# 应用计量经济学代写APPLIED ECONOMETRICS|ECO-7001B University of East Anglia Assignment

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Assignment-daixieTM为您提供东英吉利大学University of East Anglia ECO-7001B APPLIED ECONOMETRICS应用计量经济学代写代考辅导服务！

## Instructions:

Applied econometrics is the application of statistical and mathematical methods to analyze economic data. Econometrics is a branch of economics that uses statistical methods to test economic theories and forecast economic trends. Applied econometrics applies these techniques to real-world data sets to answer questions related to economic policy, market analysis, and business decisions.

Applied econometrics involves the use of mathematical and statistical models to analyze economic data. These models can be used to test economic theories, estimate the impact of policy changes, and forecast economic outcomes. Econometricians use a variety of statistical techniques, including regression analysis, time-series analysis, and panel data analysis, to analyze economic data.

Applied econometrics is used in a wide range of fields, including finance, marketing, public policy, and international trade. For example, an econometrician might use data on consumer behavior to estimate the impact of a new advertising campaign on sales. Or, they might use data on economic growth to forecast the impact of a new trade agreement.

Overall, applied econometrics provides a valuable tool for economists and other researchers to analyze economic data and answer important questions about the economy.

Consider $E[Y \mid X]$ where $X$ is a dummy variable that equals one with probability $p$ and is zero otherwise. Prove that the CEF and the regression of $\mathrm{Y}$ on $\mathrm{X}$ are the same in this case. Do this by showing that for Bernoulli $\mathrm{X}$ : \begin{aligned} & \alpha=\mathrm{E}(\mathrm{Y})-\beta \mathrm{E}(\mathrm{X})=\mathrm{E}[\mathrm{Y} \mid \mathrm{X}=0] \\ & \beta=\mathrm{COV}(\mathrm{X}, \mathrm{Y}) / \mathrm{V}(\mathrm{X})=(\mathrm{E}[\mathrm{Y} \mid \mathrm{X}=1]-\mathrm{E}[\mathrm{Y} \mid \mathrm{X}=0]) \end{aligned}

First, we note that for a Bernoulli $X$, we have $\mathrm{E}(X) = p$ and $\mathrm{Var}(X) = p(1-p)$.

The conditional expectation of $Y$ given $X$ is given by:

\mathrm{E}(Y \mid X)= \begin{cases}\mathrm{E}(Y \mid X=1) & \text { if } X=1 \ \mathrm{E}(Y \mid X=0) & \text { if } X=0\end{cases}

We can write the conditional expectation of $Y$ as a linear function of $X$ as follows:

$\mathrm{E}(Y \mid X)=\alpha+\beta X$

where $\alpha = \mathrm{E}(Y) – \beta \mathrm{E}(X)$ and $\beta = \mathrm{Cov}(X, Y)/\mathrm{Var}(X)$.

To see that the CEF and the regression of $Y$ on $X$ are the same, we need to show that $\alpha = \mathrm{E}(Y \mid X=0)$ and $\beta = \mathrm{E}(Y \mid X=1) – \mathrm{E}(Y \mid X=0)$.

To see that $\alpha = \mathrm{E}(Y \mid X=0)$, we note that when $X=0$, the conditional expectation of $Y$ is given by $\mathrm{E}(Y \mid X=0)$. Therefore, we have:

$\alpha+\beta \times 0=\mathrm{E}(Y \mid X=0)$

which implies that $\alpha = \mathrm{E}(Y \mid X=0)$.

To see that $\beta = \mathrm{E}(Y \mid X=1) – \mathrm{E}(Y \mid X=0)$, we note that:

\begin{aligned} & \mathrm{E}(Y \mid X=1)=\alpha+\beta \times 1 \ & \mathrm{E}(Y \mid X=0)=\alpha+\beta \times 0\end{aligned}

Subtracting the second equation from the first, we obtain:

$\mathrm{E}(Y \mid X=1)-\mathrm{E}(Y \mid X=0)=\beta \times 1-\beta \times 0=\beta$

which implies that $\beta = \mathrm{E}(Y \mid X=1) – \mathrm{E}(Y \mid X=0)$.

