Economic Forecasting - Science topic

There are both optimistic and pessimistic factors in macroeconomic forecasts for the world economy over the long term. Depending on how they operate and which prevail, either more optimistic or more pessimistic scenarios are developed for the development of the projected economic situation realised in the future. In terms of optimistic factors, these include the use of new information technologies, Industry 4.0 and others, which, when implemented in companies and enterprises, allow for improved profitability of business processes, increased production scale, improved quality control systems and/or improved quality of product and service offerings, etc. Besides, the green transformation consisting in the development of renewable and emission-free energy sources and based on rapidly cheap green energy technologies and on generating savings in energy consumption contributes to economic efficiency and energy security. In addition, the development of sustainable economic processes, scaling up the sharing economy, improving waste separation systems, reusing recovered secondary raw materials, improving and scaling up industrial recycling, etc. will also generate savings in the consumption of raw materials and energy in the context of an efficient economy. In this way, savings will be generated that will allow for an increase in the scale of financial subsidies directed to special purpose funds supporting the development of pro-climate and pro-environmental economic ventures and the development of green economic sectors. Pessimistic factors, on the other hand, include the retreat of economic globalisation from the onset of the pandemic, the rise of economic isolationism, the prospect of deepening trade wars between the world's major economies, the introduction of prohibitive tariffs to protect domestic labour markets, the successive reduction in the scale of cross-border transfers of strategic raw materials, components, prefabricated products and technology, etc. The reduction in the scale of the international transfer of products and services, international trade also involving factors of production, strategic raw materials was already noticeable a few years before the coronavirus pandemic (Covid-19), and during the pandemic through disrupted chains of international supply and procurement logistics the scale of intermodal logistics, international trade decreased. It also resulted in the shortening of international supply and procurement logistics chains and the development of domestic industries supplying the necessary sub-assemblies and pre-fabricated components used in the production of various goods, products, mainly technological products composed of many sub-assemblies.

Such problems determining the deepening of trade wars and the backsliding of economic globalisation processes at the end of February 2022 are increased by a full-scale military war in Ukraine. This kind of war generates economic uncertainty, and uncertainty is an increase in the scale of economic risks that are difficult to measure, not easy to quantify, and holding back investment. In addition to military sectors, apart from companies producing weapons and equipment for the military, it is in many other sectors and industries of the economy that the aforementioned increase in uncertainty becomes a limiting factor for the development of investment and economic activity. On the other hand, when the war ends and the processes of reconstruction of Ukraine's economy begin, there will be a significant recovery of economic processes in some sectors of the economy, such as construction and heavy industry. However, it is unclear when the war will end. In addition to this, determinants contributing to the deepening downturn of the economy include continued elevated inflation at double-digit levels. In the context of high consumer inflation, the high proportion of core inflation determined by domestic factors is a matter of concern. In addition to this, there remains a high level of risk of further investment bank failures in a situation of falling stock market valuations of previously issued government bonds with fixed and significantly lower interest rates on new series of government bonds being issued than at present. Consequently, there is still a high level of uncertainty about the development of the economic situation in the financial markets, including the capital markets, the stock markets on which securities are priced.

In view of the above, I would like to address the following question to the esteemed community of scientists and researchers:

What do you think about this topic?

What is your opinion on this subject?

Please respond,

I invite you all to discuss,

Thank you very much,

Best wishes,

Dariusz Prokopowicz

I'm using stock price data to model the volatility and forecast volatility using EViews. In forecasting when I use the static forecast, the mean absolute percentage error is high but it is low when I use dynamic forecast. What is the difference between these two methods? and which is more accurate?

Rising interest rates and high inflation can potentially impact the economy negatively. When interest rates rise, borrowing becomes more expensive, which can slow down economic activity and investment. Similarly, high inflation erodes the purchasing power of consumers and can lead to reduced consumer spending.

An inverted yield curve occurs when short-term interest rates are higher than long-term rates, which has historically been seen as a potential indicator of an economic downturn. It suggests that investors have less confidence in the future and expect lower returns on long-term investments.

An unexpected banking crisis can also have severe consequences for the economy. It can lead to a loss of confidence in the financial system, reduced lending, and a contraction in economic activity.

