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# 时间序列hw代写 R代写 code代写

2022-05-21 11:06 星期六 所属： R语言代写 浏览：269

## Time series

### 1.  时间序列hw代写

Estimating the macroeconomic and financial effects of uncertainty shocks

The research question is : What are the effects of uncertainty shocks on macroeconomic and financial activity ?

To answer that question, you have to use the structural VAR method. Important ingredients are :

— Data

— Choice of variables to represent economic concepts
— Frequency
— Time span
— Transformation to induce stationarity

— Identification of uncertainty shocks

— Choice of the uncertainty proxy
— Identification strategy

— Impulse responses    时间序列hw代写

— Show impulse response functions of all variables in the VAR to the identified positive uncertainty shock
— Show responses up to 4 years ahead
— Show point estimates and 90% confidence bands
— Show variance decomposition for horizons : 3 months, 1 year, 4 years

— Discussion

— Comment your results. Are uncertainty shocks important ? Do the effects vary a lot across variables (production, prices, stock market, etc.) ?
— Do you obtain similar results as in the literature ?
— Can you explain your results with economic / financial theory ?
— How robust are the results (ordering of variables in case of Cholesky identification, time span, etc.) ?

Here are some suggestions.

#### Variables

— Select macroeconomic and financial indicators that represent important concepts like labor market, production, prices, interest rates, housing market, stock market
— Work with monthly frequency because short-term identification restriction are more likely to hold than with quarterly data
— Time span should be long, but be aware of structural changes that are likely occur over time. Hence, take a long data set, for example since 1960, but do some sensitivity analysis by replying the exercice for shorter time periods (since 1984 and since 2000).
— Apply standard transformations like first difference of logs or first difference. For this part, rely on the literature.

### 2.  时间序列hw代写

Identification of uncertainty shocks Recall that uncertainty is not observable. You will try several proxies :

— Macroeconomic uncertainty from Jurado, Ludvigson and Ng (2015). Data are available at : https://www.sydneyludvigson.com/macro-and-financial-uncertainty-indexes
— Economic policy uncertainty from Baker. Data are available at : https://www.policyuncertainty.com
— Financial uncertainty
— Realized volatility
— You can use an observable measure
— Or you can construct it from daily SP500 data (+ 5 points if you do that correctly)
— Implied volatility
— You can use an observable measure like VIX
— Or you can construct it by estimating GARCH(1,1) model on SP500 data (+ 5 points if you do that correctly)
— Identification strategy
— You can use Cholesky, but do not forget that this strategy implies a particular ordering of variables.

It is important to consult the following references that contain several structural VAR exercises which will be a source of inspiration for you project.

#### References  时间序列hw代写

Baker, S. R., Bloom, N. and Davis, S. J. (2016), “Measuring economic policy uncertainty”, The Quarterly Journal of Economics, 131(40), 1593-1636

Bloom, N. (2008), “The impact of uncertainty shocks”, Econometrica, 77(3), 623-685

Jurado, K., Ludvigson, S. and Ng, S. (2015), “Measuring uncertainty”, American Economic Review 105(3), 1177-1216.

Moran, K., Stevanovic, D. and A. Touré (2021). “Macroeconomic Uncertainty and the COVID- 19 Pandemic : Measure and Impacts on the Canadian Economy”, Canadian Journal of Economics, forthcoming.