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Econometrics > Chapter 1 > Flashcards

Chapter 1 Flashcards

1
Q

What is empirical research about?

A
  1. Identify problems - not taking everything as given
  2. Think in systems and disentangle potential mechanisms
  3. Operationalize test settings to generate reliable evidence
  4. Evaluate results in order to generate predictions and recommendations
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2
Q

What is the research process?

A
  1. Formulate a research questions
  2. Literature review
  3. Develop a theory
  4. Formulate hypotheses
  5. Design empirical tests for the hypothesis testing
  6. Interpret results
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3
Q

What is the formal definition of theory?

A

A logically coherent set of ideas that can explain and predict empirical observations.

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4
Q

What does a theory need in order for it not to be only descriptive?

A

justification

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5
Q

What is the definition of econometrics?

A

The use of statistical methods to analyze economic data.

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6
Q

What data is used in Economics?

A
  • non-experimental or observation
  • it’s rare to have experimental data
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7
Q

What is important to apply to real world data?

A

economic theory

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8
Q

What are the goals of econometrics?

A
  1. Estimting relationships
  2. Testing economic theories
  3. Forecasting economic variables
  4. Evaluating and implementing
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9
Q

What are the steps in econometric analysis?

A
  1. Economic model
  2. Econometric model
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10
Q

What is the role of the Economic model?

A

formulate qusetion of interest

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11
Q

What is the role of the Econometric model?

A

operationalize the economic model

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12
Q

Economic vs Econometric model: Table

A
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13
Q

With what specification do most econometrics deal?

A

with the specification of the error u

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14
Q

What is the formula for wage?

A
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15
Q

What are types of Data?

A
  1. Cross-section data
  2. Time series data
  3. Pooled cross sections
  4. Panel/longitudinal data
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16
Q

On what do econometrics methods depend?

A

on the nature of the data used
- may lead to misleading results

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17
Q

Decribe Cross-section data.

A
  • samples of different units at a given point of time/given period
  • cross-sectional observations more or less independent

example: pure random sampling - if not random sample, sample-selection problem

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18
Q

Decribe Time Series data.

A
  • observations of a variable or several over time
  • typically serially correlated
  • ordering of observations conveys important information (lead/lag)
  • since not a random set, different problems to consider
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19
Q

Where is Cross-section data typically encountered in?

A
  • accounting
  • finance
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20
Q

Where is Time Series data typically encountered in?

A
  • trends
  • seasonality
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21
Q

Into what are two or more cross sections combined in?

A

one data set

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22
Q

How are cross sections drawn in rapport to each other?

A

independently

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23
Q

When are Pool crossed sections often used?

A

to evaluate policy changes

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24
Q

Describe Panel or longitudinal data.

A
  • same cross-sectional units are followed over time
  • panel daa have a cross-sectional and a time series dimension
  • panel data can be used to account for time-invariant unobservables
  • panel data can be used to model lagged responses
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25
Q

In what situations is panel or longitudinal data used?

A

city crime statistics, observed in two years

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26
Q

What is a Construct?

A

abstract idea which is not directly observable and should be operationalized for empirical testing

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27
Q

What is a Variable?

A

observable item which can assume different values and is used to measure a theoretical construct

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28
Q

What is the contruct validity?

A

degree to which a measurement/variable captures the underlying theoretical construct it is supposed to measure

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29
Q

Define internal validity.

A

degree of condfidence that the causal relationship being tested is trustworthy and not influenced by other factors

30
Q

Define external validity.

A

extent to which results from a study can be applied to other situations

31
Q

What is the construct reliability?

A

degree to which a measurement provides estimates which are consistent

32
Q

What is the role of the random variable?

A

assings unique to the outcome of a random experiment

33
Q

What are the types of Quantitative variables?

A
  1. Discrete random variable
  2. Continuous random variable
34
Q

What is the discrete random variable?

A

random variable with specific set of possible values (ie dice)

35
Q

Is the continuous random variable discreet?

A

no (range of possible values)

36
Q

What is the role of the qualitative (categorical) variable?

A

take category or label values and place a unit of observations into one of several categories (mutually exclusive) groups (nationality ie)

37
Q

What describes probability of observing specific values?

A

Distribution

38
Q

What can characterize normal distribution?

A
  1. mean
  2. variance/standard deviation
39
Q

Formula Variance

A
40
Q

Formula Standard deviation

A

square root of Var(X)

41
Q

List the stages of the basic process of empirical research

A
  • population (parameters)
  • producing data/sampling
  • sample/data
  • data analysis (statistics)
  • probability?
    + interference
    — all over again
42
Q

What is empirical research?

A

usage of statistics generated from a sample to make inferences about the parameters of the population

43
Q

Define Population

A

all individuals/research subjects that are of interest to a researcherq

44
Q

Define Sample

A

a subset of the population

45
Q

On what do theoretical properties of statistical estimators usually rest?

A

on the assumption of random samples

46
Q

What is the sample of selection bias?

A

bias resulting from the non-random sampling of a set of observations from the population, where the probability of the oservation being included in the sample potentially affects the observed results in the sample

47
Q

Sample mean formula

A
48
Q

What is the law of large number?

A

actual probability of an event is estimated by relative frequency with which the event occurs in a long series of trials

49
Q

What do sample averages converge to as the sample size increases, and what’s the result?

A

to the population mean nu (with probability 1)

result: estimates get more accurate

50
Q

What is the mean of all samples?

A
51
Q

What is the standard deviantion of all sample means Xi?

A
52
Q

What does the standard deviation of sample means do as sample size increases?

A

it decreases
(law of large numbers)

53
Q

How will sample of means approximately be?

A

normal
- even if the distribution is not normal
(central limit theorem)

54
Q

How can we infer mean of population by calculating mean from a random sample?

A

either:
- increasing sample size
- repeatedly taking random samples and averaging sample statistics

55
Q

What do we use to determine accuracy?

A

confidence interval

56
Q

Formula confidence interval

A
57
Q

On what does the Z multiplier depend?

A

on the desired level of confidence (comes from normal distribution)

58
Q

Formula confidence interval if sigma and population mean are unknown

A
59
Q

Formula standard error

A
60
Q

When do multipliers become nearly the same?

A

under normal distribution for large samples

61
Q

What are t-statistics?

A

for large samples over 500, multipliers for the confidence interval become nearly the same as under normal distribution

62
Q

When is t-distribution used?

A

when construction confidence intervals for the population mean, especially when sample is small or standard deviation is unknown

63
Q

When do we usually reject H0?

A

if p-value is higher than a significant level a

64
Q

Summary of testing strategy on the mean of a variable x

A
  1. Draw sample of n observations from population
  2. Calculate sample statistic X
  3. Calculate standard deviation error of X: s/root(n)
  4. Calculate test statistic that measure deviation of C from u0 in terms of standard deviation error
  5. If statistic is larger/smaller than critical values of corresponding t-distribution with -1 degrees of freedom, accept the hypothesis
65
Q

What are the two types of errors?

A

Type 1: rejecting a null hypothesis that is actually true
Type 2: accepting a null hypothesis that is actually false

66
Q

H0 rejected/not rejected
H0 false/true
TABLE

A
67
Q

Size of a test:

A

probability of falsely rejecting the null hypothesis when it’s actually correct

68
Q

Power of a test:

A

probability of correctly rejecting the null hypothesis when it is false

69
Q

What is alpha?

A

likelihood of a type 1 error <8reejcting a true H0)

70
Q

What is beta?

A

likelihood of a type 2 error (accepting a wrong H0)

71
Q
A

Econometrics (2 decks)

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