However, it's important to note that the economy is influenced by numerous complex factors, and these indicators alone do not guarantee a recession. Economic forecasts and predictions are subject to uncertainty and are best made by economists and financial experts who have access to the most up-to-date data and comprehensive analysis

Hi,

I am currently working on a research project which aims to develop a forecasting model for prices of agricultural commodities. I am mainly using ARIMA method for this purpose. If my ARIMA Model is such that there exists ARCH effect/Heteroskedasticity in that. Then can I use the same ARIMA model for Price forecasting. Can anybody advise me on this?

Thank you

If not forecasting, would you replace the forecast with a foresight prediction method?

We are entering the new year 2022 and it is at the turn of the year that many macroeconomic and microeconomic forecasts for individual markets and industries appear. On the other hand, companies listed on the stock exchange are happy to boast of their potential to create value in the first quarter of the year, present attractive forecasts of increasing sales, acquiring new customers and profits in the markets ... to tempt new investors into shares. Is it not sometimes as Peter Drucker stated years ago?

„The best way to predict the future is to create it.”

“Time is the scarcest resource. Unless it is managed, nothing else can be managed.”

What do you think about forecasting and planning the future?

I've added some inspirational leads

Some banks conducting an analysis of the creditworthiness of an enterprise applying for a long-term investment or mortgage loan take into account the issues of climate change, if these changes may affect the business profitability of a specific lending business venture. For example, in the case of a hotel application for a long-term investment loan operating on the sea, the level of which can rise and flood the hotel area.

Another example is a hotel located in the mountains, where winter sports tourists come. Climate change predictions may indicate that 10 years of snow will no longer be the place where this hotel in the mountains provides its services. Therefore, the bank may not grant credit due to the forecasted secondary effects of progressive climate changes and, above all, the rising average temperature.

On the other hand, companies are developing which produce components for new power plants producing electricity as part of renewable energy sources, produce electric car equipment components, e.g. electric motors, batteries, etc. More and more innovative startups are being produced as part of cooperation with large enterprises and renewable energy plants Wind turbine type subassemblies, charging devices for electric cars, etc. Other companies manufacture packaging from recycled materials, recycled or from biodegradable materials.

Other companies are developing innovative solutions for automatic sorting of rubbish. If eco-friendly products become popular and the state creates good institutional, legal and financial conditions for the development of such projects, then the process of implementing sustainable green economy based on the green economy concept will be implemented more quickly and business probes will become more and more profitable. Financial institutions, including banks, will gradually take into consideration eco-friendly processes and business activities of clients in concluded financial transactions.

Do you agree with me on the above matter?

In the context of the above issues, I am asking you the following question:

Please reply

I invite you to the discussion

Thank you very much

Best wishes

Will the development of computerized business analytics of large collections of economic information collected in Big Data database systems improve the forecasting of future economic processes?

Please reply

I invite you to the discussion

Thank you very much

Dear Colleagues and Friends from RG

I invite you to discussion and cooperation.

Best wishes

Currently, it is difficult to define this type of analytic problem. The key issue is forecasting future global problems. It is necessary to collect additional analytical data over the next years and perhaps in about 100 years in huge Big Data database systems supported by another generation of artificial intelligence, it will be possible to forecast what can happen to the planet Earth in the next 1000 years.

In view of the above, the current question is: Will I be able to precisely forecast in the 21st century what will be the future of planet Earth in the next 1000 years?

Please, answer, comments. I invite you to the discussion.

Will future Big Data database systems supported by artificial intelligence be used in precise forecasting in order to verify futurological projections?

Currently, it is difficult to define this type of analytic problem. The key issue is forecasting future global problems. It is necessary to collect additional analytical data over the next years, and perhaps in the 21st century, in huge Big Data database systems supported by another generation of artificial intelligence, it will be possible to predict what may happen to the planet Earth in the future.

In view of the above, the current question is: Will future Big Data database systems supported by artificial intelligence be used in precise forecasting for the verification of futurological projections?

Please, answer, comments. I invite you to the discussion.

Apparently, on the financial markets and in macroeconomic determinants of the economic situation in particular sectors and entire economies of developed countries, there are symptoms that suggest a high probability of economic slowdown from 2020 in individual countries and, consequently, in the entire global economy.