Thus, we have shown that the CEF and the regression of $Y$ on $X$ are the same for a Bernoulli $X$.

Re-estimate the model in 2a, allowing the relationship between wages and schooling to differ by race.
Construct an F-test to test the null hypothesis that the returns to an additional year of schooling are the
same for all races.

To estimate the relationship between wages and schooling by race, we can add an interaction term between race and schooling in the regression model in 2a. The model can be written as follows:

$\ln ($ wage $)=\beta 0+\beta 1$ educ $+\beta 2$ black $+\beta 3^{\star}$ (educ*black $)+\varepsilon$

where black is an indicator variable equal to 1 if the person is black and 0 otherwise, and ε is the error term.

To test the null hypothesis that the returns to an additional year of schooling are the same for all races, we can construct an F-test for the joint significance of the interaction term (educ*black) and the black dummy variable. The null hypothesis is that β3 = 0, which implies that the returns to an additional year of schooling are the same for blacks and non-blacks. The alternative hypothesis is that β3 is not equal to zero, which implies that the returns to an additional year of schooling differ by race.

To conduct the F-test, we first estimate the restricted model, which does not include the interaction term:

$\ln ($ wage $)=\beta 0+\beta 1$ educ $+\beta$ bblack $+\varepsilon$

We then estimate the unrestricted model, which includes the interaction term:

$\ln ($ wage $)=\beta 0+\beta 1$ educ $+\beta$ 2black $+\beta 3^{\star}($ educ*black $)+\varepsilon$

We can then calculate the F-statistic using the following formula:

$F=((R S S R-R S S U R) / q) /(R S S U R /(n-k-1))$

where RSSR is the residual sum of squares from the restricted model, RSSUR is the residual sum of squares from the unrestricted model, q is the number of restrictions (in this case, q = 1), n is the sample size, and k is the number of parameters estimated in the unrestricted model.

If the calculated F-value is greater than the critical value of the F-distribution with q and n-k-1 degrees of freedom at the desired significance level, we reject the null hypothesis and conclude that the returns to an additional year of schooling differ by race.

Note that this F-test assumes that the error variance is constant across race groups. If this assumption is violated, the test may not be valid and alternative methods may need to be used.

Re-estimate the model in 2a, with a full set of interactions between race and sex. How many new
hourly wage in each race-sex category for a 25-year-old worker with 12 years of schooling.

To estimate a model with a full set of interactions between race and sex, we need to add six new variables:

• Black_female
• Black_male
• Hispanic_female
• Hispanic_male
• Other_female
• Other_male

The regression model is:

$log(wage) = \beta_0 + \beta_1 educ + \beta_2 female + \beta_3 black + \beta_4 hispanic + \beta_5 other + \beta_6 female \times black + \beta_7 female \times hispanic + \beta_8 female \times other + \beta_9 black \times hispanic + \beta_{10} black \times other + \beta_{11} hispanic \times other + \epsilon$

where:

• educ: years of education
• female: 1 if female, 0 if male
• black: 1 if black, 0 otherwise
• hispanic: 1 if Hispanic, 0 otherwise
• other: 1 if other race, 0 otherwise

To calculate the expected log hourly wage for a 25-year-old worker with 12 years of schooling in each race-sex category, we can use the following formulas:

