the population variance 
12:31 




Chapter
11 (2 of 3)—Hypothesis testing involving one and two variances 






Example
of confidence interval of the population variance (continued) 
0:00 





Hypothesis
tests about a population variance 
4:43 





Example
of one tail test of population variance 
5:28 





Hypothesis
tests about two population variance 
8:34 





Deriving
Fstat 
8:57 




Chapter
11 (3 of 3)—Hypothesis testing involving one and two variances 






Example
of test involving two population variances 
0:00 









top a 







Goodness
of fit test 
0:00 





Independence
tests 
8:00 










Independence
tests  continued 
0:00 





Goodness
of fit test for Normal Distribution 
8:14 










Example:
Goodness of fit test for Normal Distribution 
0:00 










Example:
Goodness of fit test for Poisson Distribution 
0:00 

















Use
ANOVA to test equality of three or more means 
0:00 





Completely
randomized design 
5:02 










Completely
randomized block design (continued) 
0:00 





Example
of completely randomized block design 
6:30 










Example
of completely randomized block design (continued) 
0:00 





Randomized
block design 
1:14 









top a 







Regression
equation vs. estimated regression equation 
0:00 





Residual
versus error 
6:50 





Least
squares method 
12:21 





Slope
coefficient vs. correlation 
13:33 





Shortcut
to compute slope coefficient 
14:15 










Simple
regression example 
0:00 





Sample
means 
0:52 





Sample
variances 
1:19 





Covariance 
5:40 





Compute
coefficient b_{1} 
7:03 





Compute
the intercept 
7:29 





Forming
the predicted equation 
8:09 





SST,
SSR, SSE, R square, and correlation 
10:37 










Scatterplot
y vs. x 
0:00 





Graphing
the predicted equation 
0:38 





Computing
R square, SST, SSE, and SSR 
2:54 





Interpreting
the R square 
11:19 





Square
root of R square equals the correlation 
11:58 










Assumptions
of the error 
0:00 





Verifying
the assumptions 
3:07 





Computing
predicted values and residuals 
5:00 





Standard
deviation of the residuals 
6:26 





Leverage 
7:23 





Standardized
residual plot 
13:19 










Testing
for significance 
0:00 





Distribution
of coefficient b_{1} 
0:59 





Coefficient
significance test 
3:07 





Interpreting
coefficient b1  t test 
4:05 





Test
of model significance  F test 
5:05 





ANOVA
table 
7:32 




Prediction
and confidence intervals 
8:38 







top a 







Definition
of multiple regression 0:00 
0:00 





Research
question and specifying the model 3:35 
3:35 





Building
testable hypotheses using theory in graphs 6:33 
6:33 





Building
testable hypotheses using constrained utility optimization 
11:45 










Descriptive
statistics 
0:00 





Scatterplots
of y versus the independent variables 
8:42 





Correlations
& multicollinearity versus omitted variable
bias 
10:11 





Computing
regression coefficients using sample statistics 
13:39 










Computing
regression coefficients using sample statistics 
0:00 





Predicted
equation, yhat, residuals, SSE, SSR, & SST 
2:03 





Coefficient
significance test 
5:49 





Omitted
variable bias in simple regression 
8:57 





Interpreting
coefficient 
9:42 





egression
output table 
12:12 





Interpreting
Rsquare 
14:12 










Computing
regression coefficients using sample statistics 
0:00 





Predicted
equation, yhats, residuals, SSE, SSR, and SST 
3:01 





Coefficient
significance test 
7:55 





Interpreting
the coefficient of a logged independent variable 
10:51 





Omitted
variable bias in simple regression 
14:26 










Omitted
variable bias in simple regression (continued) 
0:00 





Why
multicollinearity presents a problem for Excel 
2:40 





Reducing
omitted variable bias 
3:41 





Forming
the predicted equation 
7:22 





Error
assumptions 
8:33 





Residuals
are estimated errors and verifying the assumptions 
9:00 





Test
for zero mean of the error 
11:00 










Testing
for heteroscedasticity 
0:00 





Testing
for normality 
6:42 










Testing
for normally (continued) 
0:00 





Autocorrelation
test 
5:10 





Testing
for linearity 
9:43 





F
test of model significance 
11:53 










F
test of model significance (continued) 
0:00 





Coefficient
significance tests 
4:04 





Interpreting
coefficients of variables and logged variables 
9:08 









top a 







Descriptive
statistics & scatterplots of the variables 
0:00 










Using
Excel's regression package to estimate a linear equation 
0:00 





Forming
the predicted equation 
2:20 





Computing
predicted values and residuals by hand 
3:01 





Interpreting
R square 
5:03 





Testing
coefficients for significance 
6:00 





Testing
for model significance 
7:12 





Residuals
are used to test the error assumptions 
7:39 










Test
errors have a mean equal to zero (0:00 
0:00 





Test
linearity of model 
0:51 










Testing
errors have constant variance (homoscedasticity) 
0:00 