Therefore, I am asking you: Do you know the forecasts of the global economic development that would suggest a high probability of deceleration (or possibly acceleration) of economic growth from 2020 in individual countries and, consequently, in the entire global economy?

What are the symptoms of potential changes in the financial markets and / or the scope of macroeconomic determinants of the economic situation in particular sectors and entire economies?

If you know the results of prognostic research in this area, please send links to websites or scientific publications in which this type of prognostic issues are taken.

I wish you the best in New Year 2019.

Best wishes

Is there any experience in the use of big data analytics in this area?

The original series is nonstationary as it has a clear increasing trend and its ACF plot gradually dampens. To make the series stationary, what optimum order of differencing (d) is needed?

Furthermore, if the ACF and PACF plots of the differenced series do not cut off after a definite value of lags but have peaks at certain intermittent lags. How to choose the optimum values of 'p' and 'q' in such a case?

What is the growing share of protectionism that limits cross-border trade between major economies in the projected slowdown in global economic growth?

Do you agree with my opinion that in many developing countries the influence of foreign direct, capital and financial investments is significant.

However, the analysis of this process in individual countries results in a significantly different scope and nature of the impact of foreign investment capital.

According to the doctrine of classical economics, all countries should benefit from opening up the economy to foreign investments and the development of trade, including the export and import of economic goods.

However, are all countries always benefiting from this process economic benefits and the process develops faster?

It's not always like that. If all countries benefited from the growth of trade, protectionism, such as the establishment of anti-dumping duties to reduce cross-border trade, would be unnecessary.

What is the impact of foreign investment capital in the globalization era on the economic development of developing countries?

What is the growing share of protectionism that limits cross-border trade between major economies in the projected slowdown in global economic growth?

Are the currently limited protectionist practices cross-border trade is the main factor in the forecasted slowdown in global economy growth?

Please reply

Best wishes

I am performing multi-period optimization (e.g. 2020-2035) of a MSW management system within the European context and I am struggling to find reliable data/methods/approaches for MSW composition forecasting. Most of the references I found link the generation of specific waste materials (e.g.plastic packaging, food waste, etc.) to GDP evolution. In other words, all materials contained in MSW evolve at the same rate as the GDP. This approach does not account for prevention measures for specific wastes (e.g. plastic ban) or the potential increase on the generation of other waste materials (e.g. an increased use of glass packaging). Do you have any idea/reference on this issue? (I am thinking, for example, on the use of consumption patterns to derive waste composition). I appreciate any suggestion provided.

I have been trying to forecast GDP of Pakistan for FY 2020 to FY 2021 by using a dataset having Energy, Labor and Capital as exogenous variables. The data set has been attached for reference. As its evident from the data that three values of GDP are missing (for FY 2020 to 2022) as I have to forecast these values. Rstudio is treating these missing values as NAs which poses a problem for neuralnet () function. Is there any way for Rstudio to recognize these missing values as dynamic values so that it doesn't prompt an error? Or is there any other way for me to forecast these values using ML/AI in R?

Any help in this regard would be highly appreciated.

Thanks.

What kind of scientific research dominate in the field of Futurology, forecasting, future, technologies of the future?

Please reply. I invite you to the discussion

Are there banks that take into account the forecasted climate changes related to the ongoing global warming process in the creditworthiness assessment process?

Are there banks that in lending in the field of granting long-term investment and mortgage loans in the process of assessing the creditworthiness of potential borrowers include forecasted climate changes related to the ongoing global warming process?

For long-term investment loans and mortgages granted for 20, 30 years or more, predicted climate changes related to the ongoing global warming process may already be significant.

Please reply

Best wishes

What do you think about the possibility of forecasting economic processes based on the analysis of large data sets in Big Data database systems?

Will Big Data help in precisely forecasting future economic processes, including in terms of forecasting stock exchange trends on stock exchanges?

Will Big Data help in forecasting future, next financial, economic and other crises such as climate disasters, weather anomalies, earthquakes, etc.?