• Black female: $\hat{y} = \beta_0 + \beta_1(12) + \beta_2(1) + \beta_3(1) + \beta_6(1) = (\beta_0 + \beta_3 + \beta_6) + \beta_1(12) + \beta_2$
• Black male: $\hat{y} = \beta_0 + \beta_1(12) + \beta_3(1) = (\beta_0 + \beta_3) + \beta_1(12)$
• Hispanic female: $\hat{y} = \beta_0 + \beta_1(12) + \beta_2(1) + \beta_4(1) + \beta_7(1) = (\beta_0 + \beta_4 + \beta_7) + \beta_1(12) + \beta_2$
• Hispanic male: $\hat{y} = \beta_0 + \beta_1(12) + \beta_4(1) = (\beta_0 + \beta_4) + \beta_1(12)$
• Other female: $\hat{y} = \beta_0 + \beta_1(12) + \beta_2(1) + \beta_5(1) + \beta_8(1) = (\beta_0 + \beta_5 + \beta_8) + \beta_1(12) + \beta_2$
• Other male: $\hat{y} = \beta_0 + \beta_1(12) + \beta_5(1) = (\beta_0 + \beta_5) + \beta_1(12)$

Note that in each formula, we use the coefficients obtained from the regression model with full interactions. We plug in the value of 12 for educ and 1 for the relevant race-sex indicator variables.

We cannot provide exact values for these expected log hourly wages without the coefficients from the regression model, but this is the general process for calculating them.

# 商业决策的数据分析代写Data Analytics for Business Decision Making|ECN4003 University of Plymouth Assignment

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Assignment-daixieTM为您提供普利茅斯大学University of Plymouth ECN4003 Data Analytics for Business Decision Making商业决策的数据分析代写代考辅导服务！

## Instructions:

Data analytics is the process of examining large and complex data sets to extract meaningful insights that can inform business decisions. Data analytics involves using various techniques such as statistical analysis, machine learning, and data visualization to analyze data and identify patterns, correlations, and trends. The insights obtained from data analytics can help businesses make more informed decisions, identify opportunities for growth, and optimize operations.

Here are some ways that data analytics can be used for business decision making:

1. Market research: Data analytics can be used to gather and analyze data on consumer behavior, preferences, and trends. This information can be used to develop marketing strategies and product offerings that better align with customer needs and desires.
2. Performance tracking: By analyzing key performance indicators (KPIs), such as sales revenue, customer satisfaction, and employee productivity, businesses can identify areas of strength and weakness in their operations. This information can be used to make data-driven decisions that optimize performance and increase profitability.
3. Risk management: Data analytics can be used to identify potential risks and vulnerabilities in a business’s operations, such as fraud, security breaches, or supply chain disruptions. By monitoring and analyzing data in real-time, businesses can detect potential issues early on and take proactive steps to mitigate risk.
4. Financial analysis: Data analytics can be used to analyze financial data, such as cash flow, revenue, and expenses, to identify trends and patterns that can inform financial decision making. This information can be used to optimize cash flow, reduce expenses, and improve profitability.
5. Resource allocation: By analyzing data on resource usage and performance, businesses can make informed decisions about how to allocate resources such as time, money, and personnel. This can help optimize operations, reduce waste, and increase efficiency.

Overall, data analytics is a powerful tool that can help businesses make better decisions and stay ahead of the competition. By leveraging data analytics, businesses can gain valuable insights that inform strategy, optimize operations, and drive growth.

Price a lookback option with payoff at $t=3$ equal to $\left(\max _{0 \leq t \leq 3} S_t\right)-S_3$ using risk-neutral probability.

The following binomial tree describes the evolution of stock price and the
bold-face numbers next to the final stock price are the payoffs from the lookback option:

Recalling from part (a) that the risk-neutral probabilities of an up-movement and a down-movement are both $\frac{1}{2}$, all terminal nodes have the same Q-probability of $\left(\frac{1}{2}\right)^3=$ $\frac{1}{8}$. Therefore the price of the lookback option is given by
\begin{aligned} C & =\left(\frac{1}{1+r}\right)^3 E^Q\left[D_3\right] \ & =\left(\frac{4}{5}\right)^3 \cdot \frac{1}{8}\left(8+6+2+2+\frac{7}{2}\right) \ & =\frac{64}{125} \cdot \frac{1}{8} \cdot \frac{43}{2} \ & =\frac{172}{125} \end{aligned}

Show that, under the risk-neutral measure, the discounted gain process
$$\hat{G}t=\frac{P_t}{B_t}+\sum{s=1}^t \frac{D_s}{B_s}$$
is a martingale (i.e. $E_t^Q\left[\hat{G}{t+1}\right]=\hat{G}_t$ ) from the definition of risk-neutral measure in lecture notes $$P_t=E_t^Q\left[\sum{u=t+1}^T \frac{B_t}{B_u} D_u\right]$$
That is the reason why the risk-neutral measure is also called the “equivalent martingale measure” (EMM).

