Hi i would like to find somebody to create a model and do a statistical analysis for me. These things they have to happen through the R language. I will send a dataset in the "Drop files" section which will be an excel file, that will include panel data from 4 countries, and every country will have 4 variables for the period of 1995 until 2019. I would like to be made a panel data analysis. I would like to be used all the methods of panel data analysis which means Fixed effects models, Random effects models, pooled OLS models and mixed effect model (4 models in total). When they will be created all these 4 models i would like the author to write which model is the best between these 4 and why (Present all of these 4 models when you are writing the analysis). Then the best model chosen by the writer i would like to be interpreted in terms of the coefficients of that model, and the significance of the model. Thus, generally there should be a deep analysis of the best chosen model like interpretation of the parameters ,what does it tell this model about the relationship between the dependent and independent variable,how good is this model etc. In order for you to cover this part i will have to explain the hypotheses, the dependent variable and the independent variable and probably other stuff that maybe i forget. Please tell me if something is missing to cover it. Hypothesis section: 1) Does fiscal policy and corruption have any impact on economic growth? 2) Is this impact positive or negative? 3)Is there any interaction between corruption and fiscal policy? 4) Is this interaction positive or negative? 5) Null hypothesis: Corruption and Fiscal policy have no impact on economic growth. 6) Alternative hypothesis: Corruption and fiscal policy indeed influences economic growth. 7)Null Hypothesis: There is no interaction between corruption and fiscal policy. 8) Alternative Hypothesis: There is interaction between corruption and fiscal policy. Variables section: In the excel file there are 4 variables. 1) GDPpcap(GDP per capita), GovSp(Government Spending), TaxRev(Tax Revenue) and corrup(Corruption Perception Index). Dependent Variable: GDP per capita (measured as US dollars/capita). Independent Variables: 1)Government Spending (measured as a percentage of GDP), 2)Tax Revenue(measures as a percentage of GDP) and 3)Corruption (measured with an index called "Corruption Perception Index" which is an index that measures corruption. The values of corruption they are ranged from 1 to 100. A country with a score that it is closer to 1 it means that it has high corruption. Conversely a country with a score closer to 100 means that that it has low corruption). 4) Interaction Term 1: Corruption interaction with Government Spending 5) Interaction Term 2: Corruption interaction with Tax Revenue The topic is "The impact of Fiscal policy and Corruption on Economic Growth". I have tell you that Fiscal Policy it is expressed by the variables of Government Spending and Tax Revenue. Corruption it is expressed by the corruption perception index (which is of course in the column "Corrup", in the excel file). Economic growth it is expressed by GDP per capita. Therefore the topic including the variables explanation it will be as follows: The impact of fiscal policy (Independent Variables: Government Spending + Tax revenue) and Corruption (Independent variable: Corruption Perception Index) on Economic Growth (Dependent Variable: GDP per capita). Specifically there will be a model with one dependent variable and 5 independent variables(3 normal variables and 2 interaction terms). Simply in a very very very simple and maybe wrong form the model it will be as follows: GDP per capita = Government Spending + Tax revenue + Corruption Index + interaction term1: Corruption/Government Spending + interaction term2 Corruption/Tax Revenue. Please communicate with me immediately if there is something that it is not clear. If you dont mind please prefer to communicate with me by email and not by phone. I will always check my email to respond to you as fast as i can. Final things, i dont think that the data they are stationary please check for autocorellation, or stationarity of the data, and if in general check if the data are ok to be used for statistical analysis-regression. (You know better as a statistician, please be sure that the data are good for regression to avoid spurious regressions and violations of assumptions). Please provide in a clear way all the r codes that you used in order to do and find everything in this data analysis, dont just provide the results, show me all the codes that you used in the R studio. Interpret clearly the models. Thank you very much!!!!
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