Please reply

Best wishes

Dear Researchers/Scholars,

Suppose we have time series variable X1, X2 and Y1. where Y1 is dependent on these two. They are more or less linearly related. Data for all these variables are given from 1970 to 2018. We have to forecast values of Y1 for 2040 or 2060 based on these two variables.

What method would you like to suggest (other than a linear regression)?

We have a fact that these series es have a different pattern since 1990. I want to make this 1990-2018 data as prior information and then to find a posterior for Y1. Now, please let me know how to asses this prior distribution?

or any suggestions?

Best Regards,

Abhay

I am running ARIMA model with the natural log values of 10 years daily frequency data of a stock market index i.e., Nifty

the variable is integrated at first differencing .

the lag lengths of AR and MA are decided based on partial autocorrelation and autocorrelation functions. the decided lag length is '1' for both AR and MA terms.

my doubt is whether the dependent variable should be level form(i.e., LN_Nifty) or in first differenced Nifty while running ARIMA . Because the variable is integrated at first order.

if i run the ARIMA with the level form of Ln_Nifty, the AR and MA terms are significant.

if the first differenced form of LN_Nifty is used, the AR and MA terms are not signficant .

please anyboday clarify my doubt

thanks in advance

Is it possible to heal the banking banking supervision of corporate investment banking to significantly reduce the dramatic effects of the next global financial crisis for the national economy and society? Is it too late for that?

Of course, this question should be answered in the negative, that it is never too late to repair the operation of any system that is supposed to serve people. But whether the scale of mistakes made in the past has not generated the unavoidable pursuit of a global financial crisis that is even more dramatic in the negative consequences for entire economies and societies. A crisis that will start with the spectacular collapse of one of the largest financial institutions, a bank or an investment fund. A globally operating financial institution that will lose playing "poker" on international capital markets with other investment banks. Some of these others earn from this crisis by winning this "global poker" and real economies will again plunge into a multifaceted economic crisis, debt crisis, a period of deep recession, rising unemployment and falling income of citizens. Is the capital flow in this way through these games, games in "global poker" on the capital markets played between the largest investment banks is economically effective? Well, it is not an economically effective process, it is a process harmful to economic development. So why are these games in "global poker" conducted? Is it only because in the process of excessive, secondarily realized liberalization of supervisory standards over financial systems implemented in the 90's, allowed to create too large, increasingly globally and monopolistic investment banks? In my opinion, not only because. Not only the scale of operations, not only the large share of capital compared to the financial system and the entire economy is a serious threat and a crisis-generating factor. Also important are the elementary rules of risk management, which are forgotten, ignored at certain organizational levels of the financial institution or financial system management.

Analysis of the origin of the next global economic crisis

Currently, forecasting systems are being developed regarding forecasting future trends of economic processes based on various analytical, not only economic, determinants. Personally, I also support the thesis about the impact of various cosmic and atmospheric phenomena on various events that take place on Earth in the field of economy, economics, politics, etc. On the other hand, because sources of the global financial crisis I mainly researched in terms of progress (or rather lack of it) ) in the field of improving the credit risk management process, implementation of modern IT solutions streamlining these processes, filling gaps in legal regulations developed in financial supervision institutions in relation to technological development of transactional, corporate and investment banking, creation of new derivatives etc., so I add to this type of analysis the issue of the analysis of the process of improvement of systemic management, banking credit risk. Unfortunately, the strong investment banking banking lobby influencing the politics of the world's largest economies is accepted by the government establishment, because monetary policy, periodically regulated lending policy, increasingly liberalized, transactional modernization, electronically and disseminated investment banking are areas treated as "universal magical tools" that can be used as a determinant for economic growth as part of state intervention. In this respect, there is a lack of full information flow in the area of ​​growing credit risk and the fast approaching new global financial crisis between the transactional level of sales of banking products and the level of monetary, credit and financial system security at national and supranational level. According to the demands of the classical economy, liberalism at the transactional level of the sale of banking products should not be limited by state intervention at the level of the entire financial system. But the exception in this regard is the issue of the security of the financial system. If, secondarily, the extremely liberalized principles of systemic security periodically lead to an increasing financial crisis in investment and credit banking, why should the costs of these errors be spread across entire economies? Why is it that investment banks in economic crises, which often cause them to earn money from them, and the costs are repaid by entire societies, people lose their jobs and many years of experience of their lives? Therefore, because these investment banks have genuinely monopolized the systemic credit risk management system. They no longer serve the economy, but try to shape economies according to their investment strategies. The question that now arises is whether this harmful and crisis-provoking process can be reversed, corrected before the emergence of the next global financial crisis? Is it already too late and only one of the next financial crises, which will lead to the collapse of not one but a few major banks and investment funds will make it possible to repair damages resulting from errors that politicians began to make in the 1990s liberalizing then secondary issues of banking supervision systems? If it is only in the situation of the next global economic crisis, then how dramatic are the consequences for entire national economies, for societies, for people? It is not easy to predict this issue, but it is almost certain that it will be very dramatic, above all economically and socially, but perhaps also politically, strategically and militarily for many countries.