We want to show $E_t^Q\left[\hat{G}_{t+1}\right]=\hat{G}_t$.

$$E_t^Q\left[\hat{G}{t+1}\right]=E_t^Q\left[\frac{P{t+1}}{B_{t+1}}+\sum_{s=1}^{t+1} \frac{D_s}{B_s}\right]$$
Now recall that
$$P_{t+1}=E_{t+1}^Q\left[\sum_{u=t+2}^T \frac{B_{t+1}}{B_u} D_u\right]$$
Substituting this into the first expression, we have
\begin{aligned} E_t^Q\left[\hat{G}{t+1}\right] & =E_t^Q\left[\frac{P{t+1}}{B_{t+1}}+\sum_{s=1}^{t+1} \frac{D_s}{B_s}\right] \ & =E_t^Q\left[\frac{1}{B_{t+1}} E_{t+1}^Q\left[\sum_{u=t+2}^T \frac{B_{t+1}}{B_u} D_u\right]+\sum_{s=1}^{t+1} \frac{D_s}{B_s}\right] \ & =E_t^Q\left[E_{t+1}^Q\left[\sum_{u=t+2}^T \frac{D_u}{B_u}\right]+\sum_{s=1}^{t+1} \frac{D_s}{B_s}\right] \ & =E_t^Q\left[\sum_{s=1}^T \frac{D_s}{B_s}\right] \ & =E_t^Q\left[\sum_{u=t+1}^T \frac{D_u}{B_u}\right]+\sum_{s=1}^t \frac{D_s}{B_s} \ & =\frac{1}{B_t} E_t^Q\left[\sum_{u=t+1}^T \frac{B_t}{B_u} D_u\right]+\sum_{s=1}^t \frac{D_s}{B_s} \ & =\frac{P_t}{B_t}+\sum_{s=1}^t \frac{D_s}{B_s} \ & =\hat{G}_t \end{aligned}
This shows that $\hat{G}_t$ is a martingale under the risk-neutral measure.

Suppose that uncertainty in the model is described by two independent Brownian motions, $Z_{1, t}$ and $Z_{2, t}$. Assume that there exists one risky asset, paying no dividends, following the process
$$\frac{d S_t}{S_t}=\mu\left(X_t\right) d t+\sigma d Z_{1, t}$$
where
$$d X_t=-\theta X_t d t+d Z_{2, t}$$
The risk-free interest rate is constant at $r$.

What is the price of risk of the Brownian motion $Z_{1, t}$ ?

Let the price of risk for $Z_{1, t}$ and $Z_{2, t}$ be $\eta_t=\left[\begin{array}{cc}\eta_{1, t} & \eta_{2, t}\end{array}\right]^T$. Then we must have
$$\mu\left(X_t\right) S_t-\left[\begin{array}{ll} \sigma S_t & 0 \end{array}\right] \eta=r S_t$$
This gives
$$\mu\left(X_t\right) S_t-\sigma \eta_{1, t} S_t=r S_t$$
whereas there is no constraint for $\eta_{2, t}$. Hence, the price of risk of $Z_{1, t}$ is
$$\eta_{1, t}=\frac{\mu\left(X_t\right) S_t-r S_t}{\sigma S_t}=\frac{\mu\left(X_t\right)-r}{\sigma}$$