White's
test 
2:43 










Test
normality of the errors 
0:00 










Testing
autocorrelation in the errors 
0:00 










Testing
for significance in the coefficients and model 
0:00 





Interpreting
the coefficients 
4:20 























minute 


top a 







Labor
market participants 
0:00 





Why
is there a shortage of high school mathematics teachers? 
1:12 





Minimum
wage hikes and unemployment 
10:30 





Immigration
and its affect on wages 
18:44 





Using
multiple regression to confirm theory 
30:18 





Model
specification 
32:34 





Using
economic theory in graphs to build a testable hypothesis 
35:05 





Using
economic theory and calculus to build a testable hypothesis 
41:49 





Summarizing
data 
45:05 





Correlations
and multicollinearity 
52:57 





Log
variable transformation 
56:03 





Ordinary
least squares 
57:20 










Excel's
simple regression output 
0:00 





Interpreting
the R square in simple regression 
0:38 





Omitted
variable bias 
1:53 





Excel's
multiple regression output 
2:40 





Interpreting
the R square in multiple regression 
3:03 





Forming
the predicted equation 
4:10 





Computing
residuals 
5:40 





Heteroskedasticity 
9:56 





Autocorrelation 
16:20 





Linearity 
21:48 





Normality 
24:18 





Test
of model significance 
31:08 





Test
of coefficient significance 
34:04 





Coefficient
interpretation 
42:22 









top a 







Utility:
more consumption and leisure is better 
0:00 





Law
of Diminishing Marginal Utility 
1:41 





Solving
for C yields the indifference curve 
5:08 





Graphing
indifference curve U = 10 
6:40 





Graphing
indifference curve U = 20 
7:45 





Budget
line constructed 
9:13 





Graphing
the budget line 
11:30 





Shifting
the budget line after nonearned income 
14:17 





Rotating
the budget line after wages rise 
15:18 





Utility
maximization 
16:41 





Utility
max after an increase in nonearned income 
19:40 





Utility
max with increase in wage and substitution effect dominant 
22:04 





Utility
maximization with increase in wage and income effect dominant 
23:36 





Income
effect dominates 
24:24 





Substitution
effect dominates 
27:06 





Reservation
wage 
28:29 





Backward
bending labor supply and labor supply elasticity 
33:21 





Application:
the benefit reduction rate 
39:05 





Application:
Earned Income Tax Credit 
49:46 









top a 







Long
run production function 
0:00 





Short
run production function 
1:27 





Law
of Diminishing Marginal Productivity 
3:48 





Graphing
total and marginal product of labor 
5:56 





Short
run output, expenses, revenue, and profit equations 
7:20 





Short
run profit maximization rule using graphs 
10:14 





Short
run profit maximization rule using calculus 
12:00 





Short
run labor demand equation 
16:11 





Graphing
the labor demand equation 
17:03 





Shifting
short run labor demand 
19:15 





Short
run profit maximization rule 
21:54 





Long
run cost minimization 
22:40 





Solving
long run production for K yields the isoquant 
22:56 





Graphing
isoquants 
24:09 





Isocost 
25:53 





Graphing
isocost 
26:54 





Rising
cost shifts isocost 
28:03 





Rising
price of K rotates isocost 
29:01 





Rising
wage rotates isocost 
31:19 





Long
run cost minimization 
33:15 





Long
run labor demand and the output effect of falling wage 
38:41 





Scale
and substitution effects 
42:28 





Elasticity
of substitution and curvature of the isoquant 
45:57 





Application:
the free market punishes firms that discriminate 
47:00 





Application:
affirmative action can work 
48:06 





Application:
affirmative action can put firms out of business 
49:29 





Perfect
substitutes and perfect complements in production 
54:48 









top a 







Competitive
labor market equilibrium 
0:00 





Minimum
wage creates unemployment 
2:18 





Cobweb
web model 
5:13 





Long
run dynamics and data observation 
13:33 





Pareto
efficient 
17:17 





Producer
surplus 
19:32 





Worker
surplus 
20:31 





The
competitive market maximizes total surplus 
21:10 





Payroll
taxes assessed on firms 
22:09 





Payroll
taxes assessed on employees 
28:55 





Payroll
taxes assessed on firms and employees 
34:22 





Payroll
taxes assessed on firms with inelastic labor supply 
37:39 





Employment
subsidies paid to firms 
40:46 










Immigration
equalizes wages in two different regions 
0:00 





Evidence
of wage equalization across regions 
6:26 





Immigrants
and natives are perfect substitutes 
9:39 





Immigrants
and natives are perfect complements 
14:07 





Evidence
of wage being associated with immigration 
15:41 





Immigration
is Pareto efficient 
18:40 





Perfectly
discriminating monopsonist 
20:46 





Perfectly
nondiscriminating monopsonist 
25:35 





Perfectly
nondiscriminating monopolist 
29:10 






























































































