Large amounts of information downloaded from comments, entries, posts from social media portals are processed in Big Data database systems to determine, for example, consumers' awareness of the offer of products and services of specific companies. This type of information is of great importance for the planning of advertising campaigns informing about the mission, idea, product offer, and the usability features of the company's offer. This type of data may be important for forecasting the changing preferences of consumers regarding the offer of specific companies.

In view of the above, I am asking you: To what extent the Big Data data sentiment analysis from social media portals can be used in forecasting the company's development?

Please reply. I invite you to the discussion

Technologically it becomes possible, only the level of precision of results and the usefulness of this type of analysis are still undetermined. It is connected with continuous improvement of tools, analytical systems, eg Business Intelligence, which in analytical processes would use large data sets, historical information and current real time. Improvement of these research techniques will in the future allow to create data based on the sentiment analysis of data contained in the Big Data resources, tools enriching the forecasting methods used to describe the trends of specific economic variables, including primarily macroeconomic ones. Does anyone from you conduct research on this topic? If so, I invite you to cooperation.

As per literature review, some of the studies included weather variables as input variables whereas there are also studies which treated weather variables as determining factors of technical inefficiency. Any econometric/ economic reasoning to decide this?

What is your opinion regarding to the above question?

It is important to note, therefore, that demographic projections should not be confused with economic forecasts. Changes in the number of people, families, or households do not necessarily relate to the social and economic well-being of an area.

I want to forecast volatility with GARCH, EGARCH and GJR-GARCH. How do I obtain the RMSE, MAE and MAPE. The problem now is that I am using a mean equation and the values reported in the little table on side are for stock returns. I need the same thing for volatility. Any idea except from calculating them by hand?

I do know that Eviews has an add on for this model, But I am using a old version of the Eviews and therefore the add on feature cannot be incorporated in the same.

ARIMA models, from what I know, are atheoretical models in the sense that they generally don't provide us with meaningful economic interpretation of why a process is behaving the way it does.

However in population forecasting, can ARIMA models be interpreted in a meaningful way?

i.e.

For my research paper, i have to study the relationship between stock returns and macroeconomic variabels by using a suitable regression model. (stock return is DV and macroeconomic variabels IVs)

the stock returns data is available daily-wise and that of macroeconomic variables monthwise.

how to resolve the problem of different frequencies of the stock returns and macroeconomic variables?

thanks in advance

Doing research in the field of finance and investment, I found it difficult to understand various models, such as ARCH; GARCH and many more used in the contemporary research articles.

I am searching such a book(s) which can explain these models and methods in a very simple manner accompanied by example and data set. As I am not an expert in these areas of analysis, I want to learn those model from the very basic and beginners level.

Bubbles have a very tightly circumscribed economic meaning, but they have also been studied by scholars (and methods) from humanities and social sciences other than economics. I am seeking your expert help in identifying the very best multidisciplinary writings on bubbles.

I want to remove the trend from Time series for monthly data of water consumption.

I tried both methods

1- detrend 

2-moving average

which method is appropriate for this issue?

please see attached files

Thanks in advance for you cooperation  

The Middle Eastern countries have experienced budget deficit due to lower oil revenues. Since their budget deficit is expected to persist for some periods due to lower forecasted oil prices, what options they to finance budget deficits in the short as well as long run?

I am currently working with 60 observations time series data in eviews.