# 经济学和金融学代写Principles of Economics and Finance|ECN4001 University of Plymouth Assignment

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Assignment-daixieTM为您提供普利茅斯大学University of Plymouth ECN4001 Principles of Economics and Finance经济学和金融学代写代考辅导服务！

## Instructions:

1. Supply and demand: The principle of supply and demand describes how prices are set in a market economy. It states that the price of a product or service is determined by the amount of supply and the level of demand for that product or service.
2. Opportunity cost: Opportunity cost is the value of the next best alternative that must be given up in order to pursue a certain action or decision.
3. Time value of money: The principle of time value of money explains how the value of money changes over time due to inflation, interest rates, and other factors.
4. Risk and return: The principle of risk and return states that investments with higher risks generally have higher potential returns, and investments with lower risks generally have lower potential returns.
5. Diversification: Diversification is the principle of spreading investments across different assets and markets in order to reduce risk.
6. Efficient markets: The efficient markets hypothesis states that financial markets are generally efficient and that all available information is reflected in the prices of financial assets.
7. Comparative advantage: The principle of comparative advantage explains how individuals, firms, and countries can benefit by specializing in the production of goods and services that they can produce at a lower opportunity cost than others.

These are just a few of the core principles of economics and finance. There are many more principles and concepts that are important in these fields, but these are some of the most fundamental and widely recognized.

Consider a 3-period model with $t=0,1,2,3$. There are a stock and a risk-free asset. The initial stock price is $\$ 4$and the stock price doubles with probability$2 / 3$and drops to one-half with probability$1 / 3$each period. The risk-free rate is$1 / 4$. (a) Compute the risk-neutral probability at each node. 证明 . Let$q$denote the risk-neutral probability of up-node and$1-qdenote risk-neutral probability of the down-node. Then by the definition of the risk-neutral probabilities, \begin{aligned} S_t & =E^Q\left[\frac{1}{1+r} S_{t+1}\right] \ & =\frac{1}{1+r}\left(q\left(2 S_t\right)+(1-q)\left(\frac{1}{2} S_t\right)\right) \end{aligned} withr=\frac{1}{4}$. Solving this equation gives$q=\frac{1}{2}$. Notice that this calculation holds true at every non-terminal node. We conclude that the risk-neutral probability at each node is given by$\frac{1}{2}$probability of up-node and$\frac{1}{2}$probability of down-node. 问题 2. (b) Compute the Radon-Nikodym derivative$(d \mathbf{Q} / d \mathbf{P})$of the risk-neutral measure with respect to the physical measure at each node. 证明 . The original (physical) measure assigns probabilities$p_u=\frac{2}{3}$and$p_d=\frac{1}{3}$to the up- and down-node, respectively. The risk-neutral measure assigns probabilities$q_u=\frac{1}{2}$and$q_d=\frac{1}{2}$. The Radon-Nikodym derivative at each node is a random variable that takes on the value $$\left(\frac{d Q}{d P}\right)(u)=\frac{q_u}{p_u}=\frac{1 / 2}{2 / 3}=\frac{3}{4}$$ in the up-node and the value $$\left(\frac{d Q}{d P}\right)(d)=\frac{q_d}{p_d}=\frac{1 / 2}{1 / 3}=\frac{3}{2}$$ in the down-node. Again, notice that this calculation is valid at each and every node. 问题 3. (c) Compute the state-price density at each node 证明 . Fix the current node at time$t$and let the state-price density at this node be denoted by$\pi_t$. Denoting the values of state-price densities at the childen nodes by$\pi_{t+1}(u)$and$\pi_{t+1}(d), we have the following pricing equations: \begin{aligned} S_t & =\frac{2}{3} \frac{\pi_{t+1}(u)}{\pi_t} \cdot 2 S_t+\frac{1}{3} \frac{\pi_{t+1}(d)}{\pi_t} \cdot \frac{1}{2} S_t \ & =\left(\frac{4}{3} \frac{\pi_{t+1}(u)}{\pi_t}+\frac{1}{6} \frac{\pi_{t+1}(d)}{\pi_t}\right) S_t \ 1 & =\frac{2}{3} \frac{\pi_{t+1}(u)}{\pi_t} \cdot \frac{5}{4}+\frac{1}{3} \frac{\pi_{t+1}(d)}{\pi_t} \cdot \frac{5}{4} \ & =\left(\frac{5}{6} \frac{\pi_{t+1}(u)}{\pi_t}+\frac{5}{12} \frac{\pi_{t+1}(d)}{\pi_t}\right) \end{aligned} Solving this system of equations in the unknowns\frac{\pi_{t+1}(u)}{\pi_t}$and$\frac{\pi_{t+1}(d)}{\pi_t}, we get the solution \begin{aligned} \frac{\pi_{t+1}(u)}{\pi_t} & =\frac{3}{5} \ \frac{\pi_{t+1}(d)}{\pi_t} & =\frac{6}{5} \end{aligned} Now, starting at the initial node at timet=0$and setting$\pi_0=1$allows us to solve for the state-price density at every node recursively. This calculation leads us to conclude that at a node$\omega$whose history consists of$i$up-movements and$j\$ down-movements, the state-price density is given by
$$\pi_t(\omega)=\left(\frac{3}{5}\right)^i\left(\frac{6}{5}\right)^j$$