After applying log to my model, the residuals were correlated and heteroscedastic. In order to treat this issue i created logs. After creating 2 lags i got my ADL(2) model which showed up the problem of non normality of residuals.The same was treated by taking another lag and thus ADL(3) was formed.

The problem popped up when Lag selection was to be done and information criterion was used. Thie information criterion approved that ADL(1) should be taken. Whereas that model has NON NORMALITY AND SERIALLY CORRELATED residuals. Please guide what should be done.

which ADL model should be considered?

In my research, I want to calculate realized volatility, which necessitates the availability of intraday data series. unfortunately the data at hand are only daily frequency. 

Do you know any articles where the number of trades on stock exchange is predicted? I am particulary interested in the prediction of number of trades in 1-5 minute intervals.

The company i42 GmbH, Mannheim, developed MoneyBee: a system to predict stock market values, basing on artificial intelligence (neural networks), distributed computing and different applications to optimize the input- and output-data (e.g. genetic algorithms, statistical methods).

Is there any similar project like MoneyBee?

I need some references for a project I'm working on

In International trade, to know about economic progress for the future. we have to measure by forecasting analysis. Could you possibly explain forecast of the economic in International trade?

How to measure the so-called "upgrading of consumption" ? 

I'm doing some research using som relatively high-frequency(quarterly or monthly) panel data set. In this data setting, seasonality and data availability are both concern. I want to refer to some classics in the area but find simple google is not enough. If you are familiar with this area, please throw some literatures (application is enough) for me . Thanks

For example, how to define a monetary structure of a car industry in the USA or any other country, which may consist of production of rubber, steel, glass etc. In other words, I'm interested to know how the Leontiev input-output intersectional tables are formed. Do you know any websites where such statistics is available?

This is a very general question. I have seen a lot of repeated public goods game which have different round numbers. Some have more than 20 rounds, some are less 10 rounds. To test for public goods contribution in different mechanisms, do experimental economists have a rule of thumb to determine how many rounds is enough in one treatment? Thanks in advance.

I am new in the field of developing forecasting models. 

I want to develop a forecasting model to estimate the electricity prices of IEX, India. I read some research papers and found that work on MSARIMA-EGARCH has already been done.

I want to know about some suggestion about any other model from GARCH family which can be used. 

Please also point out the important points which should be kept in mind while developing a model as I had not developed any till now.

One has to compare the delicate economic reasoning which led Keynes to assume that interest did not affect MPC, or the complex considerations on which Hicks constructs Capital and Time, with the modern axiomatic method that reasons downwards. The former seems to retain a grasp of economic reality even after mathematical cloaking, while the latter seems arid.

Can anyone assist me to get research papers on financial planning and forecasting?

I want to examine the implication of ethanol in India and its impact

I am preparing an introductory guide and course on revenue forecasting for a developing country. While I have a depth of experience in this field in more developed countries, I want to make sure that i cover the basics very well.

I am looking for advice on texts and articles with broad coverage, rather than narrow coverage of specific technical topics and applications.

Thanks, Michael

1. i want to forecast health care expenditure(HCE) for 2020 and 2030 for Asian developing countries. I am using annual data ranging form 1995-2014 n the following variables per capita HCE, GDP per capita, education, life expectancy at birth, population under 15 and above 65 years and some other variables.

2. How can i get the future values for GDP and HCE like2015,2016, 2017.....?

Appreciated if some one tell me the codes in E-Views or Grtle or SPSS

3. How can i forecast HCE and GDP ?

In the Global Knowledge Economy, the role of human talent reigns supreme.  Future national performance in Human Development Indices and GDP growth is determined by the talent pool available to nation states. Economic Development policy-making and growth projections are best girded with relevant Labour Market Information and Intelligence.  Which states are the leading avatars of LMIS globally ?  What are the features and characteristics of their LMIS ?  How does one objectively measure, compare and contrast national LMIS's ?

Hello,

I want to do a forecast project on inflation for one of my classes, however I have some questions for it. Firstly, I have data which exhibits seasonal and trending pattern. 