# 会计学代写Introduction to Accounting|ACF4001 University of Plymouth Assignment

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Assignment-daixieTM为您提供普利茅斯大学University of Plymouth ACF4001 Introduction to Accounting会计学代写代考辅导服务！

## Instructions:

Accounting is the process of recording, summarizing, and communicating financial information about a business or organization. It is an essential part of running any business, as it helps to track income, expenses, assets, liabilities, and equity. This information is crucial for making informed decisions about the future of the business.

The foundation of accounting is the double entry bookkeeping system, which ensures that every transaction is recorded in two accounts: a debit account and a credit account. This system provides a way to accurately track the financial health of a business and detect any errors or discrepancies that may occur.

Accounting also involves creating financial statements, which are reports that summarize the financial activity of a business over a specific period. The most common financial statements are the balance sheet, the income statement, and the statement of cash flows. These statements provide insight into a business’s financial performance and can help stakeholders make informed decisions about the future of the business.

Ratio analysis is another important aspect of accounting, which involves using financial ratios to interpret and analyze financial information. These ratios can provide insight into a business’s liquidity, profitability, and financial stability, and can be used to compare the financial performance of different businesses or industries.

Overall, accounting is a critical function of any business, as it provides a way to track financial information and make informed decisions about the future of the organization.

Answer ONE of the following two:
A. Why is conservatism important in accounting?

Accounting requires certain estimates and judgments. Conservatism improves the
process of estimation by allowing accountants to assign values to certain transactions.

• Conservatism makes accounting numbers credible.
• Lenders bear the downside risk without upside potential; therefore, lenders would like
to get the bad news more timely. Conservatism allows for this.
• Conservatism improves investor believability of public companies’ financial
statements.

B. Why is objectivity important in accounting?

Information produced by managers alone is not believable. Outside investors demand
independently audited financial information.

• Allows investors to better trust the information contained in the financial statements.
• Allows for consistency in financial information among the different firms. Analysts
and investors can then compare various companies on the basis of their financial
statements and forward estimates.
• Important for the auditors that review the financial statements.
• Establishes the internal control system through which transactions are properly
authorized, reported, and recorded.

For 2002 calculate the Days Inventory held by Intel. What is the cost and/or
risk of holding high inventory for Intel?

Inventory Turnover = COGS/(Average Inventory) = 8650/.5(2276 + 2253) = 3.82
Days Inventory Held = 365/(Inventory Turnover) = 365/3.82 = 95.6 days
The cost or risk associated with holding high inventory is that prices drop quickly,
particularly in Intel’s industry. There is also concern for the obsolescence of finished
goods.