I seasonally adjusted my data by using x-13 Census in Eviews, though just very minor changes occurred and it stills seem seasonal according to ACF and PACF. After this, I took differences of observations at lag 1 and now I am confused. My data points were walking around 6 and 10. Now it is between -1 and 1. When I apply Winter's Method to differenced data, I get results between -1 and 1. How can I report my forecasts in normal level, that is between 6 and 10. If I use ARIMA, it automatically take differences and it is okey. The only thing I have to specify is base and seasonality parameters. However, for Winter's Method, how should I proceed?

Hello,

lots of papers in this property cycle research use statistical forecasting methods to predict the property cycle.[1][2]

However, some researchers argue that the property cycle is deeply dependent on the fundamentals.

Still, I have not found any specific models, which use fundamentals as input(I could imagine these inputs would be GDP, interest-rate, price-to-earnings ratio etc.) to measure the property cycle.

Any papers or research that you could suggest that use fundamentals to forecast property cycles?

I appreciate your replies!

References:

[1] see Grover 2013 - http://www.emeraldinsight.com/doi/abs/10.1108/JPIF-05-2013-0030

[2] see Jadevicius 2014 - http://researchrepository.napier.ac.uk/7558/

I have collected last 10 year's monthly average prices of an item.I am working on a forecasting model.I used 2005 to 2014 data to forecast prices for 2014-15.Forecasts I got from Winter,Arima,TBATS and naive approach are not at all close to actual values.I have attached monthly price plot.

1) Which model should I use? 2) How can I improve my accuracy.

Output:

Actual Winter ARIMA TBATS

679 668 667 671

654 663 639 670

657 659 646 668

653 654 651 667

685 649 656 665

687 644 661 664

689 640 666 662

691 635 670 661

695 630 673 660

731 625 677 659

751 620 680 657

705 616 683 656

Note: I am using R.

I have supplied 2 stationary series (as per adf,test) to build a VAR model with appropriate lag order. I got the results. But when I tested for stationary of this VAR model using 'roots' command in R that tests for values of eigen values, it shows that VAR model is not stationary.

How can it happen and why?

How to take on this issue?

Hello,

I read several papers regarding forecasting methods [1]. One of the most recent works is "AN EVALUATION OF THE USE OF COMBINATION TECHNIQUES IN IMPROVING FORECASTING ACCURACY FOR COMMERCIAL PROPERTY CYCLES IN THE UK" from Jadevicius[2].

He argues that combination forecasting is currently the state of the art to forecast property cycles. However, his proposed model uses a property index as an independent variables and he tries to find a model to forecast this index. To make his forecast more stable, he uses two methods and "simply" said combines them for a more accurate forecast.

However, I was wondering if there are more sophisticated methods than the use of combination forecasting?

I would apprecaite your replies!

References:

[1] see Grover 2013 - http://www.emeraldinsight.com/doi/abs/10.1108/JPIF-05-2013-0030

[2] see Jadevicius 2014 - http://researchrepository.napier.ac.uk/7558/

Hi all,

I have monthly data for several years and for all regions of my country. The task is to make forecast basing on this data and compare the result with usual time series models. Does anybody has an idea about references? Are there any papers were such forecasts for fiscal parameters were made? Will be grateful for any help, links, thoughts.

Recent research suggests that Bayesian Model Averaging (BMA) is a useful method for combining forecasts. I am looking for prior evidence on the relative out-of-sample forecast accuracy of BMA compared to the simple average. Can you point me to relevant studies that provide empirical evidence? I am looking for evidence from all fields, although my particular interest is in social science problems.

Hey everyone,

I want to create an AR process to forecast electricity generation by photovoltaic plants. I have the data of three sample photovoltaic plants to develop identify the best number of AR-lags. Later I want to use that model to predict the electricity generation of over 300 photovoltaic plants.

I started with determining the order of my AR process by calculating AIC and BIC for each sample plant. Then I eliminated insignificant lags for each sample plant.

Now I am left with different significant lags for each of my three sample photovoltaic plants:

Plant 1: Significant Lags are 1, 2, 3, 4, 23

Plant 2: Significant Lags are 1, 2, 4, 23

Plant 3: Significant Lags are 1, 2, 3, 4, 20, 23

I've got two options now: Either I can include ALL of these lags in my final model, or I can only include lags that are significant for ALL of my three sample plants.

I would be very grateful to get your opinion on this matter.

Thanks!!

I want to fit a SARIMA model to some photovoltaic electricity generation time series to develop a forecasting model. Since the data are highly seasonal I differentiated two times to get stationary data. The kpss test now also tells me I have stationary data.

At the moment I am using 2 AR lags, 2 SAR lags, 1 MA lag and 1 SMA lag with a seasonality of 24. This configuration has so far created the best forecasts in comparison to many other configurations i have tried. When looking at the ACF and PACF of the residuals of the model there are no significant lags (except for lag 24, which I cannot seem to get rid of).

When I estimate my coefficients the value of the MA and SMA coefficients is exactly 1. This means I have a unit root in my moving average part of the process, right? And this is bad, right?

How can I get rid of this unit root while maintaining the accuracy of my forecasts? I have already tried differentiating my data only once and eliminating the MA and SMA lags so that I only have 2 AR and 2 SAR lags. The forecast accuracy is not as good as before but acceptable but the ACF and PACF both have many significant lags now...

Is it better to have a model without unit root and therefore a less accurate forecasat? Or is it better to have the best forecast quality I can get while maintaining the unit root?

Thanks a lot!!

diaspora direct investments (DDIs) are international flows similar to FDIs 

Hello,

I am currently working on my thesis - my model is as follows:

LGDP=LFDI +LGFC +LIKG + LIIG+ LICG +LLF +D1

The variables are integrated I(0) and I(1), so l must use ARDL. However, I have a big problem because my data are only available from 1990 to 2012 (23 years).

My questions are:

Is it possible to use the ARDL approach?

How can I get lag length of multiple variables by EViews 8?

Please help me to identify the current problem in Financial Market Forecasting and the Efficient Market Hypothesis of UK Market. This looks like a very interesting research topic for my PhD. However, I have to be more precise with my research topic. Please advise.

Most frequently, (economic) forecasters do not only provide the expectations of their forecasts but also some measures for uncertainty of the forecasts (like the 66% probability interval). However, the forecasters' customers (like politicians, managers, or journalists) frequently complain about the poor ex-post performance of the forecasts, about the gaps between real development and the mean of the forecast distribution – even if real development lies within the 66% confidence band. Likewise, they complain about large ex-ante forecast uncertainty (and uselessness of the forecast) when a 80% probability interval is reported, and are satisfied with accuracy of the narrower 66% probability interval even though both are calculated from the same distribution.

Do you have any suggestion on how to communicate forecast uncertainty in a way that is more easily understandable by persons without statistical training?

Should we make it a standard to report something like a `recession probability' in forecasts on economic growth or unemployment, of a `deflation probability', and so on? Similar to the precipitation probability frequently found in weather forecasts...

What is the outlook for nickel demand in non-stainless first uses?

What are the trends in the intensity of use of iron ore and steel?

I am doing my PhD research on stock market forecasting and I need to know which is the best computer program to apply the Hybrid models. I am using Concordance and genetic, GARCH-HMM and other machine algorithm models. I haven't used any advanced computer programs before and I am trying to attend more courses and to read more books in MATLAB, R, and JAVA.

In our current paper, the "Golden Rule of Forecasting" we are searching for evidence on whether, over the past half-century, the practice of forecasting in any area of the social or management sciences has been shown to produce more accurate forecasts or less accurate forecasts or if there has been no measurable change. Please provide citations to relevant sources.

I want to calculate long time series of forest product prices in current EURO value. Based on international trade prices in USD.

An answer on stack exchange :

suggests using "national currency weights to the ECU value" to obtain a time series back to 1979. It also suggest using a 1979-1999 time series called " Euro Community" from the St Louis FED. Are there other European sources of EUR/USD exchange rate before 1999?

Which one of them is the best to validate different stock market volatility models (i.e. GA- HMM, HMM-GARCH, EGARCH, APARCH, GARCH normal, EWMA model) where all will be validated under full sample period and sub periods, I believe that it depends on the estimation conditions and on the tradeoff between bias and variance. But one of them must fit those model more than others. Also I am using daily, weekly, monthly and annual data. So I must select the best method from the beginning to prepare the data with different time horizons.